Download Supplement for ADaM

ADaM Supplement to the TAUG-Diabetes
Version 1.0 (Provisional)
Prepared by the
CFAST Diabetes ADaM Sub-Team
Notes to Readers
•
•
•
This is Version 1.0 of the CDISC ADaM Supplement to the TAUG-Diabetes. It makes use of domains and
assumptions which are not final as of its publication, and is therefore a provisional, rather than final, release.
This supplement is intended to be incorporated into the next version of the TAUG-Diabetes as Section 5,
Analysis Data.
This document is based on ADaM v2.1 and ADaMIG v1.0.
Revision History
Date
2015-12-18
2015-07-17
Version
1.0
1.0 Draft
Summary of Changes
Provisional Release
Draft for Public Review
See Appendix C for Representations and Warranties, Limitations of Liability, and Disclaimers.
CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
CONTENTS
1
INTRODUCTION ................................................................................................................. 2
2
SUBJECT-LEVEL ANALYSIS DATA: ADSL .................................................................. 3
2.1
2.2
STRATIFICATION VARIABLES ................................................................................................................................3
ADSL EXAMPLE ...................................................................................................................................................5
3
ANALYSIS OF HYPOGLYCEMIC EPISODES............................................................... 8
3.1
3.2
3.3
3.4
HYPOGLYCEMIC EPISODES ANALYSIS DATASET ...................................................................................................8
HYPOGLYCEMIC EPISODES ANALYSIS RESULTS .................................................................................................. 11
HYPOGLYCEMIC EPISODES SUMMARY DATASET................................................................................................. 12
HYPOGLYCEMIC EPISODES SUMMARY ANALYSIS RESULTS ................................................................................ 15
4
ANALYSIS OF GLYCATED HEMOGLOBIN ............................................................... 18
4.1 HBA1C ANALYSIS DATASET ............................................................................................................................... 18
4.2 HBA1C ANALYSIS RESULTS ................................................................................................................................ 20
4.2.1
Longitudinal Repeated Measures Model .............................................................................................. 20
4.2.2
Categorical Analysis............................................................................................................................. 22
5
ANALYSIS OF GLUCOSE LEVELS ............................................................................... 23
5.1 SELF-MONITORED GLUCOSE PROFILE ANALYSIS DATASET ................................................................................ 23
5.2 SELF-MONITORED GLUCOSE ANALYSIS RESULTS ............................................................................................... 27
5.2.1
Longitudinal Repeated Measures Model .............................................................................................. 27
5.2.2
Self-Monitored Glucose Plots .............................................................................................................. 29
5.3 MIXED-MEAL TOLERANCE TEST DATASET ......................................................................................................... 30
5.4 MIXED MEAL TOLERANCE TEST ANALYSIS RESULTS ......................................................................................... 34
APPENDICES ............................................................................................................................. 36
APPENDIX A: CFAST DIABETES ADAM SUB-TEAM ................................................................................................... 36
APPENDIX B: REFERENCES ........................................................................................................................................... 36
APPENDIX C: REPRESENTATIONS AND WARRANTIES, LIMITATIONS OF LIABILITY, AND DISCLAIMERS........................ 37
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
1 Introduction
This ADaM Supplement to the TAUG-Diabetes demonstrates the use of the CDISC Analysis Data Model (ADaM)
to create datasets to support the analysis of statistical endpoints common to diabetes trials. Diabetes is a complex
disease for which there are many clinical assessments. In turn, these clinical assessments can be used to derive a
variety of statistical endpoints used to assess interventions. This document is focused on describing analysis data for
three areas of clinical assessments, namely hypoglycemic events, HbA1c, and glucose. This is a supplement to
version 1.0 of the CDISC Therapeutic Area Standards User Guide for Diabetes (TAUG-Diabetes v1.0), and in the
future these two documents are intended to be combined.
It is important to note that the examples in this supplement were chosen in order to illustrate different ADaM
concepts and data structures. The examples are not meant to be applicable to every possible diabetes trial since the
trial objectives and the primary endpoints will dictate the actual analyses that are performed. The analysis of
hypoglycemic events lends itself to creation of statistical endpoints that relate to events, such as incidence or
prevalence of events or event severity, and the use of the Occurrence Data Structure (OCCDS) in ADaM. The
analysis of the continuous measures of HbA1c and glucose is multifaceted where statistical endpoints may range
from continuous measures such as change from baseline or percent change from baseline to categorical measures
such as a binary response based on achieving a pre-defined criterion. These data may be analyzed at single predefined point in time (e.g., after 8 weeks of treatment) or longitudinally. As such these endpoints can be analyzed
through the use of the ADaM Basic Data Structure (BDS). An example of a subject-level analysis dataset (ADSL) is
also provided and is based on ADaM.
Examples of statistical data summaries, in tabular or graphic form, are also included in this document. These table
and figure displays are for illustration purposes. They are not meant to imply any standard analysis presentation
format or analysis method and are included to provide examples of ADaM analysis results metadata. In summary,
these examples are not meant to make recommendations as to the use of these endpoints, the methods for the
endpoints, nor the exact statistical methodology. It is important that each study be evaluated individually and that
current ADaM documentation is referenced in order to accurately and robustly design ADaM datasets.
This supplement is not intended to illustrate every possible variable that could or should be included in analysis
datasets created for statistical analysis of diabetes endpoints, but rather is intended to be descriptive and illustrative
of the use of the ADaM model. Therefore, all examples of analysis datasets are abbreviated in nature. The examples
should not be interpreted as requirements for the statistical analysis of diabetes data. Additionally, the metadata and
derivations presented are for illustrative purposes only and are not meant to imply a universally accepted definition
or derivation of the variables.
Please refer to Version 2.1 of ADaM and Version 1.0 of the Analysis Data Model Implementation Guide (ADaMIG)
for required background about the ADaM and the ADaM data structures.
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2 Subject-Level Analysis Data: ADSL
The ADSL dataset structure for an individual trial has one record per subject and contains required variables (as specified in the ADaMIG) plus other subjectlevel variables that are important in describing a subject or the subject’s experience in the trial. Examples of typical ADSL variables include population flags,
planned and actual treatment, demographic information, randomization factors, subgrouping variables, baseline values of important measures, and important
dates. ADSL variables that describe subject characteristics or disease state are often the means to creating important subpopulations. When creating new
variables for these types of data, variable name and naming fragments should be chosen to represent the content of variables, as opposed to meaningless names
such as VAR01, VAR02, etc.
Before illustrating an ADSL specific to a diabetes trial, this supplement presents a proposal for the management of stratification variables.
2.1 Stratification Variables
This supplement presents a proposal for the representation of the description and the associated values for stratification factors used during randomization and
treatment assignment. Before describing the proposed variables, the following brief summary of issues related to stratified randomization is provided:
Stratified randomization is used to ensure balance of treatment assignments across one or more prognostic factors. A prognostic factor is an aspect of the disease
or a characteristic of the subject that may influence treatment response. The prognostic factors used to stratify the randomization are specified in the protocol. As
a simple example, suppose age group (<50, >=50) and gender (male, female) are considered important prognostic factors. When a subject is deemed eligible for
randomization, their individual values of these factors are determined at the site and used as input to the randomization process to determine their treatment
assignment. The situation may occur where the value of a factor that was used for randomization was later discovered to be in error. For example, suppose a
subject was randomized according to the age group of <50 and male. Later it was discovered that the subject was actually 54 and therefore should have been
randomized according to the age group of >=50 and male. If this situation happens too often, the balance in treatment assignments across these factors is in
question which may then result in the use of sensitivity analyses. Therefore, there is an analysis need to have two sets of values to describe the stratification
factors. In this document, these two sets of values are referred to the “as randomized” values and the “as verified” values.
At present, there is not a standard method for representing the randomization strata factors and values in SDTM-based datasets. Depending on the randomization
process, it might be unnecessary to represent variables and values specific to stratification in SDTM-based datasets if the information can be found within an
appropriate domain. For example, if age and sex were used as stratification factors, then the DM variables AGE and SEX should appropriately reflect values used
for randomization. However, more sophisticated randomizations or more complicated derivations of prognostic factors, such as whether a subject had ever used a
particular concomitant medication for a given length of time, may be harder to identify or document in SDTM-based datasets. If using an Interactive Voice
Randomization System (IVRS), the values used for randomization would be captured by the system and would correspond to the values that are represented on
the randomization schedule. The “as verified” values are typically derived by comparing the values used for randomization against the data that is in SDTM,
whether it be a simple match with a single data point such as sex or the reprogramming of more complex factors such as previous treatments.
Table 2.1.1 provides the full set of proposed variables to allow maximum flexibility in representing the description of the prognostic factors, as well as the values
used for randomization and the values that were verified. These variables would be found in ADSL. Some metadata have been omitted from the table, either
because they are at the sponsor’s discretion (such as the source and manner in which they are derived) or simply to leave more space for the variable descriptions
and examples provided in the “CDISC Notes” column. To better illustrate the interrelationships of the variables, the examples are all based on the same
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hypothetical situation, in which a trial used three stratification factors of Age Group (“<50” or “ >=50”), Prior Treatment Status (“Treatment naïve”, “Treatment
experienced”), and Hypertension (“Y” or “N”). Section 3.1 has another example that is specific to the full Diabetes ADSL example.
Table 2.1.1: Proposed Stratification Variables
Variable Name Variable Label
STRATA
Randomized
Strata
STRATAN
Randomized
Strata (N)
STRATyNM
Description of
Stratum y
STRATy
Randomized
Value of Stratum
y
Randomized
Value of Stratum
y (N)
STRATyN
Type Core
CDISC Notes
text
Cond This entire string value represents the combination of values of the individual stratification factors used for
randomization.
This variable is conditional based on whether the trial used stratified randomization.
For example, “>=50, Treatment experienced, N”
integer Perm This is a numeric variable that corresponds to each unique value of STRATA. There must be a 1:1
correspondence between STRATA and STRATAN.
For example, STRATAN= “3” when STRATA = “>=50, Treatment experienced, N”
text
Perm This is a full text description of the stratification factor “y”. This text description will remain constant for all
subjects. These descriptive variables are included to quickly and clearly communicate critical study design
information as well as to facilitate integration. This strategy is consistent with other ADaM variables such as
CRIT1.
For example, STRAT3NM= “Hypertension”
text
Perm This is the subject-level value of the “y’th” stratification factor and the value used for randomization.
For example, STRAT3= “N”
integer Perm This is a numeric variable that corresponds to each unique value of STRATy. There must be a 1:1
STRATAV
Verified Strata
text
Cond
STRATAVN
Verified Strata (N) integer Cond
STRATyV
Verified Value of
Stratum y
text
STRATyVN
Verified Value of
Stratum y (N)
integer Perm
Perm
correspondence between STRATy and STRATyN.
For example, STRAT3N=0 when STRAT3= “N”
This entire string value represents the combination of values of the individual stratification factors that should
have been used and were verified after randomization. If the values used for the randomization of a given
subject were all correct, then STRATAV will equal STRATA. Otherwise, one or more components of the text
string for STRATA and STRATV will be different.
This variable is conditional based on whether the trial used stratified randomization and whether differences
between the “as randomized” and “as verified” values are important for sensitivity analysis.
For example, “>=50, Treatment experienced, Y”
This is a numeric variable that corresponds to each unique value of STRATAV. There must be a 1:1
correspondence between STRATAV and STRATAVN.
For example, STRATAVN=3 when STRATAV = “>=50, Treatment experienced, N”
This is the “as verified” subject-level value of the “y’th” stratification factor. If the value used for
randomization was correct, then STRATyV will equal STRATy.
For example, STRAT3V= “Y”
This is a numeric variable that corresponds to each unique value of STRATyV. There must be a 1:1
correspondence between STRATyV and STRATyVN.
For example, STRAT3VN=1 when STRAT3V= “Y”
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2.2 ADSL Example
The metadata tables below provide an example of an abbreviated ADSL dataset. Variables that would commonly occur in ADSL regardless of therapeutic area,
such as sex, race, age, age groups, geographic region, population flags, treatment assignments, treatment start and stop date, etc. are not shown. The variables
presented below are those that may be of specific interest to the analysis of diabetes trial data. Flag variables indicating background medical history events of
interest and baseline efficacy laboratory variables are considered optional variables, and only a few have been selected for reference.
Because the stratification variables are newly proposed, the example below demonstrates the creation of all of the stratification variables for a hypothetical phase
III parallel group design that used two stratification factors at randomization: 1) baseline HbA1c (>7-<9%, ≥9%), and 2) background use of metformin in
combination with insulin or metformin alone. Additional details regarding these medications such as brand, formulation, dose, etc., would be included in the
medication datasets and not in ADSL unless considered critical for the creation of important patient subgroups. In this example, Subject 001 was randomized into
the “>7-<9%” stratum, although the qualifying HbA1c value was 9.3%. The “as randomized” variables remain as “>7-<9%”, while the verified variables are
updated to reflect the “≥9%” stratum. Subject 002 was randomized correctly, and therefore has “as randomized” and “verified” strata variables that match.
The following tables provide examples of an ADSL analysis dataset (Table 2.2.1), ADSL dataset metadata (Table 2.2.2), and ADSL variable metadata (Table
2.2.3). The source derivation metadata for the variables are provided for illustrative purposes and not intended to represent standard derivation logic. Within the
Source/Derivation column is additional text that is meant to provide further discussion for the variable and would not be present in an actual define.xml
document.
