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Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
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PhUSE Computational Science Development of Standard
Scripts for Analysis and Programming Working Group
Analysis and Display White Papers Project Team
Analyses and Displays Associated with Thorough QT/QTc
Studies
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Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
Working Group
Table of Contents
1.
DISCLAIMER ................................................................................................................................................... 4
2.
NOTICE OF CURRENT EDITION ........................................................................................................................ 4
3.
ADDITIONS AND/OR REVISIONS ..................................................................................................................... 4
4.
OVERVIEW: PURPOSE AND SCOPE ................................................................................................................. 4
5.
PROJECT BACKGROUND ................................................................................................................................. 6
6.
ECG BACKGROUND......................................................................................................................................... 7
7.
PRE-ANALYTICAL ISSUES ................................................................................................................................ 9
7.1
CORRECTION OF THE QT INTERVAL FOR HEART RATE .................................................................................................9
7.1.1
Historical Population-Based Formula from a Historical Population.......................................................9
7.1.2
Study Population-Based Formula from the Population under Study ...................................................11
7.1.3
Individual-Based Formula (QTcI) ..........................................................................................................11
7.1.4
Choosing the Right Correction Method ................................................................................................12
7.2
THOROUGH QT (TQT) STUDY DESIGN..................................................................................................................12
7.2.1
Brief Background .................................................................................................................................12
7.2.1.1
Historical Reason for the TQT Study ................................................................................................................ 12
7.2.1.2
Study Design Background Considerations ........................................................................................................ 13
7.2.1.3
Days of ECG Collection and Time Points of ECG Collection on the Days of Collection ..................................... 14
7.2.1.3.1 Collection of ECGs for Baseline................................................................................................................... 14
7.2.1.3.2 Collection of On-Treatment ECGs ............................................................................................................... 15
7.2.1.3.3 ECGs and Their Data on Days of Collection (Baseline and On-Treatment) ................................................. 16
7.2.2
7.2.2.1
7.2.2.2
7.2.2.3
Specific Designs ....................................................................................................................................17
Parallel Studies ................................................................................................................................................. 17
Crossover Studies ............................................................................................................................................. 18
Non-standard Designs ...................................................................................................................................... 19
7.3
BASELINE AND TREATMENT DIFFERENCE (DRUG EFFECT) ..........................................................................................19
7.3.1
Time-Matched Lead-in Day Baseline; Double-Delta Treatment Difference .........................................20
7.3.2
Time-Averaged Lead-in Day Baseline; Double-Delta Treatment Difference ........................................20
7.3.3
Predose Averaged Baseline; Double-Delta Treatment Difference .......................................................21
8.
ANALYSIS ..................................................................................................................................................... 22
8.1
PRIMARY ANALYSIS ...........................................................................................................................................22
8.1.1
Testing of QT Prolongation ..................................................................................................................22
8.1.1.1
8.1.2
8.1.2.1
Multiplicity Issues............................................................................................................................................. 22
Assay Sensitivity ...................................................................................................................................23
Multiplicity Issues............................................................................................................................................. 24
8.1.3
Categorical Analyses ............................................................................................................................25
8.1.4
Morphological (Qualitative) Analyses ..................................................................................................25
8.1.5
Exploratory Analysis of Other Continuous ECG Parameters ................................................................26
8.2
CONCENTRATION-RESPONSE RELATIONSHIP (CRR) .................................................................................................26
8.2.1
Rationale for Performing a Concentration-Response Analysis within a TQT Study .............................26
8.2.1.1
8.2.1.2
8.2.1.3
When Results of the TQT Study Are Negative (Non-inferiority Is Supported by the Results) .......................... 26
When Results of the TQT Study Are Positive (Cannot Reject Inferiority based on Study Results) ................... 27
When Assay Sensitivity Is Not Demonstrated .................................................................................................. 27
8.2.2
Methodology........................................................................................................................................27
8.3
P-VALUES AND CONFIDENCE INTERVALS ................................................................................................................28
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Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
9.
Working Group: Standard Analyses and Scripts
Working Group
LIST OF OUTPUTS ......................................................................................................................................... 30
10.
OUTPUTS SHELLS ...................................................................................................................................... 31
11.
ACKNOWLEDGEMENTS ............................................................................................................................. 50
12.
PROJECT LEADER CONTACT INFORMATION .............................................................................................. 50
13.
REFERENCES ............................................................................................................................................. 51
[Version 1.0] – [2016-March-11]
Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
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1. Disclaimer
The opinions expressed in this document are those of the authors and do not necessarily represent the opinions of
Pharmaceutical Users Software Exchange (PhUSE), the members’ respective companies or organizations, or
regulatory authorities. The content in this document should not be interpreted as a data standard and/or information
required by regulatory authorities.
2. Notice of Current Edition
This edition of the “Analyses and Displays Associated with Thorough QT/QTc Studies” is the first edition.
3. Additions and/or Revisions
Date
2016-March-11
Author
See Section 12
Version
v1.0
Changes
First Edition
4. Overview: Purpose and Scope
Under the Clinical Data Interchange Standards Consortium (CDISC), standards have been defined for data
collection (Clinical Data Acquisition Standards Harmonization - CDASH), tabulation (Study Data Tabulation Model
- SDTM), and analysis (Analysis Data Model - ADaM) datasets. The next step is to develop standard tables, figures,
and listings (TFLs). The Development of Standard Scripts for Analysis and Programming Working Group is
leading an effort to create several white papers providing recommended analyses and displays for common
measurements and has developed a script repository as a place to store shared code.
The purpose of this white paper is to provide advice on displaying, summarizing, and analyzing data from a
Thorough QT/corrected QT (QTc) Study (also referred to as a TQT study). The intent is to begin the process of
developing industry standards with respect to analyses and reporting for these trials. In particular, this white paper
provides recommended processes for:


Pre-analytical issues: Study design, QT interval corrections, and Baseline adjustments
Analytical issues: Testing for QT prolongation, Assay sensitivity, Outlier analysis / Categorical analysis,
Morphological (Qualitative) abnormalities, and pharmacokinetic (PK)/pharmacodynamics (PD) analysis
This paper attempts to give recommendations for difficult decisions related to the analysis of difficult topics such as
QT interval correction, baseline, and PK/PD analysis. Because there are on-going discussions regarding these
topics, the recommendations made here are mainly based on the authors experience with these trials and submissions
to regulatory bodies (and International Conference on Harmonisation [ICH]-E14 guidelines and Q&A at the time
this white paper was written).
The content of this document can be used when developing the analysis plan for TQT studies.
Development of standard TFLs and associated analyses will lead to improved standardization from collection
through data storage, as it is necessary to determine how the results should be reported and analyzed before
finalizing how to collect and store the data. The development of standard TFLs will also lead to improved product
lifecycle management, by ensuring reviewers receive the desired analyses for consistent and efficient evaluation of
patient safety. Although having standard TFLs is an ultimate goal, this white paper reflects recommendations only,
and should not be interpreted as “required” by any regulatory agency.
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Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
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Detailed specifications for TFL or dataset development are considered out of scope for this white paper. However,
the hope is that specifications and code (utilizing SDTM and ADaM structures) will be developed consistent with
the concepts outlined here, and placed in the publicly available scripts repository.
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Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
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5. Project Background
The beginning of the effort leading to this white paper came from the initiation of the FDA/PhUSE Computational
Science Collaboration, a yearly conference and ongoing working groups to support addressing computational needs
of the industry. The FDA identified key priorities and teamed up with PhUSE to tackle various challenges using
collaboration, crowd sourcing, and innovation (Rosario LA 2012). The FDA and PhUSE created several
computational science (CS) working groups to address several of these challenges. The working group, titled
“Development of Standard Scripts for Analysis and Programming,” has led the development of this white paper,
along with the development of a platform for storing shared code. As noted in Section 1, the content in this
document should not be interpreted as a data standard and/or information required by regulatory authorities.
Members of the Analysis and Display White Papers Project Team reviewed regulatory guidance and shared ideas
and lessons learned from their experience. Draft white papers were developed and posted in the PhUSE wiki
environment for public comment.
Most contributors and reviewers of this white paper are industry statisticians, with input from non-industry
statisticians (e.g., FDA and academia) and industry and non-industry clinicians. Additional input (e.g., from other
regulatory agencies) for future versions of this white paper would be beneficial.
Several existing documents contain suggested TFLs for common measurements. Some of the documents are now
relatively outdated, and generally lack sufficient detail to be used as support for the entire standardization effort.
Nevertheless, these documents were used as a starting point in the development of this white paper. The documents
include:
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





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ICH E3: Structure and Content of Clinical Study Reports
Guideline for Industry: Structure and Content of Clinical Study Reports
Guidance for Industry: Premarketing Risk Assessment
Reviewer Guidance. Conducting a Clinical Safety Review of a New Product Application and Preparing a
report on the Review.
ICH M4E: Common Technical Document for the Registration of Pharmaceuticals for Human Use –
Efficacy
ICH E14: The Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential For NonAntiarrhythmic Drugs
ICH E14: The Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Nonantiarrhythmic drugs Questions and Answers R1.
ICH E14: The Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Nonantiarrhythmic drugs Questions and Answers R3.
FDA Guidance for Industry: ICH E14 Clinical Evaluation of QT/QTc. Interval Prolongation and
Proarrhythmic Potential for Non-Antiarrhythmic Drugs.
QT Studies Therapeutic Area Data Standards User Guide (TAUG) V1. CDISC.
The ICH E14 guidelines, FDA Guidance for Industry and TAUG are considered key documents. However, they do
not provide detailed information that would enable standardization of all analysis and presentation of TQT studies.
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Title: Analyses and Displays Associated with TQT Studies
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6. ECG Background
Some basic understanding of electrocardiograms (ECGs) can be helpful in planning and completing analyses for
TQT studies. The ECG is a graphical representation of the electrical depolarization and repolarization of the heart’s
cells that initiates and spreads through the heart in an organized manner and causes contraction of the heart muscle
that results in the pumping of blood. In 1895, Einthoven established the five primary topographic features of the
ECG tracing (P, Q, R, S, and T waves; discussed in more detail below), and in 1912 he defined the now standard
ECG leads (the waveform of potential difference over time between two sets of one or more electrodes attached to
the body) I, II, and III. Additional standard leads were established in 1938 (V1 – V6) and in 1942 (aVR, aVL, and
aVF). Therefore, the standard ECG records this activity at the body surface for 12 leads (I, II, III, aVR, aVL, aVF,
V1, V2, V3, V4, V5, V6). A continuous waveform (positive and negative changes over time) of electrical activity is
recorded for each lead. A standard ECG is a 10-second recording, but ECG data can be recorded and stored
digitally for any amount of time (limited only by storage media capacity). A standard paper ECG displays 2.5
seconds of each lead (4 sets of 3 leads) and all 10 seconds of one lead as illustrated in Figure 6-1. In Figure 6-1, an
ECG recoding of 10 seconds is displayed. The fourth (bottom) line tracing is the entire 10 seconds of the ECG data
recorded from Lead II (referred to as the “Rhythm Strip”; Lead II is the customary “Rhythm Strip” Lead, but other
leads might be selected as the “Rhythm Strip” Lead). The 3 line tracings above the “Rhythm Strip” display shorter
time segments of all 12 leads – Leads I-III simultaneously moving down from the first to third line tracing; then
leads aVR and aVF; then Leads V1-V3; and finally V4-V6.
