Download The 800 Pound Gorilla: Taking on the Challenge of Globally Standardizing AE and Medication Reporting

The 800-Pound Gorilla: Taking on the Challenge of
Globally Standardizing Adverse Event and Medication Reporting
Deborah Harper, Pfizer Global Research and Development, Ann Arbor, MI
Jennifer Tjernagel, Pfizer Global Research and Development, Ann Arbor, MI
ABSTRACT
When Pfizer merged with Warner-Lambert/Parke-Davis in 2000,
work began to harmonize various processes across all sites. One of
these efforts sought to standardize the collection and reporting of
clinical data. The principle guiding the process was first to
determine what needed to be reported, and then work backwards
toward data collection. The initial step was to agree on a set of
standard summary and listing tables for each data type defined in
the Clinical Data Interchange Standards Consortium (CDISC).
This presentation will discuss how we started, the process used,
decisions made, resulting tables, and the SAS algorithms necessary
to implement the standards for adverse events (AEs) and
medications.
Issues we addressed include common coding dictionaries, levels of
reporting, definition of treatment, imputing dates, and the definition
of treatment emergent signs and symptoms (TESS). Our
presentation will focus on tables, and algorithms, which aid in the
development of SAS code.
INTRODUCTION
In 2000 Pfizer merged with Warner-Lambert/Parke-Davis. It quickly
became evident that the harmonization of various processes and
definitions across all sites was essential. One of these efforts
sought to standardize the collection and reporting of clinical data.
This paper will detail some of the issues encountered by the
Adverse Event and Medication teams.
PROCESS BACKGROUND
When the decision was made to commit to this global effort, a
project team was formed that included representatives from multiple
functions spanning all sites and divisions. The team’s responsibility
was to design and create clinical data standards for the new Pfizer.
The standards were to encompass a variety of clinical trial activities,
from study protocol design to data analysis and summary tables.
These standards needed to be flexible enough to apply to the whole
spectrum of clinical trial types, from small Phase I studies to product
defense studies.
At the heart of the Pfizer data standards definition was the desire to
maintain consistency with the standards defined by the Clinical Data
Interchange Standards Consortium (CDISC). The CDISC
Submissions Data Domain Models have been prepared by the
CDISC Submissions Data Standards (SDS) team to guide the
organization, content, and form of submission datasets for the 12
safety-related domains listed in the FDA guidance documents.1
Additional models (e.g., efficacy, pharmacokinetics) will be
provided in the future by this organization.
Pfizer’s clinical data standardization initiative was divided into
several different phases. Process Team A was formed to define a
standard template and develop instructions for the data standards
documents. During the next phase, Process Team B began to
define the processes by which these standards were to be created,
modified and maintained in the future. Twelve sub-teams were
formed concurrently to begin work on the standards. One team was
assigned to each of the 12 domains defined in CDISC and the FDA
guidance documents.
This paper will focus on 2 of the 12 domains – AEs and
Medications, which faced many of the same issues. We will
discuss how the teams succeeded in resolving them.
KEY ISSUES:
CODING DICTIONARIES
Working with a standard coding dictionary was one issue that was
central to these 2 standard data types. The corporate decision was
to use the MedDRA2 dictionary to code AEs, and the WHO-Drug3
dictionary to code medications. The MedDRA dictionary was new
for every site, while the WHO-Drug dictionary was new to 3 of the 4
sites. Prior to the merger, each site used a different adverse event
dictionary. As the dictionary choice ultimately affects the type of
summary tables produced, the challenge for the teams was to come
to a common understanding of the dictionary structures, and then
decide whether new summary types were required to utilize the
structure of each dictionary.
Multi-axiality in the dictionaries was a new concept for many of the
members. One investigator term for a medication or AE could now
code to several different higher-level groups. For example, “aspirin”
written on a case report form would code to “ACETYLSALICYLIC
ACID” for the low level generic term in WHO-Drug, but ultimately
would code to 3 different main groups in the highest level of the
hierarchy: “ALIMENTARY TRACT AND METABOLISM,” “BLOOD
AND BLOOD FORMING ORGANS,” “NERVOUS SYSTEM”. This
will result in multiple counting of a drug among the different highestlevel terms (see Figure 1).