Table 2.2.1: ADSL Analysis Dataset
Row STUDYID
USUBJID
STRATA
STRATAN
STRAT1NM
STRAT1 STRAT1N
1
XYZ
XYZ-001-001 >7-<9% | Metformin alone
1
HbA1c at baseline >7-<9%
0
2
XYZ
XYZ-001-002 >=9% | Metformin + insulin
4
HbA1c at baseline
1
>=9%
STRAT2NM
STRAT2
STRAT2N
Background Diabetes
Metformin alone
0
Medication at Baseline
Background Diabetes
Metformin + insulin
1
Medication at Baseline
Row
STRATAV
STRAT1V STRAT1VN
STRAT2V
STRAT2V HBA1CBL HBA1CGR1
DIABCMBL
DIABDURY EGFRBL HOMAIRBL
>=9%
1
Metformin alone
0
9.3
>=9.0
Metformin alone
4.2
76.3
1.5
1 (cont) >=9% | Metformin alone
>=9%
1
Metformin + insulin
1
9.1
>=9.0
Metformin + insulin
0.5
87.2
2.3
2 (cont) >=9% | Metformin + insulin
Row CPEPTBL RETINOFL NEPHROFL HEIGHTBL WEIGHTBL BMIBL BMIGR1
2.3
Y
157
59.0
23.9
<25
1 (cont)
1.2
Y
Y
180
102.4
31.6
>=30
2 (cont)
Table 2.2.2: ADSL Dataset Metadata
Purpose
Dataset Name
Description
Class
Structure
Keys
Location Documentation
ADSL
Subject-level Analysis SUBJECT-LEVEL ANALYSIS DATASET One record per subject Analysis STUDYID, USUBJID ADSL.xpt ADSL.SAS
Table 2.2.3: ADSL Variable Metadata
Variable
Name
STUDYID
Variable Label
Study Identifier
Type
text
Length/Display
Format
$15
Codelist/Controlled Terms
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Source/Derivation/Comment
DM.STUDYID
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Variable
Name
USUBJID
Unique Subject
Identifier
Randomized
Strata
text
Length/Display
Format
$15
text
$30
Randomized
Strata (N)
integer 1
1; 2; 3; 4
STRAT1NM Description of
Stratum 1
text
$20
HbA1c at Baseline
STRAT1
Randomized
Value of Stratum
1
STRAT1N
Randomized
Value of Stratum
1 (N)
STRAT2NM Description of
Stratum 2
text
$6
>7-<9%; >=9%
STRAT2
STRATA
STRATAN
STRAT2N
STRATAV
Variable Label
Randomized
Value of Stratum
2
Randomized
Value of Stratum
2 (N)
Verified Strata
STRATAVN Verified Strata
(N)
Type
Codelist/Controlled Terms
Source/Derivation/Comment
DM.USUBJID
>7-<9% | Metformin alone;
>=9% | Metformin alone;
>7-<9% | Metformin + insulin;
>=9% | Metformin + insulin
Obtained from QVAL in SUPPDM where QNAM = “STRATA”
Note: At present there is not a standard approach for capturing stratification
factors in SDTM-based datasets. This variable represents the combination of
individual stratum values used for randomization. The above text is an example
and uses a pipe (|) as a delimiter between individual stratum values. These data
could come from other sources as well depending on methodologies used for the
design and the management of the randomization schedule.
= 1 when ADSL.STRATA = “>7-<9% | Metformin alone”;
= 2 when ADSL.STRATA = “>=9% | Metformin alone”;
= 3 when ADSL.STRATA = “>7-<9% | Metformin + insulin”;
= 4 when ADSL.STRATA = “>=9% | Metformin + insulin”
Assigned based on stratification factors defined a given trial. The value is the
same across all subjects and is intended to provide a full text description of the
first stratification factor.
Derived from ADSL.STRATA and is the text string up to the first delimiter of
“,”.
integer 1
0; 1
= 0 when ADSL.STRAT1 = “>7-<9%”;
= 1 when ADSL.STRAT1 = “>=9%”
text
$50
Background Diabetes
Medication at Baseline
text
$20
Metformin alone;
Metformin + insulin
Assigned based on stratification factors defined for a given trial. The value is the
same across all subjects and is intended to provide a full text description of the
second stratification factor.
Derived from ADSL.STRATA and is the text string after the first delimiter of
“,”.
integer 1
0; 1
= 0 when ADSL.STRAT2 = “Metformin alone”;
= 1 when ADSL.STRAT2 = “Metformin + insulin”;
text
>7-<9% | Metformin alone;
>=9% | Metformin alone;
>7-<9% | Metformin + insulin;
>=9% | Metformin + insulin
Obtained from QVAL in SUPPDM where QNAM = “STRATAV”
Note: There is no standard for if and how the updated STRAT--V are captured
and recorded. This is an example of one method and it implies that the full-text
string of the concatenated stratum variables has been recorded. These data
could come from other sources, such as a programmatic check of CM and LB
domains, and will depend on the trial design.
= 1 when ADSL.STRATAV = “>7-<9% | Metformin alone”;
= 2 when ADSL.STRATAV = “>=9% | Metformin alone”;
= 3 when ADSL.STRATAV= “>7-<9% | Metformin + insulin”;
= 4 when ADSL.STRATAV = “>=9% | Metformin + insulin”
$30
integer 1
1; 2; 3; 4
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Variable
Name
STRAT1V
Variable Label
Type
Length/Display
Codelist/Controlled Terms
Format
$6
>7-<9%; >=9%
Verified Value of
Stratum 1
STRAT1VN Verified Value of
Stratum 1 (N)
STRAT2V
Verified Value of
Stratum 2
STRAT2VN Verified Value of
Stratum 2 (N)
HBA1CBL
HbA1c at
Baseline (%)
text
float
8.1
HBA1CGR1 HbA1c at
Baseline (%)
Group 1
text
$10
>7-<9%; >=9%
DIABCMBL Background
Diabetes
Medication
text
$20
Metformin alone; Metformin +
insulin
DIABDURY Duration of
Diabetes (years)
float
8.1
EGFRBL
eGFR MDRD
(ML/MIN/1.73
M**2)
HOMAIRBL Baseline HOMAIR
float
8.1
float
6.2
CPEPTBL
float
6.2
RETINOFL
Baseline Cpeptide (ng/mL)
integer 1
0; 1
text
Metformin alone; Metformin +
insulin
0; 1
$20
integer 1
Medical Hx of
text
Diabetic
Retinopathy Flag
NEPHROFL Medical Hx of
text
Diabetic
Nephropathy Flag
$1
Y
$1
Y
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Provisional
Source/Derivation/Comment
Derived from ADSL.STRATAV and is the text string up to the first delimiter of
“,”.
= 0 when ADSL.STRAT1 = “>7-<9%”;
= 1 when ADSL.STRAT1 = “>=9%”
Derived from ADSL.STRATAV and is the text string after the first delimiter of
“,”.
= 0 when ADSL.STRAT2 = “Metformin alone”;
= 1 when ADSL.STRAT2 = “Metformin + insulin”;
Value of LB.LBSTRESN where LB.LBTESTCD = “HBA1C” and LB.LBDTC
is the closest prior date to DM.RFSTDTC. This represents the value collected
just prior to randomization and the value that should have been used to
determine the stratification used for randomization.
Categorization of ADSL.HBA1CBL
Note: When the accurate value of HbA1c group was used for randomization, this
variable will duplicate the information found in STRAT1V and in STRAT1 and
the subject was randomized correctly. However in this example, it is considered
helpful to have as a separate variable with an explicit variable label.
= “Metformin + insulin” if CM.CMCAT = “DIABETES” and CM.CMTRT =
“INSULIN” and CMSTDTC is before or on the first dose date
(ADSL.TRTSDT) = “Metformin alone” otherwise
This represents the updated stratification value.
Note: See note above for HBA1CBL. This variable is similar, yet captures the
information pertaining to background medication in a separate variable.
Difference between ADSL.SCRSDT (screening date) and MH.MHSTDTC
where MH.MHTERM = “DIABETES”. See SAP for details regarding
imputation of partial dates.
Value of LB.LBSTRESN where LB.LBTESTCD = “EGRFL” and LB.LBDTC
is the closest prior date to DM.RFSTDTC. This represents the value collected
just prior to dosing.
Value of LB.LBSTRESN where LB.LBTESTCD = “HOMAIR” and
LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value
collected just prior to dosing.
Value of LB.LBSTRESN where LB.LBTESTCD = “CPEPTIDE” and
LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value
collected just prior to dosing.
= “Y” where MH.MHTERM = 'DIABETIC RETINOPATHY' and MHOCCUR
= “Y”
= “Y” where MH.MHTERM = “NEPHROPATHY” and MHOCCUR = “Y”
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Variable
Name
HEIGHTBL
Length/Display
Codelist/Controlled Terms
Format
Height at Baseline integer 8
(cm)
WEIGHTBL Weight at
float
8.1
Baseline (kg)
BMIBL
Body Mass Index float
8.1
at Baseline
(kg/m2)
BMIGR1
BMI Group 1
text
$10
<25; >=25 - <30; >=30
Variable Label
Type
Source/Derivation/Comment
The last available of VS.VSSTRESN for VS.VSTESTCD = “HEIGHT” before
or on the first dose date (ADSL.TRTSDT)
The last available of VS.VSSTRESN for VS.VSTESTCD = “WEIGHT” before
or on the first dose date (ADSL.TRTSDT)
ADSL.WEIGHTBL /ADSL.HEIGHTBL**2
Categorization of ADSL.BMIBL
3 Analysis of Hypoglycemic Episodes
The examples of hypoglycemic data provided in the following sections are based on methodologies widely used throughout clinical research within diabetes.
Hypoglycemic episodes are mainly self-reported events where the information is gathered in patient diaries. From there, the data are entered into a hypoglycemia
form in the eCRFs. Hypoglycemic events are often summarized and analyzed by American Diabetes Association (ADA) classification (see Seaquist et al1 for
details) and each event is classified based on different kinds of information collected from the hypoglycemia form. When glucose concentrations are measured to
support classification according to the ADA severity classes, they are represented in SDTM-based datasets using the LB domain alongside any planned glucose
measurements (e.g., planned samples evaluated at a central laboratory). It should be noted that measurements of glucose that were collected as part of the
hypoglycemia information will usually only be used for the purpose of classifying hypoglycemia (e.g., according to the ADA criteria).
There are two abbreviated analysis datasets presented below. The first dataset gathers all information related to hypoglycemic events from the relevant SDTM
domains and includes the derived ADA-classification for each event. The second dataset is built from the first dataset and allows for an analysis ready approach
to the summarization of hypoglycemic episodes by classification for each subject.
Note that hypoglycemic episodes are important events for diabetic patients and presentations of analyses of hypoglycemic episodes are often based on the safety
analysis set and actual treatment. However, a reduction in hypoglycemic episodes can also be considered a positive property of an investigational drug; hence the
number of hypoglycemic episodes can also be considered an efficacy endpoint, and summarized by planned treatment for various efficacy populations.
3.1 Hypoglycemic Episodes Analysis Dataset
In the example analysis dataset ADHYPO shown below (Table 3.1.1), each row represents one hypoglycemic episode. This analysis dataset collates onto one
record the pertinent data for each episode that is represented in multiple SDTM domains. Sample data for two subjects are provided, illustrating multiple
hypoglycemic events for each subject.
The MIDS variable from the CE domain identifies the individual hypoglycemic episode. Details on a given event are mapped from the CE domain CE, in line
with the TAUG-Diabetes. A number of variables are ADaM variables that can be transferred directly from ADSL and hence are easily traced back to their
respective domains. Further, a number of analysis variables are derived such as ASTDY, the relative analysis day and the traceability for these variables are
ensured by the metadata shown in Table 3.1.3. Finally, a number of sponsor defined variables are present, such as SELFTRFL (“was the subject able to self-treat
her or himself?” (yes/no)) and LMLRELTM (“last meal relative time”). The first variable mentioned is needed to find the ADA class for each event and the
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second variable is needed in the statistical analysis of the hypoglycemic episodes, since events within 2 hours from the last main meal will be analyzed
separately. The source/derivation metadata provided below serve as an example of typical metadata and should not be interpreted as precise methods for how
these variables should be derived.
Summary tables of hypoglycemic episodes can be produced from ADHYPO. Table 3.2.1 shows a summary example presented by arm, with number and
percentage of subjects experiencing at least one event together with number of events. Table 3.2.2 presents a summary of events by time.