Figure 6-1: Standard 10-sec ECG
The waveforms are a series of complexes (a complex and its component parts are shown in Figure 6-2) that
represent the sequential depolarization and repolarization electrical activity that spreads through the heart. These
complexes have parts, briefly noted above, that are named as shown in Figure 6-2. Note that a single complex
contains a P wave, a QRS complex (that consists of a Q wave [sometimes absent], an R wave, and an S wave; the R
and S waves can have opposite polarities across leads), a T wave, and sometimes a U wave. Each of these
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Title: Analyses and Displays Associated with TQT Studies
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complexes represents a complete depolarization and repolarization of the heart. There is an isoelectric gap (no
electrical activity) between complexes. The RR interval, not shown in Figure 6-2, is the time between successive R
waves (and, therefore, the time between complexes). Analyses in TQT studies will focus on the QT interval and the
RR interval or heart rate (HR), respectively (see below), but secondary analyses will also be conducted on the PR
interval and the QRS complex. The width of the waves, and intervals, including the RR interval, represent time and
are most commonly expressed in millisecond (msec) units. Heart rate, which is the number of complexes per
minute, is usually expressed as beats per minute (bpm). Therefore, the RR interval measurement, in msec, and HR,
in beats/min, have the following relationship:
 RR = (1/(HR/60))*1000
 HR = 60,000/RR
Figure 6-2: A single ECG waveform complex and its parts
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7. Pre-analytical Issues
7.1 Correction of the QT Interval for Heart Rate
The QT interval is a measure or biomarker for the time of ventricular depolarization and repolarization to occur, but
in practice, it is used as a biomarker for the time of ventricular repolarization. The QT interval changes in inverse
relationship to HR for appropriate physiological coordination of the pumping of blood by the heart. Therefore,
because subjects’ heart rates are not constant throughout participation in a TQT study (or when evaluated clinically),
it is necessary to correct the QT interval for HR in order to make comparisons of the QT interval recorded at
different HRs at different times. Complicating the situation a bit more is the fact that the QT interval does not
change instantaneously with a change in HR. The change in QT interval is delayed; its change is subject to
hysteresis.
Hysteresis is generally ignored in the analysis of TQT studies, but researchers like Malik et al (2008) have
developed methods for evaluating hysteresis patterns of the QT interval in response to HR changes (in contrast to
the hysteresis of QT change with a drug that does change QT) on an individual basis and incorporating them into QT
correction. Discussion of this topic is beyond the scope of this white paper.
The ideal QTc interval would be uncorrelated with HR or the RR interval. In other words if QTc were plotted
against either RR interval or HR, and the data were fit to a linear model, the slope of the regression line would be
“0”. Essentially, QT correction for HR attempts to adjust the individual subject’s QT interval, at any HR, to a value
that would be expected if the subject’s HR were constant. In the majority of QT correction formulas, RR interval is
used rather than HR because RR interval is measured and expressed in the same units as the QT interval, msec,
whereas HR is measured and expressed in beats/min as illustrated above.
In general, there are three basic methods to adjust or correct the QT interval for HR (RR-interval). The methods are:
1. Historical population-based formulas derived from historical populations
2. Study population-based formulas derived from the population under study
3. Individual-based formulas derived for each individual in the population under study
All three methods are based on exploring the mathematical relationship between the QT interval and the RRinterval, but they use different populations for finding this relationship. The exploration of this mathematical
relationship amounts to finding a function and its numerical coefficients, or finding the specific numerical
coefficient(s) for either a prespecified function or best fitting mathematical function (linear or nonlinear) from
among a number of functions that model the relationship between the QT interval and the RR-interval for a set of
ECGs from a population of multiple individuals (or from one individual in the case of individual-based formulas).
The mathematical function is then translated into a correction formula, using the numerical coefficients that were
found in the data fitting process. The same formula is subsequently applied to all ECGs for which a QTc is being
computed. Therefore, for example, a set of QT interval measurements and associated RR-interval measurements
could be fitted to the data using the mathematical function:
QT = β * RRα
The value of the coefficient α that is found to give the best fit for the data might be 0.25. Then the correction
formula for QTc (in seconds) would be:
QTc = QT / RR0.25,
where QT and RR are measured in seconds.
7.1.1 Historical Population-Based Formula from a Historical Population
In a historical population, this would be a group of normal, healthy persons, generally with one ECG from each
person. Due to the normal variance between different populations, multiple researchers using different groups of
subjects have derived different formulas, even when fitting their respective data to the same mathematical function.
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The most commonly used historical population-based correction formulae were proposed in 1920 by Bazett (QTcB)
and separately by Fridericia (QTcF). Unfortunately, each formula can lead to bias for some clinically relevant
values of HR, as illustrated below. For an extensive list of 31 such historical correction formulas, including those
listed below, based on multiple mathematical functions, see a manuscript by Malik (2002a). As indicated above,
each of these formulas could be expressed using HR, where RRmiliseconds = ((1/(HRbeats-per-minute/60))*1000)
QTc = QT/ RR1/2
(i)
Bazett (obtained in seconds, where QT and RR are measured in seconds):
(ii)
Fridericia (obtained in seconds, where QT and RR are measured in seconds): QTc=QT/RR1/3
(iii)
Framingham: QTc=QT+(0.154*(1-RR))
(iv)
Van de Water: QTc=QT–((0.087*(1-RR))
It is reasonably well known that the Bazett formula over-corrects at faster HRs (over 60 beats/min) and conversely
under-corrects at slower HRs. That is, at faster HRs (smaller RR- intervals), the computed QTc is ”larger than it
should be”, and at slower HRs (larger RR-intervals), the computed QTc is ”smaller than it should be”. When
Bazett’s QTc is plotted against RR interval and a regression line is plotted, the slope is negative (Figure 7-1; with a
perfect correction, the slope of the regression line would be “0” as described above). In spite of this, Bazett’s
formula is still the most widely used for clinical correction of QT intervals. However, it is becoming more
acceptable in regulatory documents to use the Fridericia formula correction, without use of the Bazett formula (ICH
E14, 2012; Question 11), along with additional correction results as described below.
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Fridericia correction
Corrected QT interval (msec)
Corrected QT interval (msec)
Bazett correction
500
450
400
350
500
450
400
350
0.6
0.8
1.0
1.2
0.6
0.8
1.0
1.2
RR interval (sec)
RR interval (sec)
Figure 7-1: Relationship between the Bazett- and Fridericia-Corrected QT Interval and RR Interval
(Note that the solid line is not the linear regression line but the mean of QTc values at each RR value)
7.1.2 Study Population-Based Formula from the Population under Study
A study population formula derived from the population under study uses off-treatment, baseline ECGs, and
sometimes ECGs collected during placebo treatment to construct a population correction formula as described
above.
The method is based on finding the specific numerical coefficient(s) for either a prespecified or best fitting
mathematical function (linear or nonlinear) that models the relationship between the QT interval and the RR interval
for a set of ECGs from a population of multiple individuals. The mathematical function is then translated into a
correction formula using the numerical coefficients that were found in the data fitting process. The same formula is
then applied to all ECGs for which a QTc is being computed.
Because the formula is based on the behavior of the individuals actually under study, such a study populationderived formula presumably accounts for variables (e.g., disease factors, age, and gender distribution) which might
influence the QT-RR relationship. Therefore, such a formula should be more accurate for the individuals under
study than one based on a historical population.
7.1.3 Individual-Based Formula (QTcI)
It has been well established that the mathematical function that best describes the QT-RR relationship may differ
from individual to individual (Malik et al., 2002b) but is stable within individuals, and, therefore, any group-based
(study-wide) correction will be somewhat imprecise when applied to individuals. Though the magnitude of
imprecision is generally not of sufficient magnitude to affect negatively the TQT study substantially, it is possible to
derive and use individual-based correction formulas. An individual-based QTc (QTcI) requires that a number of
ECGs be obtained across a sufficient range of HRs. The number of ECGs required for individual correction is an
important matter. Morganroth (2005) has suggested that 35 to 50 ECGs covering a range of HRs of 50 to 80 beats
per minute for each individual under baseline (non-treatment) conditions are sufficient. The HR range for ECGs
collected for use in computing individual corrections should more appropriately include rates that will be observed
during treatment with the experimental drug to be tested in the TQT study. For such drugs that substantially alter
heart rate, that might be impossible under baseline conditions (see last paragraph of this section below and Garnett et
al. [2012]). Couderc (2005) has published data to support the position that at least 400 ECGs (QT-RR pairs for each
individual subject) are needed to compute an adequate individual correction, and that there must definitely be a
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Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
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range of heart rates corresponding to the heart rates that will be observed with the experimental drug. Using
continuous ECG recording, obtaining these large numbers of baseline ECGs can be easily achieved. The ECGs
collected during placebo-treatment can be considered for use in computing individual corrections but this might be
considered controversial by some regulatory agencies. Though these ECGs are not influenced by drug, they are
potentially influenced by a distinct set of circumstances relative to those collected at baseline. They are collected
with the subject knowing that they are under some treatment, rather than under no treatment and these psychological
differences might, hypothetically, result in subtle difference in autonomic tone that would influence the QT-RR
relationship.
These QT-RR data are then used to compute a specific correction formula for each individual subject, in a manner
similar to that used to compute a population correction. In computing QTcI, one sub-approach is to use a single,
predetermined mathematical model for all subjects and we can refer to this approach as individualized correction
(optimizing the coefficient[s] on an individual basis for a single correction formula). An alternative sub-approach is
to fit the individual subject’s data to several preselected mathematical models, and use the best mathematical model
for each individual subject (model with the best fit to the data and that results in flattest regression line after
correction (QTcI verus RR)) and we can refer to this approach as individualized individual correction (optimizing
the actual correction formula and its coefficient[s] on an individual basis). Malik et al. (2004) have described 12
mathematical models that could be considered when finding an individual best-fit model for a given subject. As
such, this latter method for computing QTcI, individualized individual correction, is probably the best using
sufficient baseline data (400 ECGs), with an appropriate range of heart rates as will be observed with test drug.
However, either type of individual correction formula computation is also very labor intensive and costly (ECG
collection and computation) to use.
Some researchers have developed methods of assessing changes in ventricular repolarization based on the QT
interval, which do not rely on an explicit correction of the QT- interval for HR (the RR-interval). These methods are
particularly important when the experimental drug results in marked changes in autonomic nervous system tone and
HR. These changes can be so large, that it will be difficult to obtain off-treatment ECG data at heart rates that will
be observed during treatment with the experimental drug, which would raise concerns about the validity of any
correction factor. Discussion of these alternatives beyond the introduction of the concept is outside the scope of this
document, but can be reviewed in the manuscript by Garnett et al. (2012). These methods would generally rely on
continuous recording data.
7.1.4 Choosing the Right Correction Method
As discussed above a number of correction methods can be used for the QT interval. In case of multiple correction
methods being available for a study, usually one is pre-selected and considered as primary in the statistical analysis
plan; in which case the benefits/problems of the methods as outlined above can be considered to make a choice.
Regardless of the pre-selection, it is not unreasonable to investigate after the data are received which correction
method is the best fit to the data (especially in cases where different correction methods provide different results).
This discussion is considered out of scope for this white paper but the authors consider the following paper, Darpö et
al. (2012), as a good place to start.
7.2 Thorough QT (TQT) Study Design
7.2.1 Brief Background
7.2.1.1 Historical Reason for the TQT Study
Jervell and Lange-Nielsen (1957) described correlations between hereditary long QT intervals and sudden death.
Smirk and Palmer (1960) noted that initiation of ventricular depolarization (R waves) prematurely occurring before
the complete repolarization of the ventricle following the preceding depolarization (during the T waves – referred to
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as “R-on-T Pattern”) increases the risk of ventricular arrhythmia. Torsade de pointes (TdP), a specific type of
ventricular tachyarrhythmia (fast arrhythmia), was first described in a publication by Dessertenne (1966).
Although some drugs that had been developed as anti-arrhythmic agents also altered ventricular repolarization as
evidenced by prolonged QTc, it was not widely appreciated that non-cardiac drugs could also have this property.