FIGURE 1: Summary of Concomitant Medications
Drug A
N
Drug B
%
N
Drug C
%
N
%
Number of Subjects
Number of Subjects With Any Concomitant
Medication
XX
XX
XX.X
XX
XX.X
XX
XX.X
ALIMENTARY TRACT AND METABOLISM
ACETYLSALICYLIC ACID
BECOSYM FORTE
CIMETIDINE
TOCOPHEROL
XX
XX
XX
XX
XX
XX.X
XX.X
XX.X
XX.X
XX.X
XX
XX
XX
XX
XX
XX.X
XX.X
XX.X
XX.X
XX.X
XX
XX
XX
XX
XX
XX.X
XX.X
XX.X
XX.X
XX.X
BLOOD AND BLOOD FORMING ORGANS
ACETYLSALICYLIC ACID
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
CARDIOVASCULAR SYSTEM
LISINOPRIL
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
NERVOUS SYSTEM
ACETYLSALICYLIC ACID
MEDINITE
PHENOBARBITAL
XX
XX
XX
XX
XX.X
XX.X
XX.X
XX.X
XX
XX
XX
XX
XX.X
XX.X
XX.X
XX.X
XX
XX
XX
XX
XX.X
XX.X
XX.X
XX.X
SENSORY ORGANS
TOBRADEX
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
VARIOUS
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
GENERAL NUTRIENTS
XX
XX
DATA COLLECTION TOOLS or CRFS
Another decision all teams faced was the creation of a common
data collection tool (DCT), a term inclusive of case reporting forms
(CRFs), electronic data capture screens, and so forth. The adverse
event and medication teams faced the additional challenge of
determining a process for bringing the data in-house. Some sites
collected this information as “visit” based, others used the “log”
method, which simply stated means that the form remains in the
field from event beginning to end, or study completion, which ever
comes first. Adverse Events and Medications ultimately chose the
“log” method and developed a supporting process.
INTENT-TO-TREAT (ITT) vs. ACTUAL TREATMENT AND
TREATMENT EMERGENT SIGNS AND SYMPTOMS
It was decided in the early stages of the treatment data standard
development to use the ITT method for analysis. In the simplest of
terms, ITT means a patient is analyzed as being under the influence
of randomized therapy once therapy has begun until study end
regardless of actual therapy (e.g. interruptions and unplanned
titration). It was up to each team to assess the impact of this
decision on their data standard.
Legacy Warner-Lambert and legacy Pfizer used different methods
and criteria for determining if a given adverse event was to be
considered treatment-emergent. According to ICH guidelines,
“adverse events (AEs) that are treatment emergent either start or
worsen after study drug administration begins”4. A single method
for determining TESS must be developed in order for data from
multiple studies to be comparable for submission, marketing and
product defense purposes.
For adverse events ITT analysis greatly simplified the existing
calculation that rendered the determination of Treatment Emergent
Signs and Symptoms.
DATE ESTIMATIONS
To estimate or not to estimate is always a dilemma for those
reporting clinical data. It was decided as rule of thumb that data
should stand on its own and estimations should be done sparingly, if
at all. The exception was dates. Each standards team had to
decide and justify estimating dates, and create rules regarding date
estimation independently. There is always the possibility of not
associating a subject with treatment due to a missing date. The
adverse event group decided to estimate start dates only. See the
algorithm section for additional date considerations deliberated by
the medications team.
RESULTING TABLES
The process started with reviewing current guidance documents of
regulatory bodies and determining which presentations were
essential in proving the safety of our treatments. We worked
backwards from data presentations to data sets, then on to the DCT
data validation and protocol language. The goal in developing the
summary tables was to provide a format that would meet the needs
of 80% of the projects.