Table 3.1.1: ADHYPO Analysis Dataset
Row STUDYID USUBJID MIDS
CEDECOD WASAEYN
ASTDTM
TRTEMFL SELFTRFL SYMPFL NOCTFL GLUCSTD GLUCCONV ASTDY
1
XYZ
000001
HYPO 1 Hypoglycemia
Y
07Sep2012 22:29:00
Y
N
Y
N
2.8
52
3
2
XYZ
000001
HYPO 2 Hypoglycemia
N
10Sep2012 09:12:00
Y
Y
N
N
2.6
48
6
3
XYZ
000001
HYPO 3 Hypoglycemia
N
10Sep2012 23:05:00
Y
Y
Y
Y
3.3
60
6
4
XYZ
000001
HYPO 4 Hypoglycemia
N
11Sep2012 15:24:00
Y
Y
Y
N
3.9
71
7
5
XYZ
000001
HYPO 5 Hypoglycemia
N
18Sep2012 11:39:00
Y
Y
N
N
3.9
71
14
6
XYZ
000002
HYPO 1 Hypoglycemia
N
22Oct2012 13:28:00
Y
Y
N
N
3.4
62
6
7
XYZ
000002
HYPO 2 Hypoglycemia
N
25Oct2012 13:59:00
Y
Y
Y
N
2.4
45
9
8
XYZ
000002
HYPO 3 Hypoglycemia
N
17Nov2012 05:01:00
Y
N
N
Y
2.8
51
32
Row
LMLRELTM LMLRELTU
1 (cont)
116
Minutes
2 (cont)
57
Minutes
3 (cont)
119
Minutes
4 (cont)
44
Minutes
5 (cont)
192
Minutes
6 (cont)
210
Minutes
7 (cont)
189
Minutes
8 (cont)
91
Minutes
LEXDTM
07Sep2012
20:29:00
10Sep2012
8:12:00
10Sep2012
20:05:00
11Sep2012
14:26:00
18Sep2012
07:29:00
22Oct2012
09:31:00
25Oct2012
10:29:00
17Nov2012
03:25:00
LMLDTM
07Sep2012
20:33:00
10Sep2012
08:15:00
10Sep2012
21:06:00
11Sep2012
14:40:00
18Sep2012
08:27:00
22Oct2012
09:58:00
25Oct2012
10:50:00
17Nov2012
03:30:00
LEXRELTM
LEXRELTU
ASEV
ASEVGR1
120
Minutes
Severe Hypoglycemia
Documented Symptomatic or Severe Hypoglycemia
60
Minutes
Severe Hypoglycemia
Documented Symptomatic or Severe Hypoglycemia
180
Minutes
Severe Hypoglycemia
Documented Symptomatic or Severe Hypoglycemia
58
Minutes
Severe Hypoglycemia
Documented Symptomatic or Severe Hypoglycemia
250
Minutes
Severe Hypoglycemia
Documented Symptomatic or Severe Hypoglycemia
237
Minutes
Pseudo-Hypoglycemia
Asymptomatic Hypoglycemia, Probable
Symptomatic
Hypoglycemia or Pseudo- Hypoglycemia
210
Minutes
Severe Hypoglycemia
Documented Symptomatic or Severe Hypoglycemia
96
Minutes
Severe Hypoglycemia
Documented Symptomatic or Severe Hypoglycemia
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Provisional
TRT
A
Drug
A
Drug
A
Drug
A
Drug
A
Drug
B
Drug
B
Drug
B
Drug
B
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Table 3.1.2: ADHYPO Dataset Metadata
Dataset Name
Dataset Description
Dataset Location
Dataset Structure
Keys
Class
Documentation
ADHYPO
Hypoglycemic Episodes Analysis Dataset adhypo.xpt
One record per subject per event STUDYID, USUBJID, MIDS OCCDS ADHYPO.SAS/SAP
Table 3.1.3: ADHYPO Variable Metadata
Variable
Name
STUDYID
USUBJID
MIDS
CEDECOD
WASAEYN
ASTDTM
ASTDY
TRTEMFL
Variable Label
Study Identifier
Unique Subject
Identifier
Disease Milestone ID
Dictionary-Derived
Term
Was This an Adverse
Event
Analysis Start
Datetime
Analysis Start
Relative Day
Type
text
text
Length/Display
Format
$12
$20
Codelist/Controlled Terms
ADSL.STUDYID
ADSL.USUBJID
text
text
CE.MIDS
CE.CEDECOD
text
FAORRES where FA.MIDS = CE.MIDS and FATESTCD=
“WASAEYN”
Onset of the hypoglycemic episode. Derived based on
CE.CESTDTC.
The number of days from date of first dose until onset of
hypoglycemic episode, derived from ASTDTM and
ADSL.TRTSDT
If ADSL.TRTSDT <= ASTDT <= (ADSL.TRTEDT +1) then
TRTEMFL = “Y”
FAORRES where FA.MIDS = CE.MIDS and FAOBJ =
“HYPOGLYCEMIC EVENT” and FACAT = “TREATMENT
ADMINISTRATION” and FATESTCD = “TXASSIST” and
FATEST ^= “TREATMENT ASSISTANCE”
CEYN where CECAT = “HYPO SYMPTOMS”
integer Datetime.
integer
Treatment Emergent text
Analysis Flag
Subject Able to Treat text
Self Flag
$1
Y; N
$1
Y; N
Symptomatic Event
text
Flag
Nocturnal Event Flag text
$1
Y; N
$1
Y; N
Glucose (mmol/L) at
Time of Event
float
8.1
GLUCCONV Glucose (mg/dl) at
Time of the Event
float
8.1
LMLDTM
integer Datetime.
SELFTRFL
SYMPFL
NOCTFL
GLUCSTD
Last Meal Datetime
Source/Derivation/Comment
LMLRELTM Time Btwn Last Meal integer 4.
and Hypo Onset
LMLRELTU Time Btwn Last Meal text
$7
and Hypo Onset Unit
Minutes
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Provisional
Based on sponsor definition of a nocturnal event (e.g., from
midnight until 6:00 in the morning).
LBSTRESN where LB.MIDS = CE.MIDS AND
LBTESTCD="GLUC" where LBSTRESN is converted to
standard SI units of “mmol/L” if necessary.
LBSTRESN where LB.MIDS = CE.MIDS AND LBTESTCD =
“GLUC” where LBSTRESN is converted to conventional units
of “mg/dL” if necessary.
MLSTDTC where ML.MIDS = CE.MIDS AND RELMIDS =
“LAST MEAL PRIOR TO HYPO” and MLTRT = “MEAL”
Relative time from last meal to onset of hypo (ASTDTMLMLDTM)
Unit of time from last meal to onset of hypo
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Variable
Name
LEXDTM
Variable Label
Last Exposure to
Study Drug Datetime
Length/Display
Format
integer Datetime.
Type
LEXRELTM Time Btwn Last
integer 4.
Exposure and Hypo
Onset
LEXRELTU Time Btwn Last Exp text
$7
and Hypo Onset Unit
ASEV
Analysis
text
$22
Severity/Intensity
ASEVGR1
Pooled Severity
Group 1
text
$45
TRTA
Actual Treatment
text
$32
Codelist/Controlled Terms
Source/Derivation/Comment
EXSTDTC where EX.MIDS=CE.MIDS AND RELMIDS =
“LAST DOSE PRIOR TO HYPO” and EXCAT =
“HIGHLIGHTED DOSE”
Relative time from last exposure to drug to onset of hypo.
(ASTDTM-LEXDTM)
Minutes
Unit of time from last exposure to drug to onset of hypo unit
Severe Hypoglycemia; Documented
Symptomatic Hypoglycemia; Asymptomatic
Hypoglycemia; Probable Symptomatic
Hypoglycemia; Pseudo-Hypoglycemia
Documented Symptomatic or Severe
Hypoglycemia; Asymptomatic
Hypoglycemia, Probable Symptomatic
Hypoglycemia or Pseudo-Hypoglycemia
Based on ADA Classification:
Severe Hypoglycemia/ Documented Symptomatic
Hypoglycemia/ Asymptomatic Hypoglycemia/ Probable
Symptomatic Hypoglycemia/ Pseudo-hypoglycemia
Categorization based on values of ASEV. In this example, the
categories are “Documented Symptomatic or Severe
Hypoglycemia” and “Asymptomatic, Probable Symptomatic
Hypoglycemia, and Pseudo-Hypoglycemia”
ADSL.TRT01A
3.2 Hypoglycemic Episodes Analysis Results
A first presentation of the hypoglycemic episodes will often be a summary table, where the number of total events is presented together with the number and
percentage of subjects with events. An example of the simple summary table is shown in Table 3.2.1, which presents hypoglycemic episodes that occur within
two hours since the last main meal – split by diurnal and nocturnal and by severity. The events can also be summarized by time (using the ADY variable) in the
trial, as shown in Table 3.2.2. For these two table examples, analysis results metadata are not presented.
Table 3.2.1: Summary of Post-Meal Hypoglycemic Episodes by Severity – Table Shell
Hypoglycemic episodes within 2 hours since last meal by severity
Summary – Safety Analysis Set
Drug A
Drug B
N
(%)
E
N
(%)
Number of subjects
Diurnal
Documented Symptomatic
Pseudo Symptomatic
Probable Symptomatic
Nocturnal
Documented Symptomatic
Probable Symptomatic
xxx
xxx
xx
xx
x
x
x
x
(xx.x)
(xx.x)
(xx.x)
(xx.x)
( x.x)
( x.x)
xxx
xx
xx
xx
x
x
xx
xx
xx
xx
x
x
x
xx
E
(xx.x)
(xx.x)
(xx.x)
( x.x)
( x.x)
xxx
xx
xx
x
x
( x.x)
x
N: Number of subjects; %: Percentage of subjects; E: Number of events
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Table 3.2.2: Summary of Hypoglycemic Episodes by Classification and Time – Table Shell
Hypoglycemic Episodes by Classification and Time – Summary – Safety Analysis Set
Drug A
Drug B
Total
N
(%)
E
N
(%)
E
N
(%)
Number of Subjects
Pseudo Symptomatic
Week 1
Week 2
End of treatment
Documented
Symptomatic
Week 1
Week 2
End of treatment
xxx
x
x
x
x
x.x)
x.x)
x.x)
x.x)
x
x
x
x
xxx
x
x
x
x
xx
(xx.x)
xx
x
x
xx
( x.x)
( x.x)
( x.x)
xx
x
xx
(
(
(
(
x.x)
x.x)
x.x)
x.x)
x
x
x
x
xxx
x
x
x
x
xx
(xx.x)
xx
x
x
x
( x.x)
( x.x)
( x.x)
xx
xx
xx
(
(
(
(
(
(
(
(
E
x.x)
x.x)
x.x)
x.x)
x
x
x
x
xx
(xx.x)
xxx
xx
x
xx
( x.x)
( x.x)
( x.x)
xx
xx
xx
N: Number of subjects; %: Percentage of subjects; E: Number of events
3.3 Hypoglycemic Episodes Summary Dataset
The analysis dataset ADHYSUM is built from an ADHYPO data set and supports both the statistical analysis of the hypoglycemic events and the tabular
summary of frequencies of hypoglycemic episodes (see Table 3.3.1). The dataset includes one observation per combination of subject, analysis parameter, time
window and indicator (e.g., treatment emergent flag). Each record is a summary of the type of hypoglycemic episode described by the parameter, per subject. For
each combination of parameter and the timing variable, AVISIT, records are created even if no hypoglycemic episodes occurred. The statistical model presented
below is based on the actual treatment received (TRTA) and adjusted for subject-level values of country and sex. Therefore, these variables are included in
ADHYSUM from ADSL to support analysis readiness. The duration of exposure (TRTDURD) is added to the dataset in order to facilitate exposure adjusted
incidence rates. For overall summaries the records which have “cumulative frequency count” within the text of PARAM and AVISIT = “End of treatment” can
be selected. In this example, parameters for each of the five ADA classification values are defined, along with a derived parameter that represents a grouping of
two of the classification values (documented symptomatic or severe hypoglycemia). Mock data for this summary dataset is provided below in Table 3.3.1, yet
this mock data shows only a subset of the possible values of analysis parameters. The examples below do not attempt to show all the data needed fully visualize
the traceability between ADHYPO and ADHYSUM for a given subject since the volume of required mock data would be large,. In practice, however, the counts
derived in ADHYSUM for a given subject would be completely traceable to the counts of individual rows for that subject found in the source ADHYPO dataset.
Table 3.3.1: ADHYSUM Analysis Dataset
Row STUDYID USUBJID
XYZ
000008
1
XYZ
000008
2
XYZ
000008
3
XYZ
000008
4
XYZ
000008
5
XYZ
000008
6
XYZ
000008
7
XYZ
000008
8
XYZ
000008
10
PARAMCD
ASSYMP
ASSYMPC
ASSYMP
ASSYMPC
ASSYMP
ASSYMPC
ASSYMP
ASSYMPC
ASSYMPC
PARAM
Asymptomatic Hypoglycemia (frequency count)
Asymptomatic Hypoglycemia (cumulative frequency count)
Asymptomatic Hypoglycemia (frequency count)
Asymptomatic Hypoglycemia (cumulative frequency count)
Asymptomatic Hypoglycemia (frequency count)
Asymptomatic Hypoglycemia (cumulative frequency count)
Asymptomatic Hypoglycemia (frequency count)
Asymptomatic Hypoglycemia (cumulative frequency count)
Asymptomatic Hypoglycemia (cumulative frequency count)
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Provisional
AVISIT
AVAL TRTDURD SEX AGE COUNTRY TRTA
Week 1
3
72
F
35
DZA
Drug B
Week 1
3
72
F
35
DZA
Drug B
Week 2
1
72
F
35
DZA
Drug B
Week 2
4
72
F
35
DZA
Drug B
Week 3
0
72
F
35
DZA
Drug B
Week 3
4
72
F
35
DZA
Drug B
Week 4
1
72
F
35
DZA
Drug B
Week 4
5
72
F
35
DZA
Drug B
End of Treatment
7
72
F
35
DZA
Drug B
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Row STUDYID USUBJID PARAMCD
PARAM
AVISIT
AVAL TRTDURD SEX AGE COUNTRY TRTA
…
…
…
…
…
…
…
… …
…
…
…
XYZ
000008 DOCSEVC Documented Symptomatic or Severe Hypoglycemia (cumulative frequency count) End of Treatment 17
72
F
35
DZA
Drug B
20
Table 3.3.2: ADHYSUM Dataset Metadata
Dataset Name
Dataset Description
ADHYSUM
Hypoglycemic Episodes
Summary Data
Dataset Location
Dataset Structure
Keys
ADHYSUM.xpt One record per subject per analysis visit STUDYID, USUBJID, AVISIT,
per parameter
PARAMCD
Class
Documentation
BDS ADHYSUM.SAS/SAP
Table 3.3.3: ADHYSUM Variable Metadata
Variable
Name
STUDYID
USUBJID
Study Identifier
Unique
Subject Identifier
PARAMCD Parameter Code
text
text
Length/Display
Format
$12
$20
text
$8
PARAM
Parameter
text
$80
AVISIT
Analysis Visit
text
$13
Variable Label
AVAL
Analysis Value
TRTDURD Total Treatment
Duration (Days)
SEX
Sex
AGE
Age
COUNTRY Country
TRTA
Actual Treatment
Type
Codelist/Controlled Terms
Source/Derivation/Comment
ADSL.STUDYID
ADSL.USUBJID
integer 8
integer 8
See parameter value metadata.