The use of non-sedating antihistamines, e.g., terfenadine and astemizole, from 1985 to 1999 provided an important
case study of the public health issues with the widespread use of non-cardiac drugs with such cardiac effects. Initial
reports of cardiac arrhythmias, including TdP, were predominately associated with high blood concentrations of
these antihistamines subsequent to overdose. Given the metabolic pathway of these drugs, arrhythmias were
eventually reported subsequent to coadministration with drugs and substances that slowed the metabolism of
terfenadine and astemizole, including grapefruit juice (also resulting in high blood concentrations). Despite warning
letters to physicians and restricted product labeling in 1992, inappropriate medications continued to be
coadministered with these drugs. Both drugs were withdrawn in 1999 from use in the United States (US) after safer
alternatives were developed.
The high visibility of the association between non-sedating antihistamines and fatal ventricular arrhythmias
prompted extensive research into the mechanisms by which drugs cause these cardiac arrhythmias. Although many
details remain unknown, current research suggests that most drugs with strong arrhythmic potential interfere with a
specific potassium channel in cardiac muscle fiber that functions to repolarize the muscle fiber cells. Partially or
completely blocking the potassium channel results in delayed repolarization of the muscle fiber cells. Delayed
repolarization increases the time required to restore the normal resting potential prior to the next depolarization for
the next muscle contraction. Arrhythmias such as TdP are possibly triggered by the initiation of the R waves
(beginning the depolarization of the ventricles) during the period of delayed repolarization, while the ventricles are
still partially depolarized. In summary, drugs that delay ventricular repolarization might place a person at increased
risk of a fatal ventricular arrhythmia.
Again, delayed ventricular repolarization is manifested on the ECG tracing as a prolonged QTc. The QTc is clearly
recognized as an imperfect biomarker for increased risk of fatal arrhythmia because it can be increased by a number
of drugs that are not associated with a significant incidence of such arrhythmias. None the less, an increase in QTc
is considered an important risk factor and any drug-induced increase is considered important to assess and quantify.
On an individual basis, the increase in QTc generally needs to be substantial to place the patient at risk, but for a
potential new drug, even a slight mean increase 1 in QTc can be clinically meaningful, in that some degree of risk
cannot be excluded in a small number of individuals in a large population that will receive the drug during its use in
clinical medicine. The TQT study is considered the most precise way of studying the potential drug effect on QTc
in human subjects.
7.2.1.2 Study Design Background Considerations
Clinical studies to detect QTc mean increases as small as 5 msec face significant challenges because of the
substantial variability in QT intervals. The first source of variability is the process of acquiring and measuring the
QT interval. Placement of ECG electrodes, choice of lead(s) to be measured, standardization of ECG machines,
choice of media (paper versus digital), and variability in expert measurement of the QT interval comprise critical
components of the process.
QT intervals are characterized by substantial inter- and intra-subject variability apart from that engendered by
acquisition and measurement. Sources of inter-subject variability can include a genetic predisposition to long QT
intervals, electrolyte concentrations, autonomic activity, age, and sex. Intra-subject variability is strongly influenced
by diurnal rhythms (transitioning to sleep from wakefulness and vice versa) that influence autonomic tone and heart
rate.
1
>5 milliseconds (msec) would be considered to exceed random variability (Malik, 2001) and a mean increase of ≥10 msec could be of
regulatory interest (ICH, 2005).
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Dose selection, duration of dosing, timing of ECG measurements, patient population, and control of factors
influencing variability will need to be addressed in any study designed to evaluate QT interval. Though the TQT
study is considered the most definitive study of the potential influence of a drug on QT interval, it might suffer from
limitations due to sample size, the health of the subject population, and many other factors that cause the drug
administration in the study to be different from how the drug will be used in broad clinical practice.
This brief background provided below is informed primarily by the May 2005 ICH-E14 document [ICH, 2005], that
describes the basic conduct, purpose, and expected analyses of the TQT study, as well as by its update in a
subsequent Q&A document (ICH, 2012)
The purpose of a TQT study is to evaluate the potential for an experimental drug to delay cardiac ventricular
repolarization, which the study does through evaluation of changes in QTc during drug treatment; and also to
demonstrate that the study is capable of detecting differences in the variability that can be observed during placebo
treatment (random variability; approximately 5 msec), so as to confirm that any lack of detected change is due to
actual lack of change rather than lack of assay sensitivity as assessed by a positive control that causes a slight
increase in QTc (ideally in the 5- to 10-msec range). These TQT studies are generally conducted in healthy
volunteers, which are highly screened for normal cardiac electrical activity for ease of precise measurement of the
QT- interval and to avoid additional confounding factors.
The TQT study designs can be a crossover or a parallel design discussed in more detail below. In general, the
treatments are:
1. A dose of the experimental drug that is several times higher, if possible, than the intended maximum
therapeutic dose, in order to account for drug-drug interactions and/or genetic metabolic enzyme
deficiencies that might lead to greater exposure to the experimental drug than otherwise intended with a
given dose during routine clinical use
2. Placebo
3. A positive control for purpose of demonstration of assay sensitivity (most often moxifloxacin, usually oral
but sometimes intravenous)
4. Optionally, a dose of the experimental drug that is within the intended therapeutic range (generally the
maximum intended therapeutic dose)
The administration of the active control has been allowed by regulators to be open-label, but the administrations of
the experimental drug dose(s) and placebo are double-blind, and ECG measurements and readings are performed by
persons completely blinded to associated treatments, subject details, and date/time of the ECG.
7.2.1.3 Days of ECG Collection and Time Points of ECG Collection on the Days of
Collection
The ECGs are collected as a set of replicates (in close temporal proximity, e.g., 3 ECGs collected at 1-minute
intervals) of 10 seconds in duration and utilizing all 12 leads. In analyses, the QTc values of the replicates will be
averaged before analysis of differences in changes in QTc to reduce the signal-to-noise ratio and improve the
accuracy of the measurement. When discussing the collection of ECGs below, “ECG” will refer to the set of
replicate ECGs. The ECGs can be collected as conventional ECGs, or they can be extracted from a continuous high
fidelity ECG recording. Experimental drug and metabolite concentrations are often collected for assay immediately
after the time of ECG collection for PK/PD analysis, which can be a useful secondary analysis pertinent to the
potential influence of the experimental drug on ventricular repolarization.
The timing and collection of replicate ECGs are guided by the known properties (e.g., PK) of the drug and its
metabolites.
7.2.1.3.1 Collection of ECGs for Baseline
In general, in the analyses of QTc, baseline QTc values are subtracted from on-treatment QTc values to create a
“single ∆” value that is “change in QTc” and this “change in QTc” is compared between active treatments and
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placebo. Because several alternative mathematical/statistical definitions of baseline exist, Section 7.3 describes
alternative analyses that are in large part influenced by the definition of baseline. Baseline ECGs may be collected:

On the day (or for multiple days) preceding the day of first dose administration of each treatment;
 If this type of baseline is used, ECGs are collected at multiple time points that match the
time points at which ECGs are collected on-treatment.
 If ECGs are collected on multiple days, then QTc values from those days can be averaged
for the baseline value used in analyses; this multiple baseline day collection is rarely
done.
 The averaging can be for each time point when a time-matched baseline is being
used (time-matched) or across all time points (time averaged), if a time averaged
baseline is being used (see Section 7.3 for a more detailed description of
baseline alternatives).
 Baseline day(s) and time points are the same for each treatment to maintain the blind.
 Although consideration might be given to using a single, common baseline for each
treatment in a crossover study, either before the first treatment period for all subjects or
with a subset of subjects assigned by random allocation before each of the treatment
periods (Section 7.2.2 discusses study design in more detail), such a baseline is never
used.
 This baseline that collects multiple ECGs at the same time points as the ECGs will be
collected while on treatment on at least one day that precedes the first administration of
test is necessary for parallel studies (allows time-matched baseline).
and/or

Immediately preceding the first dose administration of each treatment;
 ECGs would be collected at several time points shortly before first dose administration
such as 60 minutes, 45 minutes, 30 minutes, 15 minutes, and immediately before
treatment administration.
 This baseline is generally used for crossover studies and not allowed by regulators for
parallel design studies.
 This baseline ECG collection can be combined with the ECG collection on the day or
days preceding treatment administration, resulting in complex baseline definitions and
treatment difference definitions.
7.2.1.3.2 Collection of On-Treatment ECGs
The days on which ECGs are collected and the time points of collection are determined by the PK characteristics of
the test drug. The intent of the study is to measure QTc at the time at which a maximum increase in QTc would
occur if the drug, or relevant metabolites, does increase QTc. In crossover studies, it is often the case that the drug
is sufficiently well tolerated that desired supra-therapeutic exposure could be achieved with a single dose, so only a
single dose of treatments is given. Sometimes in crossover studies, it is necessary to titrate the drug up to intended
exposure with multiple doses over multiple days. In parallel studies, dosing is often extended over multiple days
before intended exposure is reached.
For single-dose studies, ECGs are collected on the day of treatment administration at a time point shortly before the
time of the maximum drug concentration (T max), around Tmax, and should continue even after T max to evaluate any
delayed effects of the drug or its metabolites on cardiac repolarization. Depending on the PK of drug and
metabolites, the ECG collection might continue for one or more days following the day of drug administration.
For multiday dose studies, ECGs are collected according to the schedule described in the paragraph above but
beginning on the day that the drug reaches steady state or intended exposure has been achieved. In some multiday
dose studies, ECGs will also be collected following the first dose at identical times.
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To demonstrate assay sensitivity, ECGs should also be collected close to the Tmax of the positive control.
Replicate ECGs should be collected on the same days and at the same time points in all treatment groups to ensure
that blinding is maintained.
7.2.1.3.3 ECGs and Their Data on Days of Collection (Baseline and On-Treatment)
The diagrams (Figure 7-2 and Figure 7-3) below show how ECG data are organized within 10-second ECGs, and
how those 10-second ECGs are organized within and across time points. Although analysis methods that use all the
data from continuous monitoring over a long period (e.g., 24 hours) have been developed, the analysis usually
assumes that data is organized by time points. ECGs should be recorded (or extracted from continuous recordings)
in triplicate as noted above (replicates, number can vary but will generally be 3 and can be more), 30 to 120 seconds
apart, to account for inherent variability; each recording lasting 10 seconds (these 10-second ECGs are either
recorded as 10-second ECGs or extracted from continuous recording of the ECG record that is digitally stored for
later processing, typically in 24-hour increments). Figure 7-3 illustrates the on-treatment collection of triplicate
ECGs, as an example of the replicate collection, on a single day of ECG collection following treatment
administration.
24
hours
Beat 1
(7:59:00.00)
Beat 2
(7:59:01.30)
Beat 3
(7:59:02.15)
… Beat 12
(7:59:10.05)
…
Beat
100,800
Extracted 10second ECG
 12 P-QRS-T
Figure 7-2: Illustration of 1 of 12 Leads of Continuous ECG recording from which a 10-sec ECG can be
extracted
Each cycle, in a normal ECG obtained from a healthy person, consists of a P-QRS-T complex and the subsequent
isoelectric activity before the next P-QRS-T complex as described in Section 6.
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Figure 7-3: Illustration of the concepts of recording multiple replicate ECGs at multiple time points
subsequent to treatment administration (recording would also occur at baseline)
ECGs are taken after subjects have rested, but not sleeping, for at least 5 to 10 minutes in the supine position (in an
attempt to obtain a stable heart rate under similar physiological conditions at each time of collection). If the ECGs
are to be extracted from a continuous recording, then the subjects rest as they would for actual 10-second ECG
recordings.