Note that the tables below do not present all possible values for
PARAMCD but only those that correspond to the data display.
See parameter value metadata.
Note that the tables below do not present all possible values for
PARAM but only those that correspond to the data display.
Refer to Section X.X of the SAP for windowing and imputation
algorithms based on ADHYPO.ADY.
End-of-treatment is defined as the last week during which the subject
is on treatment.
See parameter value metadata.
ADSL.TRTDURD
text
integer
text
text
ADSL.SEX
ADSL.AGE
ADSL.COUNTRY
ADSL.TRT01A
Week -1; Week 0; Week 1; Week
N; End of Treatment
$1
8
$3
$32
Table 3.3.4: ADHYSUM Parameter [CL.PARAM. ADHYSUM]
Permitted Value (Code)
Asymptomatic Hypoglycemia (frequency count)
Asymptomatic Hypoglycemia (cumulative frequency count)
Probable Symptomatic Hypoglycemia (frequency count)
Probable Symptomatic Hypoglycemia (cumulative frequency count)
Pseudo-Hypoglycemia (frequency count)
Pseudo-Hypoglycemia (cumulative frequency count)
Documented Symptomatic (frequency count)
Documented Symptomatic (cumulative frequency count)
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Permitted Value (Code)
Severe Hypoglycemia (frequency count)
Severe Hypoglycemia (cumulative frequency count)
Documented Symptomatic or Severe Hypoglycemia (frequency count)
Documented Symptomatic or Severe Hypoglycemia (cumulative frequency count)
Table 3.3.5: ADHYSUM Parameter Code [CL.PARAMCD. ADHYSUM]
Permitted Value (Code)
ASSYMP
ASSYMPC
PROBAB
PROBABC
PSEUDO
PSEUDOC
DOCUMEN
DOCUMENC
SEVHYPO
SEVHYPOC
DOCSEV
DOCSEVC
Display Value (Decode)
Asymptomatic Hypoglycemia (frequency count)
Asymptomatic Hypoglycemia (cumulative frequency count)
Probable Symptomatic Hypoglycemia (frequency count)
Probable Symptomatic Hypoglycemia (cumulative frequency count)
Pseudo-Hypoglycemia (frequency count)
Pseudo-Hypoglycemia (cumulative frequency count)
Documented Symptomatic (frequency count)
Documented Symptomatic (cumulative frequency count)
Severe Hypoglycemia (frequency count)
Severe Hypoglycemia (cumulative frequency count)
Documented Symptomatic or Severe Hypoglycemia (frequency count)
Documented Symptomatic or Severe Hypoglycemia (cumulative frequency count)
Table 3.3.6: Parameter Value-Level List – ADHYSUM [AVAL]
AVAL
PARAMCD = “ASSYMP”
Length/
Display
Format
integer 3.
AVAL
PARAMCD = “ASSYMPC”
integer 3.
AVAL
PARAMCD = “PROBAB”
integer 3.
AVAL
PARAMCD = “PROBABC”
integer 3.
AVAL
PARAMCD = “ PSEUDO”
integer 3.
AVAL
PARAMCD = “ PSEUDOC”
integer 3.
AVAL
PARAMCD = “DOCUMEN”
integer 3.
Variable
Where
Type
Codelist/
Controlled
Terms
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Provisional
Source/Derivation/Comment
Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by
AVISIT and have a value of ASEV of “Asymptomatic Hypoglycemia”.
Derived: AVAL equals the number of asymptomatic hypoglycemic events that have occurred from
the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all records
in ADHYSUM for a given value of AVISIT where PARAMCD = “ASSYMP”.
Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by
AVISIT and have a value of ASEV of “Probable Symptomatic Hypoglycemia”.
Derived: AVAL equals the number of probable symptomatic hypoglycemic events that have
occurred from the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL
from all records in ADHYSUM for a given value of AVISIT where PARAMCD = “PROBAB”.
Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by
AVISIT and have a value of ASEV of “Pseudo-hypoglycemia”.
Derived: AVAL equals the number of pseudo-symptomatic hypoglycemic events that have occurred
from the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all
records in ADHYSUM for a given value of AVISIT where PARAMCD = “PSEUDO”.
Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by
AVISIT and have a value of ASEV of “Documented Symptomatic Hypoglycemia”.
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
AVAL
Length/
Display
Format
PARAMCD = “DOCUMENC” integer 3.
AVAL
PARAMCD = “ SEVHYPO”
integer 3.
AVAL
PARAMCD = “SEVHYPOC”
integer 3.
AVAL
PARAMCD = “ DOCSEV”
integer 3.
AVAL
PARAMCD = “DOCSEVC”
integer 3.
Variable
Where
Type
Codelist/
Controlled
Terms
Source/Derivation/Comment
Derived: AVAL equals the number of pseudo-symptomatic hypoglycemic events that have occurred
from the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all
records in ADHYSUM for a given value of AVISIT where PARAMCD = “DOCUMEN”.
Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by
AVISIT and have a value of ASEV of “Severe Hypoglycemia”.
Derived: AVAL equals the number of severe hypoglycemic events that have occurred from the
beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all records in
ADHYSUM for a given value of AVISIT where PARAMCD = “SEVHYPO”.
Derived: AVAL equals the number of records in ADHYPO that occur during the period defined by
AVISIT and have a value of ASEVGR1 of “Documented Symptomatic or Severe
Hypoglycemia”.
Derived: AVAL equals the number of records in ADHYPO that occur during the period defined by
AVISIT and have a value of ASEVGR1 of “Documented Symptomatic or Severe Hypoglycemia.”
3.4 Hypoglycemic Episodes Summary Analysis Results
The summary statistics in Table 3.4.1 are presented for all hypoglycemic episodes as well as by ADA classification group. The statistics presented in the current
example are number of subjects experiencing an event, the number of events, and the raw event rate. To estimate and present the event-rate information, exposure
time is needed. Table 3.4.1 is based on the ADHYSUM dataset.
Table 3.4.1: Summary of Hypoglycemic Episodes by Classification – Table Shell
Hypoglycemic Episodes by Classification – Treatment Emergent – Summary – Safety Analysis Set
Drug A
Drug B
Total
N
(%)
E
R
N
(%)
E
R
N
(%)
E
Number of subjects
Total events
ADA
Severe hypoglycemia
Documented symptomatic hypoglycemia
Asymptomatic hypoglycemia
Probable symptomatic hypoglycemia
Pseudo-hypoglycemia
xxx
xx
x
xx
x
x
x
( xx.x) xx
xxx.x
( x.x) x
( xx.x) xx
( x.x) xx
( x.x) x
xx.x
xxx.x
xx.x
x.x
xxx
xx
x
xx
x
x
x
( xx.x) xx
xxx.x
( x.x) x
( xx.x) xx
( x.x) x
( x.x) x
x.x
xxx.x
xx.x
x.x
xxx
xx
x
xx
xx
x
x
R
( xx.x)
xxx
xxx.x
( x.x)
( xx.x)
( x.x)
( x.x)
x
xxx
xx
x
x.x
xxx.x
xx.x
x.x
N: Number of subjects; %: Percentage of subjects; E: Number of events; R: Event rate per 100 exposure years;
Severe: Subject unable to treat himself/herself and/or have a recorded PG < 3.1 mmol/L (56 mg/dL)
Treatment emergent episodes occur after trial product administration after randomization and no later than 1 day after last trial
product administration.
The hypoglycemic episodes can also be summarized by concomitant medication group (e.g., with or without metformin), time since last meal (e.g., within 1 hour
of last meal), or other relevant categorical variables.
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Provisional
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
The event rate over time since randomization for hypoglycemic episodes can be presented graphically by a mean cumulative function plot. In Figure 3.4.1 the
severe and documented symptomatic events are compared between the treatment arms. The figure is created based on the cumulative episodes by subjects over
time, found in the ADHYSUM dataset.
Figure 3.4.1: Mean Cumulative Function Plot of Documented and Severe Symptomatic Hypoglycemic Episodes
Documented and Severe Symptomatic Hypoglycemic Episodes – Treatment Emergent - Mean Cumulative Function - Safety Analysis Set
0.36
0.36
0.32
0.32
0.28
0.28
0.24
0.24
0.20
0.20
0.16
0.16
0.12
0.12
0.08
0.08
0.04
0.04
Number of Episodes per Subject
0.40
0.00
Number of Episodes per Subject
0.40
Drug A
Drug B
0.00
0
4
8
12 16 20 24 28 32 36 40 44 48
Time since Randomisation (Weeks)
52
Different approaches can be followed for the statistical analysis of hypoglycemic episodes. The negative binomial regression, Poisson regression, and several
zero-inflated models are evaluated in Bulsara et al2 and Aschner et al3.
In Table 3.4.2, the documented symptomatic or severe hypoglycemic episodes are modelled by a negative binomial distribution and compared between the
treatment arms. The predicted population mean rates (LSMeans) and the estimated rate ratios between treatment arms are presented. The analysis is based on
ADHYSUM and the result metadata are presented in Table 3.4.3.
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Provisional
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Table 3.4.2: Hypoglycemic Episodes – Full Analysis Set
Hypoglycemic Episodes – Treatment Emergent – Statistical Analysis – Full Analysis Set
FAS
N
Estimate
95% CI
p
Documented Symptomatic or Severe
Hypoglycemic Episodes
LSMeans, Events per 100 PYE
Drug A
Drug B
Treatment Ratio
Drug A / Drug B
197
183
195
183
140.97
171.91
0.82
[ 0.64 ; 1.04]
0.15
N: Number of subjects contributing to analysis; CI: Confidence Interval; PYE: Patient Years Exposure
The number of events is analyzed using a Negative Binomial Regression model using a log link and the
logarithm of the exposure time (100 years) as offset. The model includes treatment and sex as fixed
effects, and age as covariate.
Table 3.4.3: Hypoglycemic Events Analysis Results Metadata
Metadata Field
DISPLAY IDENTIFIER
DISPLAY NAME
RESULT IDENTIFIER
PARAM
PARAMCD
ANALYSIS VARIABLE
ANALYSIS REASON
ANALYSIS DATASET
SELECTION CRITERIA
DOCUMENTATION
Metadata
Table 3.4.2
Statistical analysis by negative binomial model of severe and documented symptomatic hypoglycemic episodes, by ADA classification
Sum of severe and documented symptomatic hypoglycemic events
Documented Symptomatic or Severe Hypoglycemia (cumulative frequency count)
DOCSEVC
AVAL
Confirmatory secondary endpoint, as pre-specified in the protocol
ADHYSUM
FASFL ="Y" and AVISIT =”End of Treatment”
Protocol section x.x: The number of documented symptomatic or severe hypoglycemic episodes will be analyzed based on the Full Analysis Set using
a negative binomial regression model with a log-link function, and the logarithm of the time period in which a hypoglycemic episode is considered
treatment emergent as offset. The model will include treatment and sex as factors and age as covariate.
PROGRAMMING
STATEMENTS
proc genmod data=adhysum; model AVAL = trtp sex age / dist=nb link=log offset=log(trtdurd); run;
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4 Analysis of Glycated Hemoglobin
There are a number of derived statistical endpoints and analysis methods that might be used for the analysis of the continuous clinical endpoint of HbA1c. The
examples below serve to demonstrate the use of the ADaM standard to create an analysis dataset to support two typical endpoints.
This example is based on a Phase III, parallel-group study designed to determine efficacy of Drug A for patients with Type II diabetes. The primary endpoint was
defined as the change in HbA1c from baseline. This was analyzed using observed data with a longitudinal repeated measures analysis, including the fixed
categorical effects of treatment, week, baseline-by-week, and treatment-by-week interaction, as well as the continuous fixed covariate baseline HbA1c. A
secondary endpoint was defined as the proportion of subjects who experienced one or more instances of HbA1c < 7%. This categorical data was analyzed using
chi-square tests with the use of exact tests as appropriate.
The ADaM dataset below demonstrates the use of the Basic Data Structure (BDS) for both endpoints described above using one analysis parameter for the
continuous HbA1c measure. This example also includes the variable DTYPE to show how data for missed visits could be imputed, but it was added for
demonstration purposes only, and was not used in the specified analysis examples.
4.1 HbA1c Analysis Dataset
The following tables provide examples of a BDS-structured dataset (Table 4.1.1), analysis dataset metadata (Table 4.1.2), and analysis variable metadata (Table
4.1.3) for HbA1c analyzed as a continuous variable and separately as a categorical variable. Note that only selected variables have been shown below; individual
trials may require the use of additional or other variables, such as age of onset of diabetes (years) or baseline fasting glucose.
Table 4.1.1: ADHBA1C Analysis Dataset
Row STUDYID USUBJID
PARAM PARAMCD VISIT AVISIT AWTARGET ADY TRTP ITTFL ABLFL BASE AVAL CHG ANL01FL CRIT1 CRIT1FL DTYPE
XYZ
XYZ-001-001 HbA1c (%) HBA1C
Visit 2 Baseline
1
1 Drug A Y
Y
9.2
9.2
Y
<7%
N
1
XYZ
XYZ-001-001 HbA1c (%) HBA1C
Visit 3 Week 4
28
28 Drug A Y
9.2
8.5 -0.7
Y
<7%
N
2
XYZ
XYZ-001-001 HbA1c (%) HBA1C
Visit 4 Week 8
56
56 Drug A Y
9.2
7.3 -1.9
Y
<7%
N
3
XYZ
XYZ-001-001 HbA1c (%) HBA1C
Visit 5 Week 12
84
84 Drug A Y
9.2
6.8 -2.4
Y
<7%
Y
4
XYZ
XYZ-001-001 HbA1c (%) HBA1C
Visit 6 Week 24
168
168 Drug A Y
9.2
6.3 -2.9
Y
<7%
Y
5
XYZ
XYZ-001-002 HbA1c (%) HBA1C
Visit 2 Baseline
1
1 Drug B
Y
Y
8.6
8.6
Y
<7%
N
6
XYZ
XYZ-001-002 HbA1c (%) HBA1C
Visit 3 Week 4
28
28 Drug B
Y
8.6
8.7
0.1
Y
<7%
N
7
XYZ
XYZ-001-002 HbA1c (%) HBA1C
Visit 4 Week 8
56
56 Drug B
Y
8.6
9.6
1.0
Y
<7%
N
8
XYZ
XYZ-001-002 HbA1c (%) HBA1C
Visit 5 Week 8
56
61 Drug B
Y
8.6
9.5
1.1
<7%
N
9
XYZ
XYZ-001-002 HbA1c (%) HBA1C Visit 5.1 Week 12
84
84 Drug B
Y
8.6
9.5
1.1
Y
<7%
N
LOCF
10
XYZ
XYZ-001-002 HbA1c (%) HBA1C
Visit 6 Week 24
168
168 Drug B
Y
8.6
9.5
1.1
Y
<7%
N
LOCF
11
LBSEQ
23456
45325
24768
76553
65678
90874
23454
56744
67543
67543
67543
Table 4.1.2: ADHBA1C Analysis Dataset Metadata
Dataset
Description
ADHBA1C HbA1c Analysis
Data
Class
Basic Data
Structure
Structure
One record per subject per parameter
per analysis visit and day
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Purpose
Keys
Analysis STUDYID, USUBJID,
PARAMCD, AVISIT, ADY
Location
Documentation
ADHBA1C.xpt ADHBA1C.SAS/SAP
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Table 4.1.3: ADHBA1C Analysis Variable Metadata
Variable
Name
STUDYID
USUBJID
Variable Label
Length/Display
Format
Type
Codelist/Controlled Terms
text
text
3
20
PARAM
Study Identifier
Unique Subject
Identifier
Parameter
text
32
HbA1c (%)
PARAMCD
VISIT
Parameter Code
Visit Name
text
text
8
20
AVISIT
Analysis Visit
text
11
HBA1C
Visit 2; Visit 3; Visit 4;
Visit 5; Visit 5.1; Visit 6
Baseline; Week 4; Week 8;
Week 12; Week 24
AWTARGET Analysis Window
Target
ADY
Analysis Relative
Day
TRTP
Planned
Treatment
ITTFL
Intent-To-Treat
Population Flag
ABLFL
Baseline Record
Flag
BASE
Baseline Value
AVAL
Analysis Value
CHG
Change from
Baseline
ANL01FL
Analysis Record
Flag 01
CRIT1
integer 3
integer 3
Populated with ”HbA1c (%)” for records corresponding to HbA1c
(LB.LBTESTCD = “HBA1C”)
Populated with ”HBA1C” (based on LB.LBTESTCD = “HBA1C”)
LB.VISIT
Refer to Section X.X of the SAP for windowing algorithm based on
ADHBA1C.ADY. Baseline visit is defined as the last available value prior to
randomization.
Refer to Section X.X of the SAP for windowing algorithm.
text
15
Drug A; Drug B
Refer to Section X.X of the SAP for windowing algorithm based on
ADHBA1C.ADY.
ADSL.TRT01P
text
1
Y; N
ADSL.ITTFL
text
1
Y
Set to “Y” when HBA1C.AVISIT = “Baseline”. See SAP for visit windowing.
float
float
float
8.1
8.1
8.1
Y
BASE = ADHBA1C.AVAL where ADHBA1C.ABLFL = “Y”
AVAL = LB.LBSTRESN where LB.LBTESTCD =”HBA1C”
CHG = AVAL-BASE
text
1
Y
50
<7%
Populate with “Y” to identify the record selected to be analyzed for the specific
value of AVISIT (already populated based on the analysis window algorithm
defined in SAP X.X).
If there are multiple records for a value of AVISIT, use the record that is closest
to ADHBA1C.AWTARGET.
Populated with ”<7%”
1
Y; N
Set to “Y” when ADHBA1C.AVAL<7%. Set to “N” otherwise.
10
LOCF
Set to “LOCF” when ADHBA1C.AVAL is imputed using last observation
carried forward (post-baseline only).
LB.LBSEQ from the record in the SDTM LB domain containing AVAL.
DTYPE
Analysis Criterion text
1
Criterion 1
text
Evaluation Result
Flag
Derivation Type
text
LBSEQ
Sequence Number integer 4
CRIT1FL
Source/Derivation/Comment
ADSL.STUDYID
ADSL.USUBJID
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4.2 HbA1c Analysis Results
The HbA1c results are presented using a repeated measures model in Section 4.2.1 and a categorical analysis in Section 4.2.2. Within each section, example
output is presented first, followed by the analysis results metadata.
4.2.1 Longitudinal Repeated Measures Model
Table 4.2.1: HbA1c Longitudinal Repeated Measures Analysis - Table Shell
Protocol: XYZ
Page 1 of 2
HbA1c (%) Longitudinal Repeated Measures Analysis
24-Week Short-term Double-blind Treatment Period
Intention-to-treat Population
Drug A
N=125
125
X.XX( X.XXX)
Drug B
N=125
125
X.XX ( X.XXX)
BASELINE
N#
Mean (SD)
WEEK 4
N#
Change from baseline: Mean (SD)
Adjusted change from baseline: Mean (SD)
95% Confidence interval for adjusted mean
Difference vs. Drug B (SE)
95% Confidence interval for difference
P-value vs. Drug B
XXX
X.XX ( X.XXX)
X.XX ( X.XXX)
(XX.XX, XX.X)
XXX
X.XX ( X.XXX)
X.XX ( X.XXX)
(XX.XX, XX.X)
XX.XX ( X.XXXX)
(XX.XX, XX.X)
X.XXXX
N#
Change from baseline: Mean (SD)
Adjusted change from baseline: Mean (SD)
95% Confidence interval for adjusted mean
Difference vs. Drug B (SE)
95% Confidence interval for difference
P-value vs. Drug B
X.XX( X.XXX)
XXX
X.XX ( X.XXX)
X.XX ( X.XXX)
(XX.XX, XX.X)
X.XX ( X.XXX)
XXX
X.XX ( X.XXX)
X.XX ( X.XXX)
(XX.XX, XX.X)
XX.XX ( X.XXXX)
(XX.XX, XX.X)
X.XXXX
...
WEEK 12
N: the number of subjects in the Intention-to-treat (ITT) Population.
N#: the number of subjects in the ITT population with non-missing baseline and non-missing Week t value.
Repeated measures model: change = baseline treatment visit visit*treatment
<date>:<time>
Program Source: xxxxxxxx\xxxx\xxxx\t-hba1c-repmeas.sas
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Figure 4.2.1: Mean Change from Baseline in HbA1c (%) Over Time – Figure Shell
Protocol: XYZ
Page 2 of 2
HbA1c (%) Mean Change from Baseline
Mean Change from Baseline in HbA1c (%) Over Time
24-Week Short-term Double-blind Treatment Period
Intention-to-treat Population
1
0.5
0
-0.5
-1
-1.5
-2
-2.5
-3
Drug A
Drug B
Week 4
325
324
Week 8
325
324
Week 12
322
319
Week 24
320
318
Repeated measures model: change = baseline treatment visit visit*base visit*treatment
Mean changes from baseline are based on adjusted changes from baseline from the repeated measure model.
Program Source: /xxxxx/xxxx/xxxx/t-hba1c-repmeas.sas
<date>:<time>
Table 4.2.2: HbA1c Longitudinal Repeated Measures Analysis Results Metadata
Metadata Field
DISPLAY IDENTIFIER
DISPLAY NAME
RESULT IDENTIFIER
PARAM
PARAMCD
ANALYSIS VARIABLE
ANALYSIS REASON
ANALYSIS PURPOSE
ANALYSIS DATASET
Metadata
Table 4.2.1/Figure 4.2.1
Mean Change from Baseline in HbA1c (Percent) Longitudinal Repeated Measures Analysis, 24-Week Short-term Double-blind Treatment
Period, Intention-to-treat Population
Treatment difference results (LSMean, confidence interval, p-value)
HbA1c (%)
HBA1C
CHG (Change from baseline)
SPECIFIED IN SAP
PRIMARY OUTCOME MEASURE
ADHBA1C
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Metadata Field
SELECTION CRITERIA
DOCUMENTATION
Metadata
ITTFL= “Y” and PARAMCD = “HBA1C” and CHG ne . and ANL01FL = “Y” and DTYPE = “ ”
See SAP Section XX for details. Program: t-hba1c-repmeas.sas LS means and 95% CIs are based on repeated measures model adjusting for
planned treatment, baseline HbA1c value, avisit, avisit*baseline and avisit*treatment interaction.
PROGRAMMING STATEMENTS [SAS version 9.2]
PROC MIXED DATA = ADHBA1C;
WHERE ITTFL = “Y” and PARAMCD = “HBA1C” and CHG ne . and ANL01FL = “Y” and DTYPE = “ ”
CLASS TRTP AVISIT;
MODEL CHG = TRTP BASE AVISIT BASE*AVISIT AVISIT*TRTP / DDFM=KR;
LSMEANS TRTP / CL DIFF; REPEATED usubjid / subject = USUBJID
TYPE=UN;
RUN ;
4.2.2 Categorical Analysis
Table 4.2.3: HbA1c Categorical Analysis Table Shell
Protocol: XYZ
Page 1 of 1
Proportion of Subjects with HbA1C < 7%
Intention-to-treat Population
Drug A
(N=XXX)
Week 4
X/N#
Percent
P-value vs. Drug B
Drug B
(N=XXX)
x/xxx
x.x%
x.xxxx
x/xxx
x/x%
x/xxx
x.x%
x.xxxx
x/xxx
x/x%
...
WEEK 12
X/N#
Percent
P-value vs. Drug B
N: number of subjects in the ITT analysis set;
N#: number of subjects in the ITT analysis set with non-missing baseline and non-missing Week t values;
X: number of subjects with HbA1c <7%.
P-value is based on a chi-square test. In case of less than 5 events per treatment group, the exact method is used.
Program Source: /xxxxx/xxxx/xxxx/t-hba1c-cat.sas
<date>:<time>
Table 4.2.4: HbA1c Categorical Analysis Results Metadata
Metadata Field
DISPLAY IDENTIFIER
DISPLAY NAME
RESULT IDENTIFIER
PARAM
PARAMCD
ANALYSIS VARIABLE
Metadata
Table 4.2.3
Proportion of Subjects with HbA1c <7%, Intention-to-treat Population
Treatment proportions and p-values
HbA1C (%)
HBA1C
CRIT1
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Metadata Field
ANALYSIS REASON
ANALYSIS PURPOSE
ANALYSIS DATASET
SELECTION CRITERIA
DOCUMENTATION
Metadata
SPECIFIED IN SAP
SECONDARY OUTCOME MEASURE
ADHBA1C
PARAMCD = “HBA1C” and ITTFL = “Y” and ANL01FL = “Y” and CHG ne . and DTYPE = “ ”
See SAP Section XX for details. Program: t-hba1c-cat.sas. P-value based on chi-square test. In case of <5 events per treatment group, the exact
method is used. No imputation method is applied and only subjects with baseline and week t data are included.
PROGRAMMING
STATEMENTS
[SAS version 9.2]
PROC FREQ DATA = ADHBA1C;
WHERE PARAMCD = “HBA1C” and ITTFL = “Y” and ANL01FL =”Y” and CHG ne . and DTYPE = “ ”;
BY AVISIT;
TABLES TRTP*CRIT1FL/ CHISQ EXACT;
RUN;
5 Analysis of Glucose Levels
It is important in the treatment of diabetes that glucose levels remain controlled both in the short term and long term. Several methodologies can be used to
collect and analyze glucose data in order to measure this level of control, such as laboratory fasting glucose, self-monitored blood glucose, and glucose tolerance
testing.
Fasting glucose is measured in a laboratory setting at regular clinical visits over time. Self-monitored blood glucose (SMBG) is measured by the patient multiple
times during the course of normal daily activities using a device such as a glucose meter, and data are recorded by the device or manually entered into a diary.
SMBG can also sometimes be referred to as SMPG (self-monitoring plasma glucose) if the glucose meters reports blood glucose values as plasma glucose. For
the purposes of the remainder of this document, the terms SMBG and SMPG are treated as interchangeable. Glucose tolerance testing (e.g., oral glucose
tolerance test (OGTT), intravenous glucose tolerance test (IVGTT), mixed-meal tolerance test (MMTT)) can also be used to measure glucose control in
conjunction with a meal or other glucose source.
Another analysis concept described in this section is glucose excursion, or the change in glucose from a constant time point to another targeted time point (e.g.,
from 0 to 180 minutes, from 0 to 240 minutes).
5.1 Self-Monitored Glucose Profile Analysis Dataset
This example is based on a Phase III, parallel-group study designed to determine efficacy of Drug A for patients with Type II diabetes. While the choice of model
may vary, for this example, the SMBG data are analyzed using a longitudinal repeated measures analysis including the fixed categorical effects of treatment,
week and treatment-by-week interaction as well as the continuous fixed covariate baseline.
The following tables provide examples of a Basic Data Structure (BDS) analysis dataset, and analysis-variable metadata for glucose analyzed as a continuous
variable. Note that some important variables may not be presented, as only selected variables were chosen to focus on the most important concepts.
First, consider the simplified data in the SDTM LB dataset below. It shows two visits with complete 9-point SMBG in a 24-hour period. In a more realistic case
there would be more than one day of measurements per visit.
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For analysis, a single representative visit value was derived for each of the 9 time points using some algorithm (typically a mean value).