7.2.2 Specific Designs
The examples of study designs presented below illustrate specific TQT study designs. A typical TQT study is
designed as double-blind (partial double-blind as in some cases the investigator might not be blind to administration
of the active control), placebo- and positive-controlled to determine whether the test treatment fails to prolong the
QTc (primary statistical test is for noninferiority), and to demonstrate the assay sensitivity using the positive control
treatment in the study population. Traditional TQT studies employ parallel or crossover designs, generally are
designed with equal study duration, and sample size for the different treatment arms or periods.
7.2.2.1 Parallel Studies
Under certain circumstances (related to the PK characteristics of the test drug), a parallel design may be preferred
for a TQT study. Such circumstances include (ICH E14, 2005):
• Drugs with long elimination half-lives for which lengthy time intervals would be required to achieve
steady-state and complete washout
• If carryover effects are prominent for other reasons, such as irreversible receptor binding or long-lived
active metabolites
• If multiple doses of the investigational drug are required to evaluate the effect on QT/QTc intervals
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Example of TQT - Parallel Study
Below is the study schema diagram for a parallel study. This study has 4 treatment arms (placebo, positive control,
therapeutic study drug dose, and supratherapeutic study drug dose), which correspond to the four possible left-toright "paths" through the study. Moxifloxacin has become the standard positive control with a well characterized
(peak effect and time course) expected influence on QTc in healthy subjects with a mean increase in QTc in the
range of 10 to 15 msec. Other positive control compounds are possible (e.g., low-dose ibutilide).
Note: Moxifloxacin is one
example of a positive control.
Note: This is an optional arm.
Figure 7-4: Parallel Study Design Schema for Example TQT Study 1
T = Therapeutic Dose (DRUG A 1 MG), ST = Supratherapeutic Dose (DRUG A 100 MG)
For parallel studies, an alternative to using a separate treatment arm for active-control is to embed the active-control
treatment within the placebo treatment in a blinded manner. See Section 7.2.2.3, paragraph 1 below for a reference
to a published study of such design.
7.2.2.2 Crossover Studies
In comparison to parallel studies, crossover studies have at least two potential advantages:
• A smaller number of subjects are typically required. Subjects serve as their own controls, resulting in
reduced variability of differences related to inter-subject variability.
• Heart rate correction approaches based on individual subject data may be more feasible (as baseline ECGs
are collected before each treatment period; therefore, more ECGs are available for each subject for
computation).
Example of TQT – Crossover Study
Below is the study schema diagram for a crossover study. In this example, subjects were screened for eligibility and
then randomized in a 1:1:1:1 ratio to receive one of four treatment sequences (Williams design). As with the
parallel design, the therapeutic dose is optional.
If the test drug is sufficiently well tolerated such that the necessary supratherapeutic exposure can be achieved with
a single dose and washout is not lengthy, then these crossover studies often involve administration of a single dose
of drug. If the drug must be titrated to reach required exposure but the titration period is not too lengthy, and
washout is not lengthy, then the crossover design can be used. When the titration or washout is lengthy, the parallel
design is used. Sponsors make the decision regarding whether a study should be crossover or parallel based on the
required titration and or washout time.
As in most crossover studies, the treatment arms are distinguished by the order of treatments, with all treatments
present in each arm.
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Figure 7-5: Crossover Study Design Schema for Example TQT Study 2
T = Therapeutic Dose (DRUG A 1 mg), ST = Supratherapeutic Dose (DRUG A 100 mg)
A washout period sufficient to clear all drug exposure has to be present between treatment periods.
7.2.2.3 Non-standard Designs
A design has been used for a parallel TQT study that required lengthy treatment periods, in which the positive
control treatment was embedded in the placebo treatment arm (Malik et al., 2008b). Discussion of this design
alternative is beyond the scope of this white paper, but the reader can review the cited manuscript.
When both a therapeutic dose and a supratherapeutic dose are studied, they might be contained in a single arm of a
parallel study with the supratherapeutic dose following the therapeutic dose (dose escalation), or the
supratherapeutic dose can follow the therapeutic dose (dose escalation) in a crossover study (see for example Zhang,
et al., 2007). When such designs are employed, the supratherapeutic dose clearly does not have the same design
characteristics as the other treatments and questions regarding potential bias can arise. Discussion of such design
alternatives is beyond the scope of this white paper.
7.3 Baseline and Treatment Difference (Drug Effect)
In this section, three different baseline definition alternatives are described. For each baseline definition, the
resulting definition of treatment differences is described. Note that this is not an exhaustive list of possibilities. For
example, triple-delta (∆∆∆QTc) treatment difference definitions are possible where both lead-in day ECGs are
collected at matched time points to the time points of collection on the treatment day and one or more ECGs are
collected immediately before treatment administration (and at the same time point on the lead-in days), essentially
combining 7.3.1 and 0 below. Multiple lead-in days could be used to create averaged lead-in day values to be used
for a time-matched baseline. Potentially, on-treatment QTc values could be compared without any baseline
difference comparison, especially in crossover studies where each subject is acting as his/her own control (singledelta [∆QTc]) but we are unaware of such a single-delta ever being used for analysis in a published TQT study.
Research on alternative baseline definitions is likely to continue, and what might be acceptable to any given
regulatory agency at any specific point in time cannot be predicted with known accuracy. The paragraph above
alludes to the potential for many alternatives and three such alternatives are discussed in greater detail below. As of
the publication of ICH E-14 Q& A (ICH, 2012; Question 6), the recommended baseline for crossover studies (the
most common design) is discussed in Section 7.3.3 and the recommended baseline for parallel studies is discussed in
Section 7.3.1. The baseline definition discussed in Section 7.3.2 is discussed in some detail because it has received
attention in published statistical literature. Again, even more alternatives can be conceptualized.
The notation of the sections below has its origins in the TAUG document (see Section 5). The authors have
attempted to improve upon the notation in order to make it more precise for a statistical audience. Because there is a
discrepancy between the TAUG and this document at present, we hope that this discrepancy would be resolved
completely in the next version.
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7.3.1 Time-Matched Lead-in Day Baseline; Double-Delta Treatment
Difference
For time-matched baseline, the baseline for each period is the average of the replicate set of values at a time point on
the lead-in (baseline) day (Day -1) that corresponds to the post-dose time point. ECGs are collected or extracted
from continuous recording in replicate sets (usually 3 replicates about a minute or so apart) at each b j and Xij. The
average of the replicates is used for analysis. With the original ICH E-14 guidance, this was the standard baseline
definition for both crossover and parallel studies. With the publication of the ICH E-14 Q&A (ICH, 2012; Question
6), the requirement for this baseline definition for crossover studies was relaxed (See Section 7.3.3 below).
For both parallel and crossover designs, ΔQTcij is computed for each subject at each drug (including Placebo) QTc
at each time point for each day of treatment on an individual subject basis: ΔQTcij = (Xij − bj ) where i=1, 2, … d,
j=1, 2, … n; d=days postdose and n=time point. ΔQTcij is the change from baseline (time-matched) in QTc at each
time point for each day of treatment on an individual subject basis.
For crossover designs, ΔΔQTcij is computed for each subject: ΔΔQTcij = ΔQTcijDrug A − ΔQTcijPlacebo . ΔΔQTcij is
the difference between the change from baseline (time-matched) in QTc for drug and placebo at each time point for
each day of treatment on an individual subject basis.
For a parallel design, ΔQTcijs would be averaged across subjects: ̅̅̅̅̅̅̅̅̅̅̅
ΔΔQTcij = ̅̅̅̅̅̅̅̅̅
ΔQTcij Drug A − ̅̅̅̅̅̅̅̅
ΔQTcij
.
Placebo
̅̅̅̅̅̅̅̅̅̅
ΔΔQTcij is the average difference between drug and placebo across subjects of the change from baseline (timematched) in QTc, at each time point for each day of treatment.
7.3.2 Time-Averaged Lead-in Day Baseline; Double-Delta Treatment
Difference
The ECGs are collected or extracted from continuous recording in replicate sets (usually three replicates about a
minute or so apart at each bj and Xij). The average of the replicates is used for analysis. The time-averaged baseline
from a lead-in (baseline) day and the baseline day of each period is the average of all baseline QTc values of each of
the baseline days (e.g. Day -1, 1 hour, 2 hour, 3 hour, 4 hour, etc.). Several statistical manuscripts have advocated
this baseline definition over the time-matched lead-in day baseline for parallel studies (Meng et al., 2010) and both
crossover and parallel studies (Sethuraman and Sun, 2009) but this has not become a regulatory standard.
For both parallel and crossover designs, ΔQTcij is computed for each subject at each drug (including Placebo) for
each day of treatment on an individual subject basis: ΔQTcij = (Xij − bavg ) where bavg = ∑ bj /n; i=1, 2, … d, j=1,
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2, … n; d=days postdose and n=time point. ΔQTcij is the change from baseline (time-averaged) in QTc at each time
point for each day of treatment on an individual subject basis.
For crossover designs, ΔΔQTcij is computed for each subject: ∆∆QTcij = ΔQTcijDrug A − ΔQTcijPlacebo . ΔΔQTcij is
the difference in the change from baseline (time-averaged) in QTc between drug and placebo at each time point for
each day of treatment on an individual subject basis.
For a parallel design, ΔQTcij’s would be averaged across subjects: ̅̅̅̅̅̅̅̅̅̅
ΔΔQTcij = ̅̅̅̅̅̅̅̅
ΔQTcij
− ̅̅̅̅̅̅̅̅̅
ΔQTcij jPlacebo .
Drug A
̅̅̅̅̅̅̅̅̅̅
ΔΔQTcij is the average difference in the change from baseline (time-averaged) in QTc between drug and placebo
across subjects at each time point for each day of treatment.
This baseline definition is discussed and arguments supporting its use are advanced in statistical literature (Meng et
al., 2010 and Sethuraman and Sun, 2009) but it is not suggested as a baseline for either parallel or crossover studies
in ICH E-14 Q& A (ICH, 2012; Question 6).
7.3.3 Predose Averaged Baseline; Double-Delta Treatment Difference
For predose averaged baseline, ECGs are collected or extracted as replicate sets (usually three replicates about a
minute or less apart) at predose in close temporal proximity to treatment administration (e.g., 15 minute intervals
and immediately before treatment administration on the same day of treatment administration) and as replicate sets
(usually 3 replicates about a minute or so apart) at each X ij post dose. The average of all the replicates collected
predose is used as the baseline for analysis. This baseline definition has the advantage of eliminating the necessity
of an inpatient lead-in day from the experimental design with all the monetary and operational expense for each
treatment period. This baseline definition is now accepted for crossover studies that are both single-dose and
multiple-dose administered over multiple days (ICH E14, 2012; Question 6).
For both parallel and crossover designs, ΔQTcij is computed for each subject at each drug (including Placebo) for
each day of treatment on an individual subject basis: ΔQTcij = (Xij − b0 ) where b0 = ∑ bj /k; i=1, 2, … d, j=1, 2,
… n; d=days postdose and n=time point. ΔQTcij is the change from baseline (time-averaged) in QTc at each time
point for each day of treatment on an individual subject basis.
For crossover designs, ΔΔQTcij is computed for each subject: ∆∆QTcij = ΔQTcijDrug A − ΔQTcijPlacebo where b0 =
∑ bj /k; i=1, 2, … d, j=1, 2, … n; d = days postdose, k = number of predose QTc values, n = time point. ΔΔQTcij is
the difference between drug and placebo in the change from baseline (predose-matched) in QTc at each time point
for each day of treatment on an individual subject basis.
̅̅̅̅̅̅̅̅̅̅ij = ̅̅̅̅̅̅̅̅
For a parallel design, the ΔQTcij’s would be averaged across subjects: ΔΔQTc
ΔQTcij
− ̅̅̅̅̅̅̅̅
ΔQTcij
.
Drug A
Placebo
̅̅̅̅̅̅̅̅̅̅
ΔΔQTc
ij is the average difference between drug and placebo across subjects of the change from baseline (predoseaveraged) in QTc at each time point for each day of treatment. Recall however that this baseline definition might
not be accepted by regulators for parallel studies at this time.
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8. Analysis
8.1 Primary Analysis
There are two hypothesis tests to be performed in a TQT/QTc studies:
1. The hypothesis test to confirm no study drug effect that results in a relevant prolongation of the QT/QTc as
compared to the placebo group;
2. The study is capable of detecting differences in QT/QTc, (to establish the assay sensitivity) by
demonstrating the QT/QTc effects of an active control that results in QTc prolongation only slightly longer
than can be observed by chance with placebo (5 to 10 msec range, ideally) can be detected.
8.1.1 Testing of QT Prolongation
The primary endpoint should be the time-matched mean difference between the drug and placebo after baseline
adjustment at each time point.
According to the ICH E14 (Section 2.2.4), the test drug is classified as negative (lack of evidence of QT/QTc
prolongation), if the upper bound of the one-sided 95% confidence interval (CI) for the largest time matched mean
difference between the drug and placebo excludes 10 msec. This definition was chosen to provide reasonable
assurance that the mean effect of the study drug on the QT/QTc is not greater than around 5 msec. When the CI
upper bound of the largest time-matched difference exceeds the threshold, the study is termed “positive” (lack of
prolongation effect cannot be established) and additional ECG safety evaluation in subsequent clinical studies
should be performed.
The QT intervals (means of replicates) are usually measured at multiple time points to provide reasonable assurance
that the mean difference between study drug and placebo on the QT/QTc interval is not greater than the pre-defined
threshold. In practice, an intersection-union test (IUT) is applied to assess QT/QTc prolongation. It is the uniformly
most powerful unbiased test (Berger and Hsu, 1996). The hypothesis is specified as follows:
H 0 : {( drug(ti )   placebo (ti ) )  10}, i 1, 2,..., n , versus
H1 : {( drug(ti )   placebo (ti ) )  10}, i  1, 2,..., n
where
drug(t )
i
and
 placebo(t ) are the mean change from baseline of QT for test drug and placebo respectively, at
i
time point ti.
The statistical model for estimating the treatment effects and the CIs depend on the study design and other factors.
An analysis of covariance model (ANCOVA) or mixed effects model repeated measures (MMRM) is usually used
to estimate the treatment effect and the confidence intervals. For crossover designs, the model usually includes
treatment, time, period, treatment sequence, and the time-by-treatment interactions as fixed effects, and baseline as a
covariate and ΔΔQTc being the dependent variable. For parallel designs, the model usually includes treatment,
time, time-by-treatment interactions as fixed effects and baseline as a covariate and ΔQTc being the dependent
variable. The ANCOVA model using day-averaged (time-averaged; Section 7.3.2) baseline is recommended for the
analysis of parallel-group thorough QT/QTc studies (Sun et al. 2012). The baseline definition should be prespecified (refer to Section 7.3). The authors have had success with the models specified here and are of the opinion
that other covariates should only to be added if there are excellent clinical reasons for including them. Further
discussion on the models and possibly the covariance structure is beyond the scope of this white paper.
8.1.1.1 Multiplicity Issues
For the test drug to placebo comparison, as noted above, an IUT method has been proposed and most frequently
used as the primary method of analyzing the TQT/QTc study.
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The IUT method controls the Type I error. Specifically, the comparison between the test drug and placebo requires
no adjustment for multiplicity and thus the standard one-sided 95% CIs are used at all post-dose time points.
However, the IUT method may potentially lead to false positive trial results (failing to reject inferiority [not finding
non-inferiority] in this specific case of TQT study analysis). The false positive rates depend on several factors
including variability of the study, sample size, the number of time points, and the true mean difference to be
detected. The probability of incorrectly not being able to reject a potentially clinically meaningful QT/QTc effect
increases (or statistical power decreases) with the number of postdose time points (Patterson et al. 2005).
8.1.2 Assay Sensitivity
The confidence in the ability of the study to detect QT/QTc prolongation can be greatly enhanced by the use of a
concurrent positive control group to establish assay sensitivity. The positive control should have an effect on the
mean QT/QTc interval of about 5 msec (i.e., an effect that is close to the QT/QTc effect that represents the threshold
of regulatory concern, around 5 msec). However, as moxifloxacin is the accepted regulatory positive control
standard, an effect in the 10 to 15 msec range for the positive control is acceptable (Florian et al., 2011).
In the ICH E14 Question and Answers in 2012 (ICH E14, 2012), FDA clarified how to assess the adequacy of the
positive control in the QTc study. There are two conditions required for ensuring assay sensitivity:
1.
The positive control should show a significant increase in QTc; i.e., the lower bound of the one-sided 95%
CI must be above 0 msec for at least one time point. This result shows that the trial is capable of detecting
an increase in QTc, a conclusion that is essential to concluding that a negative finding for the test drug is
meaningful.
2.
The study should be able to detect an effect of about 5 msec (the QTc threshold of regulatory concern).
Therefore, the size of the effect of the positive control is of particular relevance. It determines the
threshold of the lower bound. There are at least two approaches:
a.
If a positive control has a known effect of greater than 5 msec (e.g., 10 msec), assay sensitivity will be
established if the lower bound of the one-sided 95% CI for the mean treatment difference between the
positive control and placebo is above 5 msec for at least one time point. FDA authors (Florian 2011)
have reported that for studies using oral moxifloxacin as a positive control, 18 conventional ΔΔQTcF
for (moxifloxacin – placebo) ranged from 7.7 to 16.7 msec and for 11 of these studies, the range was
10.7 to 12.9 msec. This approach has proven to be useful in many regulatory cases. However, if the
positive control has too large an effect, the study’s ability to detect a 5-msec QTc prolongation might
be questioned.
H0 :
iR
H1 :
iR
{( active(ti )   placebo (ti ) )  5},
versus
{( active(ti )   placebo (ti ) )  5}.
where R is a pre-selected subset of time points; 𝜇𝑎𝑐𝑡𝑖𝑣𝑒(𝑡 ) and 𝜇𝑝𝑙𝑎𝑐𝑒𝑏𝑜(𝑡 ) are mean changes from
𝑖
𝑖
baseline of QTc for active drug and placebo respectively, at time point t i.
The authors note that if moxifloxacin is used, then this criterion implicitly requires that the
experimental group respond to moxifloxacin in the same manner as historical control groups. Even if
the smallest ΔΔQTcF between active control (moxifloxacin) and placebo is found to be statistically
significant at the conventional p<0.05 level after appropriate adjustment for multiple comparisons but
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the CI for the difference does not meet the a priori magnitude specified above, the study will be
declared to have failed to demonstrate assay sensitivity. The authors recommend, based on their
experience with oral moxifloxacin (see for example Loghin et al., 2013), as well as the fact that FDA
authors (Florian et al., 2011) report that when oral moxifloxacin has been administered within 3 hours
of a meal, mean maximum concentration (Cmax) is reduced from 3085 ng/mL to 2668 ng/mL (13.5%)
due to delayed absorption and Cmax is the determinant of the maximum effect on QTc rather than total
cumulative exposure (area under the curve) to reduce risk of failing to establish assay sensitivity by
using intravenous moxifloxacin to avoid potential issues with other factors such as food effect.
b.
If a positive control has a known effect close to 5 msec, assay sensitivity can be demonstrated if the
point estimate of the maximum mean difference with placebo is close to 5 msec for at least one time
point, and the lower bound of the one-sided 95% CI for the mean treatment difference between the
positive control and placebo is above 0 msec for at least one time point.
H0 :
iR
H1 :
iR
{( active(ti )   placebo (ti ) )  0},
versus
{( active(ti )   placebo (ti ) )  0}.
where R is a a pre-selected subset of time points; 𝜇𝑎𝑐𝑡𝑖𝑣𝑒(𝑡 ) and 𝜇𝑝𝑙𝑎𝑐𝑒𝑏𝑜(𝑡 ) are mean changes from
𝑖
𝑖
baseline of QT for active drug and placebo respectively, at time point t i.
The analysis model of the positive control compared to placebo is similar to the analyses of the test drug compared
to placebo.
8.1.2.1 Multiplicity Issues
Assay sensitivity is usually defined in terms of the statistically significant difference between the positive control
and placebo at one or more postdose time points.
Due to the multiple comparisons, the probability of demonstrating assay sensitivity is inflated.
To avoid the inflation, the clinical trial sponsor can consider the following options:


Perform the assay sensitivity analysis at fewer post-dose time points. Because the effect of a positive
control on QTc interval is generally well understood, it is reasonable to restrict the positive control versus
placebo comparisons to the number of time points when the QTc effect of the positive control is most
pronounced. For example, if moxifloxacin 400 mg serves as the positive control, significant QT interval
prolongation is likely to occur during the 2- to 4-hour window after the dose and the sponsor can consider
excluding the post-dose ECG recordings collected after 10 hours post-dose from the assay sensitivity
analysis.
Perform a multiplicity adjustment. When performing this adjustment, it is important to utilize a multiple
testing procedure that takes into account correlations among the estimated treatment differences at
postdose time points (e.g., resampling-based multiplicity adjustments, Westfall and Young, 1993). Basic
multiple tests such as the Bonferroni test may be avoided because they tend to be very conservative in
multiplicity problems. The choice of which multiplicity adjustment method to use must be pre-specified
for a specific study.
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8.1.3 Categorical Analyses
Categorical (or outlier) analyses are often performed to gain an impression of the proportion of study participants
who exceed predefined upper reference limit values. Outlier reference limits can be defined in terms of absolute
values, change from baseline values or a combination of change from baseline and absolute value. The following
thresholds are often used (but alternative limits may be used):
Absolute QTc interval prolongation:

QTc interval >450 msec

QTc interval >480 msec

QTc interval >500 msec
Change from baseline measurement in QTc interval:

QTc interval increase >30 msec

QTc interval increase >60 msec
It has to be noted that the limits above were selected based on the experience of the writers of this white paper and
ICH E14 guidance. As these limits have their basis in QTcB whereas QTcF is most commonly used, it is strongly
recommended for the reader to investigate recent literature from the regulators before defining their analysis (see
Mason et al., 2007, demonstrating that comprable, non-parametric 98 percentile limits for 46,129 subjects with
morphologically normal ECGs and no cardiac disease, was 457 msec fror QTcB and 445 msec for QTcF [12 msce
for QTcF compared to QTcB]), as these recommended limits may change in the future. Change limits should be put
in raw numbers or can be percentage adjusted if empirically derived percentage limits are available.
All outliers should be summarized for each treatment group on at each time point and overall basis. The outlier
summary tables should include counts of subjects (at each time point and overall). Therefore, if a subject
experienced more than one incidence of a particular outlier event, the subject should be counted only once for that
event.
Many regulators might require categorical analyses of the other ECG numerical parameters as well (i.e., PR, QRS,
HR). Multiple lower and upper limits exist for these additional numerical parameters and others have been
suggested for QTc as well. Different regulatory bodies might have different limits of interest across time, it is
strongly recommended for the reader to investigate recent literature from the regulators before defining their limits
to be used in the analysis.
Statistical analyses comparing treatments may be performed but is considered out of the scope of this white paper.