Table 5.1.1: Glucose excerpt from SDTM LB domain corresponding to SMBG readings
Row STUDYID DOMAIN USUBJID LBSEQ LBTESTCD LBTEST
LBCAT
LBSTRESN LBSTRESU
XYZ
LB
XYZ-1-002
1
GLUC
Glucose CHEMISTRY
6.8
mmol/L
1
XYZ
LB
XYZ-1-002
2
GLUC
Glucose CHEMISTRY
8.9
mmol/L
2
XYZ
LB
XYZ-1-002
3
GLUC
Glucose CHEMISTRY
7.3
mmol/L
3
XYZ
LB
XYZ-1-002
4
GLUC
Glucose CHEMISTRY
8.5
mmol/L
4
XYZ
LB
XYZ-1-002
5
GLUC
Glucose CHEMISTRY
7.9
mmol/L
5
XYZ
LB
XYZ-1-002
6
GLUC
Glucose CHEMISTRY
8.1
mmol/L
6
XYZ
LB
XYZ-1-002
7
GLUC
Glucose CHEMISTRY
19.1
mmol/L
7
XYZ
LB
XYZ-1-002
8
GLUC
Glucose CHEMISTRY
6.4
mmol/L
8
XYZ
LB
XYZ-1-002
9
GLUC
Glucose CHEMISTRY
6.6
mmol/L
9
XYZ
LB
XYZ-1-002
10
GLUC
Glucose CHEMISTRY
6.5
mmol/L
10
11
XYZ
LB
XYZ-1-002
11
GLUC
Glucose
CHEMISTRY
12
13
14
15
16
17
18
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
LB
LB
LB
LB
LB
LB
LB
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
12
13
14
15
16
17
18
GLUC
GLUC
GLUC
GLUC
GLUC
GLUC
GLUC
Glucose
Glucose
Glucose
Glucose
Glucose
Glucose
Glucose
CHEMISTRY
CHEMISTRY
CHEMISTRY
CHEMISTRY
CHEMISTRY
CHEMISTRY
CHEMISTRY
Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)
8 (cont)
9 (cont)
10 (cont)
11 (cont)
12 (cont)
13 (cont)
14 (cont)
15 (cont)
16 (cont)
17 (cont)
18 (cont)
LBSTAT
NOT
DONE
6.3
9.1
13.8
14.1
7.1
5.4
5.3
mmol/L
mmol/L
mmol/L
mmol/L
mmol/L
mmol/L
mmol/L
LBSPEC LBBLFL LBFAST VISITNUM VISIT
PLASMA
Y
Y
2
VISIT2
PLASMA
Y
2
VISIT2
PLASMA
Y
2
VISIT2
PLASMA
Y
2
VISIT2
PLASMA
Y
2
VISIT2
PLASMA
Y
2
VISIT2
PLASMA
Y
2
VISIT2
PLASMA
Y
2
VISIT2
PLASMA
Y
Y
2
VISIT2
PLASMA
Y
4
VISIT4
PLASMA
4
VISIT4
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
4
4
4
4
4
4
4
VISIT4
VISIT4
VISIT4
VISIT4
VISIT4
VISIT4
VISIT4
Y
LBDTC
LBTPT
LBTPTNUM
2015-01-19T06:00 PRE-BREAKFAST
1
2015-01-19T08:00 POST-BREAKFAST
2
2015-01-19T11:00
PRE-LUNCH
3
2015-01-19T13:00
POST-LUNCH
4
2015-01-19T18:00
PRE-DINNER
5
2015-01-19T19:00
POST-DINNER
6
2015-01-19T21:00
BEDTIME
7
2015-01-20T03:00
0:300 AM
8
2015-01-20T06:00 PRE-BREAKFAST
9
2015-02-16T06:00 PRE-BREAKFAST
1
2015-02-16T08:00 POST-BREAKFAST
2
2015-02-16T12:00
PRE-LUNCH
3
2015-02-16T13:00
POST-LUNCH
4
2015-02-16T18:00
PRE-DINNER
5
2015-02-16T19:00
POST-DINNER
6
2015-02-16T21:00
BEDTIME
7
2015-02-17T03:00
0:300 AM
8
2015-02-17T06:00 PRE-BREAKFAST
9
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Parameter/time-point combinations were created for each time point within a visit.
Rows 1-18:
Show glucose measured in mmol/L as indicated by the parameter. The parameter code is set to GLUCSTD, and the variable BASETYPE (not
shown) will match the values of ATPT. Please note: BASETYPE is added for current conformance to the ADaM Implementation Guide. This use
is currently being discussed as a topic for possible enhancement in a future ADaM Implementation Guide.
Rows 19-20: Show an example of glucose excursion from pre-breakfast to bedtime.
Row 21-22: Show an example of a 24-hour glucose average.
Table 5.1.2: ADSMBG Analysis Dataset
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
USUBJID
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
XYZ-1-002
19
XYZ-1-002
20
XYZ-1-002
21
22
XYZ-1-002
XYZ-1-002
PARAM
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose Excursion
Pre-Breakfast to Bedtime (mmol/L)
Glucose Excursion
Pre-Breakfast to Bedtime (mmol/L)
Glucose 24-Hour Average (mmol/L)
Glucose 24-Hour Average (mmol/L)
PARAMCD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
AVISIT
Week 2
Week 2
Week 2
Week 2
Week 2
Week 2
Week 2
Week 2
Week 2
Week 4
Week 4
Week 4
Week 4
Week 4
Week 4
Week 4
Week 4
Week 4
TRTP ITTFL ABLFL BASE AVAL CHG ANL01FL
ATPT
LBSEQ
Drug A
Y
Y
6.8
6.8
Y
Pre-Breakfast
1
Drug A
Y
Y
8.9
8.9
Y
Post-Breakfast
2
Drug A
Y
Y
7.3
7.3
Y
Pre-Lunch
3
Drug A
Y
Y
8.5
8.5
Y
Post-Lunch
4
Drug A
Y
Y
7.9
7.9
Y
Pre-Dinner
5
Drug A
Y
Y
8.1
8.1
Y
Post-Dinner
6
Drug A
Y
Y
19.1
19.1
Y
Bedtime
7
Drug A
Y
Y
6.4
6.4
Y
0:300 AM
8
Drug A
Y
Y
6.6
6.6
Y
Pre-Breakfast Next day
9
Drug A
Y
6.8
6.5
-0.3
Y
Pre-Breakfast
10
Drug A
Y
8.9
Post-Breakfast
11
Drug A
Y
7.3
6.3
-1.0
Y
Pre-Lunch
12
Drug A
Y
8.5
9.1
0.6
Y
Post-Lunch
13
Drug A
Y
7.9
13.8
5.9
Y
Pre-Dinner
14
Drug A
Y
8.1
14.1
6.0
Y
Post-Dinner
15
Drug A
Y
19.1
7.1
-12.0
Y
Bedtime
16
Drug A
Y
6.4
5.3
-1.2
Y
0:300 AM
17
Drug A
Y
6.6
6.3
-0.2
Y
Pre-Breakfast Next Day
18
GLUXFBM
Week 2 Drug A
Y
GLUXFBM
Week 4 Drug A
Y
GLUCAVM Week 2 Drug A
GLUCAVM Week 4 Drug A
Y
Y
Y
12.3
12.3
12.3
0.6
-11.7
Y
9.13
9.13
8.9
-.23
Y
Y
Y
Y
Table 5.1.3: ADSMBG Dataset Metadata
Dataset
Description
ADSMBG SMBG Analysis
Data
Class
Basic Data
Structure
Structure
One record per subject per
parameter per visit
Purpose
Keys
Analysis STUDYID, USUBJID, PARAMCD,
ATPT, AVISIT
Location
Documentation
ADSMBG.xpt ADSMBG.SAS/SAP
Table 5.1.4: ADSMBG Variable Metadata
Variable
Name
USUBJID
PARAM
Variable Label
Unique Subject Identifier
Parameter
Type
text
text
Length/Display
Format
$20
$60
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Provisional
Codelist/Controlled
Terms
Source/Derivation/Comment
ADSL.USUBJID
See parameter-value metadata.
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Variable
Variable Label
Name
PARAMCD Parameter Code
AVISIT
Analysis Visit
text
text
ATPT
STUDYID
TRTP
ITTFL
AVAL
BASE
text
text
text
text
float
float
Length/Display
Codelist/Controlled
Format
Terms
$8
$11
Week 2; Week 4; Week 8; Week 12;
Week 24, …
$20
$12
$15
Drug A; Placebo
$1
Y; N
8.1
8.1
BASETYPE Baseline Type
text
$20
CHG
ABLFL
Change from Baseline
Baseline Record Flag
float
text
8.1
$1
LBSEQ
Sequence Number
integer 4.0
ANL01FL
Analysis Record Flag 1
text
Type
Analysis Timepoint
Study Identifier
Planned Treatment
Intent-To-Treat Population Flag
Analysis Value
Baseline Value
$1
Pre-Breakfast; Post-Breakfast; PreLunch; Post-Lunch; Pre-Dinner; PostDinner; …
Y
Y
Source/Derivation/Comment
See parameter-value metadata.
Set to the corresponding visit from SDTM.
See parameter-value metadata.
ADSL.STUDYID
ADSL.TRT01P
ADSL.ITTFL
See parameter-value metadata.
BASE = AVAL where ABLFL = “Y” for each corresponding
timepoint
See parameter-value metadata.
Set to AVAL-BASE for each corresponding timepoint.
Set to “Y” when AVISIT = “Baseline”. See SAP for visit
windowing.
LB.LBSEQ from the record in the SDTM LB domain
containing the result copied to AVAL.
Set to “Y” on records intended for MMRM analysis, where
AVAL is not null.
Table 5.1.5: ADSMBG Parameter [CL.PARAM.ADSMBG]
Permitted Value (code)
Glucose (mmol/L)
Glucose Excursion Pre-Breakfast to Bedtime (mmol/L)
Glucose 24-Hour Average (mmol/L)
Table 5.1.6: ADSMBG Parameter Code [CL.PARAMCD.ADSMBG]
Permitted Value (Code)
GLUCSTD
GLUXFBM
GLUCAVM
Display Value (Decode)
Glucose (mmol/L)
Glucose Excursion Pre-Breakfast to Bedtime (mmol/L)
Glucose 24-Hour Average (mmol/L)
Table 5.1.7: Parameter Value-Level List – ADSMBG [AVAL]
Variable
AVAL
AVAL
Where
PARAMCD =
“GLUCSTD”
PARAMCD =
“GLUXFBM”
Type
float
float
Length/Display Codelist/Controlled
Source/Derivation/Comment
Format
Terms
8.1
Derived: AVAL = LB.LBSTRESN where LB.LBTESTCD =”GLUC” at the corresponding
LBTPT to each ATPT
8.1
Derived: Set AVAL to the difference between AVAL at Bedtime minus AVAL at PreBreakfast of the same day for PARAMCD = “GLUCSTD”.
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Variable
AVAL
Where
PARAMCD =
“GLUCAVM”
Type
float
Length/Display Codelist/Controlled
Source/Derivation/Comment
Format
Terms
8.1
Derived: Set AVAL to the average of glucose at all 9 timepoints during the 24 hour period
from pre-breakfast to pre-breakfast next day
Table 5.1.8: Parameter Value-Level List – ADSMBG [ATPT]
Variable
Where
Length/Display
Format
$23.
Type
ATPT
PARAMCD = “GLUCSTD”
text
ATPT
ATPT
PARAMCD = “GLUXFBM” text
PARAMCD = “GLUCAVM” text
$23.
$23.
Codelist/Controlled Terms
Source/Derivation/Comment
Pre-Breakfast; Post-Breakfast; Pre-Lunch; Post-Lunch; PreDerived: Set to the corresponding value
Dinner; Post-Dinner Bedtime; 0:300 AM; Pre-Breakfast Next Day of SDTM LB.LBTPT
Derived: Set to blank
Derived: Set to blank
Table 5.1.9: Parameter Value-Level List – ADSMBG [BASETYPE]
Variable
Where
BASETYPE PARAMCD = “GLUCSTD”
Type
text
BASETYPE PARAMCD = “GLUXFBM” text
BASETYPE PARAMCD = “GLUCAVM” text
Length/Display
Codelist/Controlled Terms
Format
$23.
Pre-Breakfast; Post-Breakfast; Pre-Lunch; Post-Lunch; Pre-Dinner;
Post-Dinner Bedtime; 0:300 AM; Pre-Breakfast Next Day
$9
“EXCURSION”
$7
“AVERAGE”
Source/Derivation/Comment
Derived: Set to match the ATPT
Derived: Set to “EXCURSION”
Derived: Set to “AVERAGE”
5.2 Self-Monitored Glucose Analysis Results
5.2.1 Longitudinal Repeated Measures Model
The table shell below represents a family of SMBG analyses where <Timepoint PARAM> could be any of:
• Pre-Breakfast Glucose (mmol/L)
• 0:300 AM Glucose (mmol/L)
• Post-Breakfast Glucose (mmol/L)
• Pre-Breakfast-Bedtime Excursion (mmol/L)
• Pre-Lunch Glucose (mmol/L)
• 03:00 Hr-Next Day Pre-Breakfast Excursion (mmol/L)
• Post-Lunch Glucose (mmol/L)
• Bedtime-Next Day Pre-Breakfast Excursion (mmol/L)
• Pre-Dinner Glucose (mmol/L)
• Bedtime-0300 Hrs Excursion (mmol/L)
• Post-Dinner Glucose (mmol/L)
• Daily Glucose Average(mmol/L)
• Bedtime Glucose (mmol/L)
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Table 5.2.1: SMBG Timepoint Summary
Table x.x Summary and Analysis of <Timepoint PARAM>
Page x of y
Mixed Model Repeated Measures (MMRM))
hh:mm ddmmmyyyy
<Analysis Population>
<Study Name>
LS Mean
B-A LSMean Diff
B/T Trt
Visit
Treatment N
Mean (SD)
(Min, Med, Max)
(SE)*a*b
(95% CI)*a*b
P-Val*a*b
Actual measurement of <Timepoint PARAM>
Baseline
Drug A
xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx) xx.xx(xx.xx, xx.xx)
.xxx
Drug B
xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx)
Total
xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx)
...