8.1.4 Morphological (Qualitative) Analyses
Morphological (qualitative) abnormal findings (e.g., rhythm; axis; conduction; evidence of ischemia, injury, or
infarction; evidence of hypertrophy; other ST abnormalities; other T-wave abnormalities; U-wave abnormalities;
findings consistent with pericarditis, electrolyte abnormalities, chronic obstructive pulmonary disease [COPD], etc.)
in the ECG waveform should be described and the data presented in terms of the number and percentage of subjects
in each treatment arm who had changes from baseline that represented the appearance or worsening of the
morphological abnormality (e.g., tables of the incidences of the observed treatment emergent abnormalities by
specific abnormal finding, not just by category of findings). Special attention can be directed at abnormalities
and/or changes in the appearance of the T wave/U wave that might be indicative of delayed repolarization, such as
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double humps ("notched" T wave), indistinct terminations (TU complex), delayed inscription (prolonged isoelectric
ST segment), widening, flattening, and inversion. T-wave alternans (beat-to-beat variability in the amplitude,
vector, and/or morphology of the T wave) is considered to be a harbinger of ventricular arrhythmias and might
receive special attention with respect to occurrence of any of these findings. Several of these T-wave/U-wave
findings can be numerically quantified and analyzed, but this is not a routine expectation in TQT study analyses.
While the predictive value of morphological analyses is not well characterized (even if the drug does have an effect
on the ECG, these abnormal morphological findings will be observed with low frequency if at all in a TQT study),
differences in the incidence of abnormalities between treatment arms, if observed, could prove informative. This is
particularly the case for delayed conduction (delayed depolarization) for which the TQT study would be equally
sensitive as it is for delayed repolarization. Morphological changes including atrioventricular blocks (PR
prolongatyion and absence of QRS complexes following P waves) and widened QRS complexes (bundle branch
blocks or interventriular conduction delay) might be observed. Such delays can be observed with a variety of drugs,
such as tricyclic antidepressants, and can be of equal clinical significance to that of delayed ventricualr
repolarization (prolonged QTc).
Statistical analyses comparing treatments may be performed but is considered out of the scope of this white paper.
8.1.5 Exploratory Analysis of Other Continuous ECG Parameters
In the authors’ experience, exploratory analysis is often performed on the other continuous ECG parameters such as
PR, QRS, and HR. This analysis is often performed using the same model as the one defined for the primary
analysis (Section 8.1.1) to obtain estimates of the mean difference in comparison to placebo for change from
baseline (no formal statistical test is usually performed only CI’s). This analysis is regarded by the authors “as good
to have” but not critical.
8.2 Concentration-Response Relationship (CRR)
Recent research has strongly supported the contention that intensive PK-PD modeling (CRR analysis) in ascending
dose Phase I studies is sufficiently robust in demonstrating a drug’s effect or lack of effect on ventricular
repolarization / QTc to substitute for (replace the need for) a TQT study (Darpo et al., 2015 and Zhang et al., 2015).
As of December 2015, the ICH Working Group had expressed an official position that PK-PD (CRR) analysis might
substitute for a TQT study (ICH 2015). A discussion of this topic is out of scope for this white paper. The
following discussion focuses on performance of CRR analysis within a TQT study.
8.2.1 Rationale for Performing a Concentration-Response Analysis
within a TQT Study
8.2.1.1 When Results of the TQT Study Are Negative (Non-inferiority Is Supported by
the Results)
When the primary analysis shows evidence of lack of meaningful QT/QTc changes, there still may be small QTc
changes taking place upon administration of the investigational drug at supra-therapeutic doses below the threshold
of regulatory concern. A CRR analysis can clarify whether this is the case or not and inform drug development (e.g.
predict the QTc changes at doses and in subpopulations/factors that were not studied directly). It can also help in
increasing confidence in regards to the time points chosen for the primary analysis, by investigating possible delayed
effects. If the TQT is negative a PK-QTc analysis might not be required by authorities; however when a small drug
effect is expected (based on pre-clinical info, such as human ether-a-go-go [hERG] test, animal data, etc.) it is a
”nice to have”. This is an evolving regulatory matter and regulators might want a CRR analysis even with a
negative study.
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8.2.1.2 When Results of the TQT Study Are Positive (Cannot Reject Inferiority based
on Study Results)
When the primary analysis does not support lack of QT/QTc prolongation, CRR analysis is an excellent tool to
inform further sponsors and regulators not only about the magnitude of the possible QTc prolongation but also:

as mentioned earlier, the primary IUT analysis is very conservative (the false-positive rate reported in
literature [ICH,2014] is around 20%) and a CRR analysis can either confirm the results of the primary
analysisas well as provide a potentially less-biased characterization of the drug effect than the primary
analysis or strongly suggest that the results of the primary analysis represented a false-positive result and
point toward further investigation being needed;
 help predict the QT effects of doses, dosing regimens, routes of administration, or formulations that were
not studied directly. Interpolation within the range of concentrations studied is considered more reliable
than extrapolation above the range (any conclusion drawn based on extrapolation of the observed range of
concentrations are likely to be rejected by regulators);
 inform dose selection for later studies;
 inform whether the QTc change occurs simultaneously with the peak concentration (C max) or delayed (e.g.,
effect-compartment or turnover models);
 may assist and clarify the interpretation of equivocal data (on occasion, a TQT study can yield ambiguous
results). For example, if QTc prolongation is observed at a lower dose at a higher dose, or QTc
prolongation is observed at a single isolated time point among a relatively large number of time ppoints,
CRR analysis can help interpret the data;
 analyses of CRR by sex can be helpful for studying the effect of the drug on QT/QTc interval in cases
where there is evidence or mechanistic theory for a gender difference;
 can help predict the effects of intrinsic (e.g., cytochrome P450 isoenzyme status) or extrinsic (e.g., drugdrug PK interactions) factors, possibly affecting inclusion criteria or dosing adjustments in later phase
studies;
8.2.1.3 When Assay Sensitivity Is Not Demonstrated
A CCR might demonstrate that the PK-PD relationship for the positive control is as expected based on historical
control and that failure to demonstrate assay sensitivity was likely due to inadequate positive control exposure due to
one or more of several factors (e.g., delayed absorption of an oral formulation and failure to reach an expected Cmax
due to a food effect when a meal was given shortly before the positive control). Furthermore, it might be possible to
demonstrate that assay sensitivity would have likely been demonstrated if sufficient, and expected, exposure to the
positive control had been achieved.
8.2.2 Methodology
In all situations, it is important that the modeling assumptions, criteria for model selection, and rationale for model
components be specified prior to analysis to limit bias as models with different underlying assumptions on the same
data can produce discordant results. For the same reason pre-specification of model characteristics (e.g., structural
model, objective criteria, goodness of fit) based on knowledge of the pharmacology is recommended whenever
possible.
Mixed effects model can be used to describe the CRR with (Δ)ΔQTc as response (the (Δ)ΔQTc notation will be used
to show that the equation/statement applies both for the ΔQTc and ΔΔQTc subject to the study design). The
following model definition can be considered:
(Δ)ΔQTci (t) = Intercept(i) + drugEffect + eta(i) + eps,
for subject i
where eta(i) stands for subjects’ i inter-individual variability and eps stands for the residual variability.
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The drug effect is given by
(i) in linear effect models
drugEffect = Concentration * Slope
where Slope = drug effect slope
(ii) in power models
drugEffect = Concentrationb
where b = drug effect power
and
(iii) in Emax models
drugEffect = Emax * Concentration / (EC50 + Concentration)
where Emax = maximal effect of the drug on QTc changes
and EC50 = the concentration at which half of the maximal drug effect is reached.
If a time delay is observed between peak concentration and peak QT effect (hysteresis), other models will need to be
considered. These models are considered out of scope for this white paper.
As with CRR analysis in other contexts, log transformation of concentration or inclusion of other parameters in the
model can be considered. Further discussion on the CRR models is beyond the scope of this white paper, but they
are recommended to be investigated especially in cases of a poor model fit.
For crossover designs, the ΔΔQTc should be used. For parallel designs, the ΔQTc is used. There are different
opinions for parallel designs whether placebo observations should be included in the analysis as having zero
concentration. As no formal guidance exists at the time of writing, the authors leave it at the reader’s personal
experience, but they recommend for the reader to investigate recent literature from the regulators in case such
guidance is issued. The baselines recommended are the same as in the primary analysis i.e. for crossover designs
“pre-dose averaged” baseline and for parallel designs “time-matched” baseline see Sections 7.3 and 8.1.1.
Other considerations for CRR analysis
If assay sensitivity is in question based on the results of the primary analysis, PK/PD analysis of the active control
data can be performed to bring confidence in the assay sensitivity claim. The models recommended here are the
same as the ones for the other PK/PD analysis. If moxifloxacin is to be used then based on Tornøe et al. (2011) and
Florian et al. (2011), we recommend model (i) from the models above.
Finally, the authors stress that a CRR analysis is credible only when the data are well behaved with respect to the
regression line along its entire observed length.
8.3 P-Values and Confidence Intervals
There has been an ongoing debate on the value or lack of value for the inclusion of p-values and/or confidence
intervals in safety assessments (Crowe et al., 2009). This white paper does not attempt to resolve this debate. As
noted in the Reviewer Guidance, p-values or confidence intervals can provide some evidence of the strength of the
finding, but unless the trials are designed for hypothesis testing, these should be thought of as descriptive.
Throughout this white paper (in the suggested tables in the last section of the document), p-values and measures of
spread are included in several places. Where these are included, they should not be considered as hypothesis testing.
If a company or compound team decides that these are not helpful as a tool for reviewing the data, they can be
excluded from the display.
Some teams may find p-values and/or confidence intervals useful to facilitate focus, but have concerns that lack of
“statistical significance” provides unwarranted dismissal of a potential signal. Conversely, there are concerns that
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due to multiplicity issues, there could be over-interpretation of p-values adding potential concern for too many
outcomes. Similarly, there are concerns that the lower- or upper-bound of confidence intervals will be overinterpreted. A mean change can be as high as x causing undue alarm. It is important for the users of these TFLs to
be educated on these issues if p-values and/or confidence intervals are included in the TFLs.
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9. List of Outputs
In TQT studies, the below list of outputs are commonly produced (for the baseline definitions for parallel and
crossover studies, please refer to Section 7.3). The outputs list and shells below is mainly applicable for the parallel
design (unless otherwise stated) with change from baseline (∆QTc) as the primary endpoint, for crossover design the
authors believe the same list/shells are applicable by using the difference to placebo in change from baseline
(∆∆QTc) being the primary endpoint instead.
Furthermore, despite the list below for parallel studies including only change from baseline summary tables and
plots, this is because they are regarded as an absolute necessity; many sponsors like to have these outputs repeated
for the raw values. For crossovers, as stated above, the difference to placebo in change from baseline outputs are
considered as an absolute necessity; however, many sponsors like to have these outputs repeated for the change from
baseline and raw values as well.
It is finally stressed that the list below is not an exhaustive one and only a list of commonly produced outputs.
Type
Figure
Figure
Figure
Figure
Figure
Figure
Table
Table
Table
Table
Table
Table
Table
Listing
Listing
Listing
Listing
Listing
Title
Box plots of change from baseline in continuous ECG parameters by time-point for each
treatment
Estimated mean difference in comparison to placebo and 90% CI for change from baseline in
QTc (∆∆QTc) for treatment
Estimated mean difference in comparison to placebo and 90% CI for change from baseline in
QTc (∆∆QTc) for active control
Raw mean (+/-SE) change from baseline in continuous ECG parameters by treatment
Concentration response for change from baseline in QTc for active control (assay-sensitivity)
Concentration response for change from baseline in QTc for treatment
Treatment comparisons of change from baseline in QTc intervals by time for treatment
Treatment comparisons of change from baseline in QTc intervals by time for active control
Treatment comparisons of change from baseline to all time points in ECG parameters (HR, PR,
QRS) by time for treatment
Summary of values and changes from baseline to all time points in ECG parameters by time and
treatment
Number and percentage of subjects meeting or exceeding clinically noteworthy QT and QTc
interval changes by time point and overall
Number and percentage of subjects meeting or exceeding clinically noteworthy PR, QRS and
HR interval changes by time point and overall
Number and percentage of subjects with abnormal morphological/qualitative ECG findings
ECG intervals (average over repeated measurements)
Change from baseline in ECG intervals (average over repeated measurements)
ECG intervals (each replicate)
ST segment, T-wave and U-wave morphology
ECG findings
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10. Outputs shells
For the shells below, QTcF is often used as the example; however, if other QT correction method
will be used (such as QTcI), the outputs should present that correction method instead. In
summary tables/figures where only treatment and placebo are included, it is recommended that
the output include a second page presenting Assay and Placebo as well (the same applies to cases
where two doses of the active compound are tested).
The author would like to remind the reader that the below is what the authors would expect to
see for this type of study and does not prohibit anyone from providing alternative outputs.
However, it has to be noted that where the authors have an understanding on what the authorities
would like to see (such as the PK/PD outputs below, which originate in publications from FDA
personnel) they presented them. Finally, the outputs below are not meant to overrule sponsor
preferences (see for example the section on p-values above) and in particular for the listings the
authors understand that the information presented will heavily rely on the raw data available.
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Figure 14.2-X.X: Box plots of change from baseline in continuous ECG parameters by time-point for each treatment
PROTOCOL/PRODUCT INFO
(page x of x)
Figure 14.2-X.X: Box plots of change from baseline in continuous ECG parameters by time-point for each treatment
Analysis set: PD analysis set
Cardiac parameter:
XXXXXX
The horizontal line in the box interior represents the median. The symbol in the box interior represents the mean.
Values outside the whiskers are identified with symbols. The upper (lower) edge of the box represents the
75th (25th) percentile. A whisker is drawn from the upper (lower) edge of the box to the largest (smallest)
value within 1.5× interquartile range above (below) the edge of the box
PATH DATA/PROGRAM/OUTPUT
PRODUCTION STATUS/RUN DDMMYYYY: HHMM
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Figure 14.2-X.X: Estimated mean difference in comparison to placebo and 90% CI for change from baseline in QTc (∆∆QTc) for treatment
PROTOCOL/PRODUCT INFO
Figure 14.2-X.X:
(page x of x)
Estimated mean difference in comparison to placebo and 90% CI for change from baseline in QTc (ddQTc) for treatment
Analysis set: PD analysis set
Cardiac parameter: XXXXXX
Treatment: XXXX
PATH DATA/PROGRAM/OUTPUT
PRODUCTION STATUS/RUN DDMMYYYY: HHMM
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Figure 14.2-X.X: Estimated mean difference in comparison to placebo and 90% CI for change from baseline in QTc (∆∆QTc) for active control
PROTOCOL/PRODUCT INFO
Figure 14.2-X.X:
(page x of x)
Estimated mean difference in comparison to placebo and 90% CI for change from baseline in QTc (ddQTc) for active control
Analysis set: PD analysis set
Cardiac parameter: XXXXXX
Treatment: XXXX
PATH DATA/PROGRAM/OUTPUT
PRODUCTION STATUS/RUN DDMMYYYY: HHMM
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Figure 14.2-X.X: Mean (+/-SE) change from baseline in QT, QTc, and HR by treatment
PROTOCOL/PRODUCT INFO
Raw mean (+/-SE) change from baseline in continuous ECG parameters by treatment
Analysis set: PD analysis set
Cardiac parameter:
Treatment:
time (unit)
PATH DATA/PROGRAM/OUTPUT
PRODUCTION STATUS/RUN DDMMYYYY: HHMM
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Figure 14.2-X.X: Concentration-response for change from baseline in QTc for treatment
Note: As the raw data are presented by the data points no raw data points are included in the figure. Individual points in the figure can be added but are not
critical.
PROTOCOL/PRODUCT INFO
(page x of x)
Figure 14.2-X.X:
Concentration response for change from baseline in QTc for treatment
Analysis set: PD analysis set
Compound: XXX, Matrix: YYY, Analyte:ZZZ
Cardiac Parameter:
xxx
Black line is the change from baseline in QTc vs concentration predictions and the grey band is its 90% confidence interval.
Data points depict the raw change from baseline means obtained by grouping at each 10th quantile of concentrations
(each subject contributes only once in the mean, if subject has more than one observation in a quantile the
mean of the subject is obtained prior to the calculation).
Secondary ticks depict the concentration quantiles.
PATH DATA/PROGRAM/OUTPUT
PRODUCTION STATUS/RUN DDMMYYYY: HHMM
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Figure 14.2-X.X: Concentration-response for change from baseline in QTc for active control
Note: As the raw data are presented by the data points no raw data points are included in the figure. Individual points in the figure can be added but are not
critical.
PROTOCOL/PRODUCT INFO
Figure 14.2-X.X:
(page x of x)
Concentration response for change from baseline in QTc for active control
Analysis set: PD analysis set
Compound: XXX, Matrix: YYY, Analyte:ZZZ
Cardiac Parameter:
xxx
Black line is the change from baseline in QTc vs concentration predictions and the grey band is its 90% confidence interval.
Data points depict the raw change from baseline means obtained by grouping at each 10th quantile of concentrations
(each subject contributes only once in the mean, if subject has more than one observation in a quantile the
mean of the subject is obtained prior to the calculation).
Secondary ticks depict the concentration quantiles.
PATH DATA/PROGRAM/OUTPUT
PRODUCTION STATUS/RUN DDMMYYYY: HHMM
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Table 14.2-X.X: Treatment comparisons of change from baseline at all time points in QTc intervals by time for treatment
Note: The model in the footnote is an example, in case of a different model the footnote needs to be updated.
PROTOCOL/PRODUCT INFO
(page x of x)
Table 14.2-X.X: Treatment comparisons of change from baseline at all time points in QTc intervals by time for treatment
Analysis set: PD analysis set
Cardiac parameter:xxxxx
Scheduled time
point (h)
Treatment comparison
x.x
Drug X - Placebo
x.x
x.x
..
x.x
x.x
x.x
Drug X - Placebo
x.x
x.x
..
x.x
N
xx
Estimate
xx
SE
xx
90% CI
(xxx, xxx)
p-value*
0.xxx
xx
xx
xx
(xxx, xxx)
0.xxx
All subjects who have values at both baseline and scheduled time/
relevant visit time period are included in the treatment comparison analysis.
*p-value is the one-sided p-value that Estimate <10
Estimates are obtained from am ANCOVA model with treatment, time, time-by-treatment interactions as fixed effects
and baseline as a covariate
PATH DATA/PROGRAM/OUTPUT
PRODUCTION STATUS/RUN DDMMYYYY: HHMM
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Table 14.2-X.X: Treatment comparisons of change from baseline in QTc intervals for active control, by time at all time points
Note: The model in the footnote is an example, in case of a different model the footnote needs to be updated. The same applies to the p-value.
PROTOCOL/PRODUCT INFO
(page x of x)
Table 14.2-X.X: Treatment comparisons of change from baseline to all time points in QTc intervals by time for active control
Analysis set: PD analysis set
Cardiac parameter:xxx
Scheduled time
point(h)
Treatment comparison
x.x
Drug X - Placebo
x.x
x.x
x.x
N
xx
Estimate
xx
SE
xx
90% CI
(xxx, xxx)
p-value
0.XXX
All subjects who have values at both baseline and scheduled time/
relevant visit time period are included in the treatment comparison analysis.
*p-value is the one-sided p-value that Estimate >5
Estimates are obtained from am ANCOVA model with
and baseline as a covariate
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treatment, time, time-by-treatment interactions as fixed effects
Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
Working Group
Table 14.2-X.X: Treatment comparisons of change from baseline to all time points in ECG parameters (HR, PR, QRS) by time for treatment
Note: The model in the footnote is an example, in case of a different model the footnote needs to be updated.
PROTOCOL/PRODUCT INFO
(page x of x)
Table 14.2-X.X: Treatment comparisons of change from baseline to all time points in ECG parameters (HR, PR, QRS) by time for treatment
Analysis set: PD analysis set
Cardiac parameter:xxxxx
Scheduled time
point(h)
x.x
x.x
x.x
..
Treatment comparison
Drug X - Placebo
N
xx
Estimate
xx
SE
xx
90% CI
(xxx, xxx)
All subjects who have values at both baseline and scheduled time/
relevant visit time period are included in the treatment comparison analysis.
*p-value is the two-sided p-value that the Estimate is not equal to 0
Estimates are obtained from am ANCOVA model with
and baseline as a covariate
PATH DATA/PROGRAM/OUTPUT
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treatment, time, time-by-treatment interactions as fixed effects
Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
Working Group
Table 14.2-X.X: Summary of values and changes from baseline to all time points in ECG parameters by time and treatment
Note: This table has crossover in mind, if a Parallel design, the difference between Treatment and Placebo column can be removed and only their p-value kept.
PROTOCOL/PRODUCT INFO
Table 14.2-X.X: Summary of values and changes from baseline to all time points in ECG parameters
Analysis set: PD analysis set
(page x of x)
by time and treatment
Cardiac parameter:xxxxx
Treatment
N=xxx
Day
XX
Scheduled
time point(h) Statistics
n
x.x
Mean
SD
Min
Q1
Med
Q2
Max
x.x
n
Mean
SD
Min
Q1
Med
Q2
Max
Baseline
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
Change
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
Placebo
N=xxx
p-value*
0.XXX
0.XXX
Baseline
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
Change
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
Difference between
treatment and Placebo
p-value
0.XXX
0.XXX
Change
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
XXX.XX
- Post = Post Baseline, Change= Post-Baseline - Baseline, Baselie is defined as the time-matched value on Day -1
- Only subjects with values at both baseline and scheduled timepoint are included in the analysis
P-value* tests if change from baseline is not equal to 0
P-value** tests the change from baseline difference between treatment and placebo
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p-value**
0.XXX
0.XXX
Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
Working Group
Table 14.2-X.X: Number and percentage of subjects meeting or exceeding clinically noteworthy QT and QTc interval limits by time point and overall
PROTOCOL/PRODUCT INFO
(page x of x)
Table 14.2-X.X: Number and percentage of subjects meeting or exceeding clinical noteworthy QT and QTc interval changes by timepoint and overall
Analysis set: PD analysis set
Day
XX
Treatment
N=xx
n/m (%)
Scheduled
time point(h) Variable
x.x
QTcF (ms)
Placebo
N=xx
n/m (%)
p-value*
Increase > 30ms
Increase >60ms
New > 450 ms
New > 480 ms
New > 500 ms
xx/XX
xx/XX
xx/XX
xx/XX
xx/XX
(xx.x)
(xx.x)
(xx.x)
(xx.x)
(xx.x)
xx/XX
xx/XX
xx/XX
xx/XX
xx/XX
(xx.x)
(xx.x)
(xx.x)
(xx.x)
(xx.x)
0.XXX
0.XXX
0.XXX
0.XXX
0.XXX
Increase > 30ms
Increase >60ms
New > 450 ms
New > 480 ms
New > 500 ms
xx/XX
xx/XX
xx/XX
xx/XX
xx/XX
(xx.x)
(xx.x)
(xx.x)
(xx.x)
(xx.x)
xx/XX
xx/XX
xx/XX
xx/XX
xx/XX
(xx.x)
(xx.x)
(xx.x)
(xx.x)
(xx.x)
0.XXX
0.XXX
0.XXX
0.XXX
0.XXX
QT (ms)
- n : Number of subjects who meet the designated criterion
- m: Number of subjects at risk for a designated change with a non missing value at baseline and postbaseline
- N: Total number of subjects in the treatment group in this analysis set
“new” is the number of subjects who have have an a clinically noteworth QTc at post dose which is not present at pre-dose.