...
...
...
...
...
...
...
Week 4
Drug A
xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx) xx.xx(xx.xx, xx.xx)
.xxx
Drug B
xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx)
...
...
...
...
...
...
...
...
Week <n>
Drug A
xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx) xx.xx(xx.xx, xx.xx)
.xxx
Drug B
xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx)
...
...
...
...
...
...
...
...
Abbreviations: N = number of patients in <Analysis Population>; B/T = between; W/I = Within; CI = confidence interval; Diff =
difference; LSMean = least squares mean; Max = maximum; Med = median; Min = minimum; N = total number of patients; p-Val = pvalue; SD = standard deviation; SE = standard error; Trt = treatment.
*a - MMRM model for post-baseline measures: [Response Variable = Baseline + Treatment + Visit + <covariates> + Treatment*Visit
(Type III sums of squares)].
*b - ANOVA model for baseline measures: [Response Variable = Treatment (Type III sums of squares)].
Table 5.2.2: SMBG Longitudinal Repeated Measures Analysis Results Metadata
Metadata Field
DISPLAY IDENTIFIER
DISPLAY NAME
RESULT IDENTIFIER
PARAM
PARAMCD
ANALYSIS VARIABLE
ANALYSIS REASON
ANALYSIS DATASET
SELECTION CRITERIA
DOCUMENTATION
Metadata
Table x.x
Summary and Analysis of <Timepoint PARAM> - Mixed Model Repeated Measures (MMRM))
Pre-Breakfast Treatment difference results (LSMean, confidence interval, p-value)
Glucose (mmol/L)
GLUCSTD
AVAL
Primary efficacy analysis as pre-specified in protocol
ADSMBG
ITTFL = “Y” and PARAMCD = “ GLUCSTD” and ATPT = “ Pre-Breakfast” and ANL01FL = “Y”
See SAP Section XX for details. Program: xxx.sas LS means and 95% CIs are based on repeated measures model adjusting for planned
treatment, baseline, Glucose value, visit, and visit*treatment interaction.
PROGRAMMING
STATEMENTS
PROC MIXED DATA = ADSMBG; WHERE ITTFL = “Y” and PARAMCD = “ GLUCSTD” and ATPT = “ Pre-Breakfast” and ANL01FL
= “Y”
CLASS TRTP AVISIT;
MODEL AVAL = TRTP BASE AVISIT AVISIT*TRTP / DDFM = KR;
LSMEANS TRTP / CL DIFF;
RANDOM usubjid / subject = SUBJID TYPE = UN;
RUN ;
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5.2.2 Self-Monitored Glucose Plots
The graph below shows a plot of 9-point self-monitored plasma glucose, which has been used instead of whole blood glucose. Results are shown in both
conventional and SI units.
Figure 5.2.1: 9-Point Self-Monitored Plasma Glucose Profile at Baseline - Mean Plot - Full Analysis Set
SMPG: Self monitored plasma glucose.
Observed data.
Error bars: ± Standard error (mean).
The conversion factor between mmol/L
and mg/dL is 0.0555.
Table 5.2.3: 9-Point SMPG Profile Analysis Results Metadata
Metadata Field
DISPLAY IDENTIFIER
DISPLAY NAME
RESULT IDENTIFIER
PARAM
PARAMCD
ANALYSIS VARIABLE
ANALYSIS REASON
ANALYSIS DATASET
SELECTION CRITERIA
DOCUMENTATION
PROGRAMMING STATEMENTS
Metadata
Figure 5.2.1
9-Point Self-Monitored Plasma Glucose Profile at Baseline - Mean Plot - Full Analysis Set
Mean SMPG (mmol/L)
Glucose (mmol/L)
GLUCSTD
AVAL
Pre-specified in protocol
ADSMBG
ITTFL = “Y” and PARAMCD = “ GLUCSTD” and ANL01FL = “Y” and AVISIT = “Baseline”
See SAP Section XX for details. Program: xxx.sas.
PROC MEANS DATA = ADSMBG; where <selection criteria>;
CLASS TRTP ATPT;
VAR AVAL ;
output out=means mean=mean stderr=stderr;
RUN ;
/** add points for upper and lower standard errors and plot **/
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
5.3 Mixed-Meal Tolerance Test Dataset
This example is based on a Phase III, parallel-group study designed to determine efficacy of Drug A for patients with Type II diabetes. The MMTT analysis will
consist of an area-under-the-curve (AUC) analysis. The following tables provide examples of an LB excerpt from an SDTM dataset, a BDS structured dataset,
analysis dataset metadata, and analysis-variable metadata for an exploratory mixed-meal tolerance test where AUC is calculated from baseline to endpoint. Note
that some valuable variables may not be presented, as only selected variables were chosen to focus on the most important concepts. The example below is also
based on the “Glucose” parameter, but can be applied to other MMTT-related laboratory values such as C-peptide, insulin levels, etc.
Consider the following sample data from SDTM:
Table 5.3.1: Glucose Excerpt from LB domain
Row STUDYID DOMAIN USUBJID LBSEQ LBTESTCD LBTEST
LBCAT
LBSTRESN LBSTRESU LBSPEC LBBLFL LBFAST VISITNUM VISIT
XYZ
LB
XYZ-001-002
1
GLUC
Glucose CHEMISTRY
4.8
mmol/L
PLASMA
Y
Y
2
VISIT2
1
XYZ
LB
XYZ-001-002
2
GLUC
Glucose CHEMISTRY
9.9
mmol/L
PLASMA
Y
2
VISIT2
2
XYZ
LB
XYZ-001-002
3
GLUC
Glucose CHEMISTRY
9.3
mmol/L
PLASMA
Y
2
VISIT2
3
XYZ
LB
XYZ-001-002
4
GLUC
Glucose CHEMISTRY
8.0
mmol/L
PLASMA
Y
2
VISIT2
4
XYZ
LB
XYZ-001-002
5
GLUC
Glucose CHEMISTRY
5.9
mmol/L
PLASMA
Y
2
VISIT2
5
XYZ
LB
XYZ-001-002
9
GLUC
Glucose CHEMISTRY
6.1
mmol/L
PLASMA
Y
4
VISIT4
6
XYZ
LB
XYZ-001-002
11
GLUC
Glucose CHEMISTRY
8.7
mmol/L
PLASMA
4
VISIT4
7
XYZ
LB
XYZ-001-002
12
GLUC
Glucose CHEMISTRY
12.1
mmol/L
PLASMA
4
VISIT4
8
XYZ
LB
XYZ-001-002
13
GLUC
Glucose CHEMISTRY
9.8
mmol/L
PLASMA
4
VISIT4
9
XYZ
LB
XYZ-001-002
14
GLUC
Glucose CHEMISTRY
6.1
mmol/L
PLASMA
4
VISIT4
10
Row
1(cont)
2(cont)
3(cont)
4(cont)
5(cont)
6(cont)
7(cont)
8(cont)
9(cont)
10(cont)
LBDTC
LBTPTNUM
LBTPT
2015-01-19T08:00
1
0 MINUTE
2015-01-19T08:30
2
30 MINUTE
2015-01-19T09:00
3
60 MINUTE
2015-01-19T10:00
4
120 MINUTE
2015-01-19T11:00
5
180 MINUTE
2015-02-16T08:00
1
0 MINUTE
2015-02-16T08:30
2
30 MINUTE
2015-02-16T09:00
3
60 MINUTE
2015-02-16T10:00
4
120 MINUTE
2015-02-16T11:00
5
180 MINUTE
Parameters were created from each time point within a visit.
Rows 1-10:
Show glucose measured in mmol/L as spelled out in the parameter. The parameter code was set to GLUCSTD, and the variable BASETYPE (not
shown) will match the values of ATPT.
Rows 11-16: Show examples of glucose excursion from fasting to 30, 60, and 120 minutes at baseline and at Week 4.
Rows 17-19: Show an example of glucose AUC.
Row 19:
Show an example of a derived Last Observation Carried Forward (LOCF) corresponding to an AVISIT of Endpoint for baseline to endpoint
analysis.
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Rows 20-21: Show an example of glucose incremental AUC.
Table 5.3.2: ADMMTT Analysis Dataset
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
USUBJID
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
XYZ-001-002
PARAM
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose (mmol/L)
Glucose Excursion 0 - 30 Min (mmol/L)
Glucose Excursion 0 - 60 Min (mmol/L)
Glucose Excursion 0 -120 Min (mmol/L)
Glucose Excursion 0 - 30 Min (mmol/L)
Glucose Excursion 0 - 60 Min (mmol/L)
Glucose Excursion 0 -120 Min (mmol/L)
Glucose Area Under Curve
Glucose Area Under Curve
Glucose Area Under Curve
Glucose Incremental AUC
Glucose Incremental AUC
PARAMCD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUCSTD
GLUX030
GLUX060
GLUX0120
GLUX030
GLUX060
GLUX0120
GLUCAUC
GLUCAUC
GLUCAUC
GLUCIAUC
GLUCIAUC
ATPT
0 Min
30 Min
60 Min
120 Min
180 Min
0 Min
30 Min
60 Min
120 Min
180 Min
AVISIT
Baseline
Baseline
Baseline
Baseline
Baseline
Week 4
Week 4
Week 4
Week 4
Week 4
Baseline
Baseline
Baseline
Week 4
Week 4
Week 4
Baseline
Week 4
Endpoint
Baseline
Week 4
TRTP ANL01FL ITTFL AVAL LBSEQ DTYPE
Drug A
Y
Y
4.8
1
Drug A
Y
Y
9.9
2
Drug A
Y
Y
9.3
3
Drug A
Y
Y
8.0
4
Drug A
Y
Y
5.9
5
Drug A
Y
Y
6.1
6
Drug A
Y
Y
8.7
7
Drug A
Y
Y
12.1
8
Drug A
Y
Y
9.8
9
Drug A
Y
Y
6.1
10
Drug A
Y
Y
5.1
Drug A
Y
Y
4.5
Drug A
Y
Y
3.2
Drug A
Y
Y
2.6
Drug A
Y
Y
6.0
Drug A
Y
Y
3.7
Drug A
Y
Y
24.075
Drug A
Y
Y
27.800
Drug A
Y
Y
27.800
LOCF
Drug A
Y
Y
9.675
Drug A
Y
Y
9.500
Table 5.3.3: ADMMTT Analysis Dataset Metadata
Dataset
Description
ADMMTT Mixed-Meal Tolerance
Test Analysis Data
Class
Basic Data
Structure
Structure
One record per subject per
parameter per visit per timepoint
Purpose
Keys
Analysis STUDYID, USUBJID,
PARAMCD, ATPT, AVISIT
Location
Documentation
ADMMTT.xpt ADMMTT.SAS/SAP
Table 5.3.4: ADMMTT Analysis-Variable Metadata
Variable
Name
USUBJID
Variable Label
text
Length/Display
Format
$20
text
text
text
$36
$8
$40
Type
AVISIT
Unique Subject
Identifier
Parameter
Parameter Code
Analysis Time
Point
Analysis Visit
text
$11
STUDYID
TRTP
Study Identifier
text
Planned Treatment text
$12
$15
PARAM
PARAMCD
ATPT
Codelist/Controlled Terms
Source/Derivation/Comment
ADSL.USUBJID
See parameter-value metadata.
See parameter-value metadata.
See parameter-value metadata.
Baseline; Week 2; Week 4;
Week 8; Week 12; Week 24;
Endpoint
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Refer to the SAP for windowing. Endpoint visit is imputed using LOCF
algorithm. See parameter-value metadata.
ADSL.STUDYID
ADSL.TRT01P
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ITTFL
text
AVAL
LBSEQ
DTYPE
Intent-to-Treat
Population Flag
Analysis Value
Sequence Number
Derivation Type
ANL01FL
BASE
BASETYPE
Analysis Flag 1
Baseline Value
Baseline Type
text
float
text
$1
8.1
$20
ABLFL
Baseline Record
Flag
Change From
Baseline
text
$1
float
8.1
CHG
$1
Y; N
float
8.1
integer 4.0
text
10
ADSL.ITTFL
See parameter-value metadata.
LB.LBSEQ from the record in the SDTM LB domain containing AVAL.
See parameter-value metadata.
Y
Pre-Breakfast; Post-Breakfast;
Pre-Lunch; Post-Lunch; PreDinner; Post-Dinner, …
Y
Set to “Y” on records intended for MMRM analysis, where AVAL is not null.
BASE = AVAL where ABLFL = “Y” for each corresponding timepoint
See parameter-value metadata.
Set to “Y” when AVISIT = “Baseline”. See SAP for visit windowing.
AVAL-BASE
Other parameters of interest, depending on the analyses required for the particular compound could be: insulin, pro-insulin, and/or C-peptide. Each of them could
have derived parameters such as: excursion, AUC, incremental AUC, etc.
Table 5.3.5: ADMMTT Parameter [CL.PARAM.ADMMTT]
Permitted Value (code)
Glucose (mmol/L)
Glucose Excursion 0 - 30 Min (mmol/L)
Glucose Excursion 0 - 60 Min (mmol/L)
Glucose Excursion 0 -120 Min (mmol/L)
Glucose Area Under Curve
Glucose Incremental AUC
Table 5.3.6: ADMMTT Parameter Code [CL.PARAMCD.ADMMTT]
Permitted Value (Code)
GLUCSTD
GLUX030
GLUX060
GLUX0120
GLUCAUC
GLUCIAUC
Display Value (Decode)
Glucose (mmol/L)
Glucose Excursion 0 - 30 Min (mmol/L)
Glucose Excursion 0 - 60 Min (mmol/L)
Glucose Excursion 0 -120 Min (mmol/L)
Glucose Area Under Curve
Glucose Incremental AUC
Table 5.3.7: Parameter Value-Level List – ADMMTT [AVAL]
Variable
AVAL
Where
PARAMCD = “GLUCSTD”
Length/ Codelist/
Type Display Controlled
Source/Derivation/Comment
Format
Terms
float best12.