* P-value compares the probability of the clinically noteworthy event of active vs Placebo
PATH DATA/PROGRAM/OUTPUT
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Working Group
Table 14.2-X.X: Number and percentage of subjects meeting or exceeding clinically noteworthy PR, QRS and HR interval limits by time point and overall (with
example limits)
PROTOCOL/PRODUCT INFO
(page x of x)
Table 14.2-X.X: Number and percentage of subjects meeting or exceeding clinically noteworthy PR, QRS and HR interval changes by timepoint and overall
Analysis set: PD analysis set
Day: X
Scheduled
time point(h) Variable
x.x
PR increase > 25% to a value > 200 ms
QRS increase > 25% to a value > 120 ms
HR decrease > 25% to a HR < 50 beats/min
HR increase > 25% to a HR > 100 beats/min
x.x
PR increase > 25% to a value > 200 ms
QRS increase > 25% to a value > 120 ms
HR decrease > 25% to a HR < 50 beats/min
HR increase > 25% to a HR > 100 beats/min
Treatment
N=xx
n/m (%)
Placebo
N=xx
n/m (%)
p-value*
xx/XX
xx/XX
xx/XX
xx/XX
(xx.x)
(xx.x)
(xx.x)
(xx.x)
xx/XX
xx/XX
xx/XX
xx/XX
(xx.x)
(xx.x)
(xx.x)
(xx.x)
0.XXX
0.XXX
0.XXX
0.XXX
xx/XX
xx/XX
xx/XX
xx/XX
(xx.x)
(xx.x)
(xx.x)
(xx.x)
xx/XX
xx/XX
xx/XX
xx/XX
(xx.x)
(xx.x)
(xx.x)
(xx.x)
0.XXX
0.XXX
0.XXX
0.XXX
- n : Number of subjects who meet the designated criterion
- m: Number of subjects at risk for a designated change with a non missing value at baseline and postbaseline
- N: Total number of subjects in the treatment group in this analysis set
* P-value compares the probability of the clinically noteworthy event of active vs Placebo
PATH DATA/PROGRAM/OUTPUT
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Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
Working Group
Table 14.2-X.X: Number and percentage of subjects with abnormal morphological/qualitative ECG findings
PROTOCOL/PRODUCT INFO
Table 14.2-X.X: Number and percentage of subjects with abnormal morphological/qualitative ECG findings
Analysis set: PD analysis set
(page x of x)
Day: X
Treatment
Abnormality Type Finding
Any ECG abnormality
Rhythm
Atrial Flutter
Atrial Fibrillation
Junctional Rhythm
…
Conclusion
complete heart block
Left bundle branch block
…
Placebo
New postbaseline
n(%)
xx (xx.x)
New postbaseline
n(%)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
0.XXX
0.XXX
0.XXX
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
0.XXX
0.XXX
xx (xx.x)
xx (xx.x)
0.XXX
xx (xx.x)
xx (xx.x)
0.XXX
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
0.XXX
0.XXX
p-value
0.XXX
N is the number of subjects with a valid pre-dose and with at least one observation post dose and is used as the denominator
in the calculation of the percentages.
“n” for baseline is the number of subjects with an ECG abnormality at least at one time point at baseline
“n” for post-baseline is the number of subjects with an ECG abnormality at least at one post dose value for the Treatment columns.
“new” is the number of subjects who have an abnormal ECG finding at post dose which is not present at pre-dose.
A subject with multiple occurrences of an abnormality is counted only once for the corresponding treatment or for pre-dose.
* P-value compares the probability of the post-dose abnormality event of active vs Placebo
PATH DATA/PROGRAM/OUTPUT
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Working Group
Listing 16.2.9-X.X: ECG intervals (average over repeated measurements)
PROTOCOL/PRODUCT INFO
Listing 16.2.9-X.X: ECG intervals (average over repeated measurements)
Analysis set: PD analysis set
(page x of x)
Treatment / Treatment sequence: xxxx
Country/
Site/
Subject
Age/
Sex/ Visit/
Race Day
CNTR /
ST1/
XXXXX
YY/
M/
Ca
x/
ddMMyy
CNTR /
ST1/
XXXXX
YY/
M/
Ca
x/
ddMMyy
Scheduled
time
point (h)
QT
(ms)
QTcI
(ms)
QTcF
(ms)
QTcB
(ms)
PR
(ms)
QRS
(ms)
RR
(ms)
HR
(beats/
min)
x.x
x.x
x.x
xx
xx
xx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xx
xx
xx
xx
xx
xx
xx
xx
xx
x.x
x.x
x.x
xx
xx
xx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xx
xx
xx
xx
xx
xx
xx
xx
xx
+ : QT or QTc > 450 ; ++ : QT or QTc >480 ; +++ QT or QTc > 500
* : HR <50 ; ** : HR > 100
^ : PR > 200
@ : QRS > 120
& : RR < 600 ; &&: RR > 1200
! : Value has been excluded from the PD analysis
ECG interval values are based on the average of the repeated measurements within the same scheduled time point.
Flags are applied to the average of the repeated measurements.
Scheduled time includes both baseline and post baseline time points.
PATH DATA/PROGRAM/OUTPUT
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Listing 16.2.9-X.X: Change from baseline in ECG intervals (average over repeated measurements)
PROTOCOL/PRODUCT INFO
(page x of x)
Listing 16.2.9-X.X: Change from baseline in ECG intervals (average over repeated measurements)
Analysis set: PD analysis set
Treatment / Treatment sequence: xxxx
Country/ Age/
Site/
Sex/ Visit/
Subject Race Day
CNTR /
ST1/
XXXXX
YY/
M/
Ca
x/
ddMMyy
CNTR /
ST1/
XXXXX
YY/
M/
Ca
x/
ddMMyy
Scheduled
time
point (h)
QT
(ms)
QTcI
(ms)
QTcF
(ms)
QTcB
(ms)
PR
(ms)
QRS
(ms)
RR
(ms)
HR
(beats/
min)
x.x
x.x
x.x
xx
xx
xx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xx
xx
xx
xx
xx
xx
xx
xx
xx
x.x
x.x
x.x
xx
xx
xx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xx
xx
xx
xx
xx
xx
xx
xx
xx
ECG interval values are based on the average of the repeated measurements within the same scheduled time.
PATH DATA/PROGRAM/OUTPUT
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Working Group: Standard Analyses and Scripts
Working Group
Listing 16.2.9-X.X: ECG intervals (each replicate)
PROTOCOL/PRODUCT INFO
(page x of x)
Listing 16.2.9-X.X: ECG intervals (each replicate)
Analysis set: PD analysis set
Treatment / Treatment sequence: xxxx
Country/ Age/
Site/
Sex/ Visit/
Subject Race Day
Scheduled
time
ECG time
point (h) (hh:mm:ss)
CNTR /
ST1/
XXXXX
x.x
YY/
M/
Ca
x/
ddMMyy
x.x
CNTR /
ST1/
XXXXX
YY/
M/
Ca
x/
ddMMyy
x.x
x.x
x.x
QT
(ms)
QTcI
(ms)
QTcF
(ms)
QTcB
(ms)
PR
(ms)
QRS
(ms)
RR
(ms)
HR
(beats/
min)
hh:mm:ss
hh:mm:ss
hh:mm:ss
hh:mm:ss
hh:mm:ss
hh:mm:ss
xx
xx
xx
xx
xx
xx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xx
xx
xx
xx
xx
xx
xx
xx
xx
xx
xx
xx
xx
xx
xx
xx
xx
xx
hh:mm:ss
hh:mm:ss
hh:mm:ss
xx
xx
xx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xxx
xx
xx
xx
xxx
xxx
xxx
+ : QT or QTc > 450 ; ++ : QT or QTc >480 ; +++ QT or QTc > 500
* : HR <50 ; ** : HR > 100
^ : PR > 200
@ : QRS > 120
& : RR < 600 ; &&: RR > 1200
! : Value has been excluded from the PD analysis
PATH DATA/PROGRAM/OUTPUT
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Working Group: Standard Analyses and Scripts
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Listing 16.2.9-X.X: ST segment, T-wave and U-wave morphology
PROTOCOL/PRODUCT INFO
Listing 16.2.9-X.X: T-wave and U-wave morphology
Analysis set: PD analysis set
(page x of x)
Treatment / Treatment sequence: xxxx
Country/
Site/
Subject
Age/
Sex/ Visit/
Race Day
CNTR /
ST1/
XXXXX
YY/
M/
Ca
x/
ddMMyy
CNTR /
ST1/
XXXXX
YY/
M/
Ca
x/
ddMMyy
Scheduled
time point
(h)
ECG Time
(hh:mm:ss)
T-wave
morphology
ST
Segment
U-wave
present
x.x
x.x
x.x
hh:mm:ss
hh:mm:ss
hh:mm:ss
xxxxx
xxxxx
xxxxx
xxxx
xxxx
xxxx
xxxxx
xxxxx
xxxxx
x.x
x.x
x.x
hh:mm:ss
hh:mm:ss
hh:mm:ss
xxxxx
xxxxx
xxxxx
xxxx
xxxx
xxxx
xxxxx
xxxxx
xxxxx
! : Value has been excluded from the PD analysis
ST segment: all terms
T waves: all terms
U waves: all terms
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Listing 16.2.9-X.X: ECG findings
Note: This listing will include the morphological (qualitative) abnormal findings.
PROTOCOL/PRODUCT INFO
(page x of x)
Listing 16.2.9-X.X: ECG findings
Analysis set: PD analysis set
Treatment / Treatment sequence: xxxx
Country/
Site/
Subject
Age/
Sex/ Visit/
Race Day
CNTR /
ST1/
XXXXX
YY/
M/
Ca
x/
ddMMyy
CNTR /
ST1/
XXXXX
YY/
M/
Ca
x/
ddMMyy
ECG Interpretation
Comments
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
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Working Group: Standard Analyses and Scripts
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11. Acknowledgements
The key contributors include: Christos Stylianou, Charles Beasley, Balakrishna Hosmane, Xuewei Cui and Otilia
Lillin.
Additional contributors include: Charlotte Baidoo, Cathy Bezek, Greg Ball, Walter Beate, Chris Decker, Simons
Gudrun, Patel Katie, Donna Kowalski, Nejamin Lang, Fang Liu, Mercy Navarro, Mary Nilsson, Palani Ravindran,
John Smith, Troy Steven, Anastasia Stylianou, Sigrun Unger, Lu Zhang, and any additional contributors that may
have provided comments anonymously.
12. Project Leader Contact Information
Name: Christos Stylianou (Lead author for this white paper)
Enterprise: ClinBAY Ltd
Address: Office 401, Vanezis business center, 171 Arch. Makariou III av., Limasol,3027, Cyprus
Work Phone: +35799909082
E-mail: [email protected]
Name: Mary Nilsson (Analysis and Display White Papers Project Team leader)
Enterprise: Eli Lilly & Company
City, State ZIP: Indianapolis, IN 46285
Work Phone: 317-651-8041
E-mail: [email protected]
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13. References
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of atomoxetine on the QT interval in healthy CYP2D6 poor metabolizers." British Journal of Clinical
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of cardiovascular electrophysiology, 2001: 12(4), 411-420.
Malik, M. "The imprecision in heart rate correction may lead to artificial observations of drug induced QT interval
changes." Pacing and clinical electrophysiology, 2002a: 25(2), 209-216.
Malik, M., Andreas, J. O., Hnatkova, K., Hoeckendorff, J., Cawello, W., Middle, M., ... Braun, M. "Thorough
QT/QTc study in patients with advanced Parkinson's disease: cardiac safety of rotigotine." Clinical
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Project: Analysis and Display White Papers Project Team
Title: Analyses and Displays Associated with TQT Studies
Working Group: Standard Analyses and Scripts
Working Group
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