Derived: AVAL = LB.LBSTRESN where LB.LBTESTCD = “GLUC” at the corresponding LBTPT to
each ATPT
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CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Variable
AVAL
AVAL
AVAL
AVAL
AVAL
Length/ Codelist/
Type Display Controlled
Source/Derivation/Comment
Format
Terms
PARAMCD = “GLUX030”
float best12.
Derived: Set to the difference between Glucose at time 30 min minus Glucose value at 0 minutes
PARAMCD = “GLUX060”
float best12.
Derived: Set to the difference between Glucose at time 60 min minus Glucose value at 0 minutes
PARAMCD = “GLUX0120” float best12.
Derived: Set to the difference between Glucose at time 120 min minus Glucose value at 0 minutes
PARAMCD = “GLUCAUC” float best12.
Derived: Set AVAL = the sum from i = 1 to n - 1 of ((Ti+1 - Ti) * (Ci + Ci+1))/2 where
n = the number of timepoints
Ti = the timepoint in hours at time i
Ti+1 = the timepoint in hours at time i + 1
Ci = LB result at time i
Ci+1 = LB result at time i + 1
PARAMCD = “GLUCIAUC” float best12.
Derived: Set AVAL = the sum from i = 1 to n - 1 of ((Ti+1 - Ti) * (Ei + Ei+1))/2 where
n = the number of timepoints
Ti = the timepoint in hours at time i
Ti+1 = the timepoint in hours at time i + 1
Ei = Glucose excursion at time i
Ei+1 = the Glucose excursion at time i + 1
Where
Table 5.3.8: Parameter Value Level List – ADMMTT [AVISIT]
Variable
AVISIT
AVISIT
Where
Type
PARAMCD in (“GLUCSTD” “GLUX030”, text
“GLUX060”, “GLUX0120”, “GLUCIAUC”)
PARAMCD = “GLUCAUC”
text
Length/Display
Codelist/Controlled
Source/Derivation/Comment
Format
Terms
$10.
Baseline; Week 2; Week 4; Week 8; Derived: AVISIT mapped from SDTM VISIT when
Week 12; Week 24
possible. See SAP for windowing rules.
$10.
Baseline; Week 2; Week 4; Week 8; Derived: Endpoint visit is added using LOCF
Week 12; Week 24; Endpoint
Table 5.3.9: Parameter Value-Level List – ADMMTT [DTYPE]
Variable
DTYPE
DTYPE
Where
Type
PARAMCD in (“GLUCSTD” “GLUX030”, text
“GLUX060”, “GLUX0120”,
“GLUCIAUC”)
PARAMCD = “GLUCAUC”
text
Length/Display
Format
$10.
$10.
Codelist/Controlled
Terms
Source/Derivation/Comment
Derived: Set to blank
LOCF
Derived: Set to LOCF for the additional Endpoint visit
Table 5.3.10: Parameter Value-Level List – ADMMTT [ATPT]
Variable
Where
Type
ATPT
PARAMCD = “GLUCSTD”
text
ATPT
PARAMCD in ( “GLUX030”, “GLUX060”, text
“GLUX0120”, “GLUCAUC”,
“GLUCIAUC”)
Length/Display
Codelist/Controlled
Format
Terms
$10.
0 Min; 30 Min; 60 Min; 120 Min;
180 Min
$10.
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Provisional
Source/Derivation/Comment
Derived: Set to the corresponding ADaM version of
SDTM LB.LBTPT
Derived: Set to blank
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Dec 18, 2015
CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Table 5.3.11: Parameter Value-Level List – ADMMTT [BASETYPE]
BASETYPE PARAMCD = “GLUCSTD”
text
BASETYPE PARAMCD in (“GLUX030”,
“GLUX060”, “GLUX0120”)
BASETYPE PARAMCD in (“GLUCAUC”,
“GLUCIAUC”)
text
Length/Display
Codelist/Controlled Terms
Format
$10.
0 Min; 30 Min; 60 Min; 120 Min;
180 Min
$10.
EXCURSION
text
$10.
Variable
Where
Type
AUC
Source/Derivation/Comment
Derived: Set to match the ATPT.
Derived: Set to “AUC”
Derived: Set to “EXCURSION”
5.4 Mixed Meal Tolerance Test Analysis Results
The following graph shows an example of glucose levels at baseline to endpoint for one patient. The shaded area describes the glucose AUC change from
baseline to endpoint for the same patient.
Figure 5.4.1: Change of Glucose AUC from Baseline to Endpoint
Change of Glucose AUC from Baseline to Endpoint
Glucose [mmol/L]
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00
0
15
30
45
60
75
90
105 120 135 150 165 180 195 210 225 240
Time Point (min)
Baseline
Endpoint
Once the AUC value has been calculated for each patient, common analyses of AUC include ANCOVA on actual values and change from baseline. Below is an
example of an analysis with change from baseline included.
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Dec 18, 2015
CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Table 5.4.1: MMT Area Under the Curve Glucose Table Shell
Summary and Analysis of AUC Glucose (0-xyz minutes)
ANCOVA at X Weeks
<Analysis population>
Study ABCDEF
Variable analyzed: AUC Glucose (mmol*hr/L)
Actual value
Visit
(Week)
Treatment N
Mean
SD
Min
Median
Max
Mean
SD
Min
Baseline
Drug C
Drug A
Drug B
xx
xx
xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
x (yy)
Drug C
Drug A
Drug B
xx
xx
xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
xx.xx
pairwise p-value, 95% CI*b
Drug A
Drug B
-x.xx
-x.xx
-x.xx
x.xx
x.xx
x.xx
vs Drug C
.xxx,
(-x.xx,
.xxx,
(-x.xx,
-xx.xx
-xx.xx
-xx.xx
Page x of y
hh:mm ddmmmyyyy
Change from Baseline
Median
Max
LSM
SE
pvalue*a
-x.xx
-x.xx
-x.xx
xx.xx
xx.xx
xx.xx
-x.xx
-x.xx
-x.xx
x.xx
x.xx
x.xx
<.xxx
<.xxx
<.xxx
vs Drug A
x.xx)
x.xx)
.xxx,
(-x.xx,
x.xx)
ANCOVA: analysis of covariance, AUC: Area under the curve, CI: confidence interval, hrs: hours, hr: hour, LSM: least-squares mean, Max
= maximum, Min: minimum, mmol/L: millimole per liter, N: total number of patients with non-missing value at baseline and specified
visit in specified treatment arm, OAM: oral antihyperglycemic medication, SD: standard deviation, SE: standard error, vs: versus.
*a - Within group p-values are from t-tests on LS Mean change from baseline.
*b - Treatment pairwise comparison p-value and 95% CI of pairwise difference of LS Means of change from baseline are from Analysis of
Covariance (ANCOVA) Model: Change from Baseline = Baseline + Pooled Country for Test Meal + Prior Medication Group(previous OAM vs. no
previous OAM) + Treatment (Type III sum of squares)
The Analysis results metadata for the observed AUC analysis are shown below.
Table 5.4.2: MMTT Area Under the Curve Analysis Results Metadata
Metadata Field
DISPLAY IDENTIFIER
DISPLAY NAME
RESULT IDENTIFIER
PARAM
PARAMCD
ANALYSIS VARIABLE
ANALYSIS REASON
ANALYSIS DATASET
SELECTION CRITERIA
DOCUMENTATION
PROGRAMMING STATEMENTS
Metadata
Table 5.4.1
Summary and Analysis of Glucose AUC - ANCOVA
Treatment difference results (LSMean, confidence interval, p-value)
Glucose Area Under the Curve
GLUCAUC
AVAL
Primary efficacy analysis as pre-specified in protocol
ADMMTT
ITTFL = “Y” and PARAMCD =”GLUCAUC” and ANL01FL = “Y”
See SAP Section XX for details. Program: xxx.sas
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Dec 18, 2015
Appendices
Appendix A: CFAST Diabetes ADaM Sub-Team
Name
Rachael Zirkle, Team Leader
Susan Kenny
Nate Friemark
Birgitte Ronn
Paula Martin
Alan Zimmerman
Karina Stender
Stephen Faulkner
Isaac Swanson
Mario Widel
Yingshan You
Jennie G. Jacobson
Institution/Organization
Eli Lilly and Company
Maximum Likelihood
The Griesser Group
Novo Nordisk
Independent Consultant
Eli Lilly and Company
Novo Nordisk
Pfizer
Eli Lilly and Company
Eli Lilly and Company
Johnson & Johnson
Eli Lilly and Company
Appendix B: References
1. Seaquist ER, Anderson J, Childs B, et al. Hypoglycemia and diabetes: a report of a workgroup of the american
diabetes association and the endocrine society. Diabetes Care. 2013;36(5):1384-95. doi: 10.2337/dc12-2480.
2. Bulsara MK, Holman CD, Davis EA, Jones TW. Evaluating risk factors associated with severe hypoglycaemia
in epidemiology studies-what method should we use? Diabet Med. 2004;21(8):914-9. doi: 10.1111/j.14645491.2004.01250.x.
3. Aschner P, Chan J, Owens DR, et al. Insulin glargine versus sitagliptin in insulin-naive patients with type 2
diabetes mellitus uncontrolled on metformin (EASIE): a multicentre, randomised open-label trial. Lancet.
2012;379(9833):2262-9. doi: 10.1016/S0140-6736(12)60439-5.
CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)
Appendix C: Representations and Warranties, Limitations of
Liability, and Disclaimers
CDISC Patent Disclaimers
It is possible that implementation of and compliance with this standard may require use of subject matter covered by
patent rights. By publication of this standard, no position is taken with respect to the existence or validity of any
claim or of any patent rights in connection therewith. CDISC, including the CDISC Board of Directors, shall not be
responsible for identifying patent claims for which a license may be required in order to implement this standard or
for conducting inquiries into the legal validity or scope of those patents or patent claims that are brought to its
attention.
Representations and Warranties
“CDISC grants open public use of this User Guide (or Final Standards) under CDISC’s copyright.”
Each Participant in the development of this standard shall be deemed to represent, warrant, and covenant, at the time
of a Contribution by such Participant (or by its Representative), that to the best of its knowledge and ability: (a) it
holds or has the right to grant all relevant licenses to any of its Contributions in all jurisdictions or territories in
which it holds relevant intellectual property rights; (b) there are no limits to the Participant’s ability to make the
grants, acknowledgments, and agreements herein; and (c) the Contribution does not subject any Contribution, Draft
Standard, Final Standard, or implementations thereof, in whole or in part, to licensing obligations with additional
restrictions or requirements inconsistent with those set forth in this Policy, or that would require any such
Contribution, Final Standard, or implementation, in whole or in part, to be either: (i) disclosed or distributed in
source code form; (ii) licensed for the purpose of making derivative works (other than as set forth in Section 4.2 of
the CDISC Intellectual Property Policy (“the Policy”)); or (iii) distributed at no charge, except as set forth in
Sections 3, 5.1, and 4.2 of the Policy. If a Participant has knowledge that a Contribution made by any Participant or
any other party may subject any Contribution, Draft Standard, Final Standard, or implementation, in whole or in
part, to one or more of the licensing obligations listed in Section 9.3, such Participant shall give prompt notice of the
same to the CDISC President who shall promptly notify all Participants.
No Other Warranties/Disclaimers. ALL PARTICIPANTS ACKNOWLEDGE THAT, EXCEPT AS PROVIDED
UNDER SECTION 9.3 OF THE CDISC INTELLECTUAL PROPERTY POLICY, ALL DRAFT STANDARDS
AND FINAL STANDARDS, AND ALL CONTRIBUTIONS TO FINAL STANDARDS AND DRAFT
STANDARDS, ARE PROVIDED “AS IS” WITH NO WARRANTIES WHATSOEVER, WHETHER EXPRESS,
IMPLIED, STATUTORY, OR OTHERWISE, AND THE PARTICIPANTS, REPRESENTATIVES, THE CDISC
PRESIDENT, THE CDISC BOARD OF DIRECTORS, AND CDISC EXPRESSLY DISCLAIM ANY
WARRANTY OF MERCHANTABILITY, NONINFRINGEMENT, FITNESS FOR ANY PARTICULAR OR
INTENDED PURPOSE, OR ANY OTHER WARRANTY OTHERWISE ARISING OUT OF ANY PROPOSAL,
FINAL STANDARDS OR DRAFT STANDARDS, OR CONTRIBUTION.
Limitation of Liability
IN NO EVENT WILL CDISC OR ANY OF ITS CONSTITUENT PARTS (INCLUDING, BUT NOT LIMITED
TO, THE CDISC BOARD OF DIRECTORS, THE CDISC PRESIDENT, CDISC STAFF, AND CDISC
MEMBERS) BE LIABLE TO ANY OTHER PERSON OR ENTITY FOR ANY LOSS OF PROFITS, LOSS OF
USE, DIRECT, INDIRECT, INCIDENTAL, CONSEQUENTIAL, OR SPECIAL DAMAGES, WHETHER
UNDER CONTRACT, TORT, WARRANTY, OR OTHERWISE, ARISING IN ANY WAY OUT OF THIS
POLICY OR ANY RELATED AGREEMENT, WHETHER OR NOT SUCH PARTY HAD ADVANCE NOTICE
OF THE POSSIBILITY OF SUCH DAMAGES.
Note: The CDISC Intellectual Property Policy can be found at
http://www.cdisc.org/system/files/all/article/application/pdf/cdisc_20ip_20policy_final.pdf.
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