The adverse event summary table (Figure 2) shown below is an
example of one of the data presentations defined for AEs.
Figure2: Summary of Adverse Events
System Organ Class
Preferred Term
TESS Adverse Events
Number of Subjects (%) with Adverse Events
Treatment A
Treatment B
N=59
N=59
Total Number of Subjects with
AEs
Body as a whole
Headache
Abdominal pain
Accidental injury
Asthenia
Back pain
Chest pain
Flu syndrome
Chills
Infection
Neck pain
Pain
Face edema
Generalized edema
Cardiovascular system
Postural hypotension
* asterisked and italicized items are optional
* Total
N=118
39 (66.1)
45 (76.3)
84 (71.2)
23 (39.0)
7 (11.9)
4 (6.8)
2 (3.4)
3 (5.1)
3 (5.1)
0 (0.0)
3 (5.1)
0 (0.0)
6 (10.2)
1 (1.7)
2 (3.4)
1 (1.7)
1 (1.7)
16 (27.1)
6 (10.2)
2 (3.4)
2 (3.4)
2 (3.4)
2 (3.4)
2 (3.4)
2 (3.4)
1 (1.7)
1 (1.7)
1 (1.7)
1 (1.7)
0 (0.0)
0 (0.0)
39 (33.1)
13 (11.0)
6 (5.1)
4 (3.4)
5 (4.2)
5 (4.2)
2 (1.7)
5 (4.2)
1 (0.8)
7 (5.9)
2 (1.7)
3 (2.5)
1 (0.8)
1 (0.8)
3 (5.1)
0 (0.0)
2 (3.4)
1 (1.7)
5 (4.2)
1 (0.8)
Two different medication summary tables were defined in the data
standards. The first summarized the data first by highest-level term,
then by low-level generic term within each highest-level term (Figure
1). As previously mentioned, this table may categorize a generic
term in several main groups. An option was added to the standards
to allow therapy teams to choose primary highest-level terms for
summarization. This would eliminate multiple counting of a drug, if
desired.
The second available medication table type is summarized just by
the low level generic term (Figure 3).
Figure 3: Summary of Concomitant Medications, Low Level WHODrug Term only
Drug A
N
Drug B
%
N
Drug C
%
N
%
XX.X
XX
XX
XX.X
Number of Subjects
Number of Subjects With Any
Concomitant Medication
XX
XX
XX.X
XX
XX
ACETYLSALICYLIC ACID
BECOSYM FORTE
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
CIMETIDINE
GENERAL NUTRIENTS
LISINOPRIL
XX
XX
XX
XX.X
XX.X
XX.X
XX
XX
XX
XX.X
XX.X
XX.X
XX
XX
XX
XX.X
XX.X
XX.X
MEDINITE
PHENOBARBITAL
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
TOBRADEX
TOCOPHEROL
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
XX
XX
XX.X
XX.X
ALGORITHMS
The standardization of data sources is difficult when trying to create
a standard that will accommodate all the needs of investigational
studies, post marketing, and product defense. A three- tier
approach to managing data sets was devised to allow the collection
of data from a variety of sources. The harmonization and
standardization of data occur in the intermediate data set.
Therefore, data manipulation and calculations are performed after
the creation of the intermediate data set, or at the time of data
presentation generation. An example is a portion of the algorithm
created to apply MedDRA fields to the intermediate listed below:
AEDECOD1 = MedDRA: Lowest Level Term
AEDECOD2 = MedDRA: Preferred Term
AE DECOD3 = High Level Term
AE DECOD4 = High Level Group Term
AEBODSYS = System Organ Class
There were two primary algorithms for the medication data
standard. The first considered data estimation. For medications it
was decided to estimate both start and stop dates if the date was
partially missing. Completely missing dates were not estimated.
For example, for partially missing dates, the most conservative date
was estimated:
For Start Date – assume 1st day of month or 1st month of year.
E.g. xxMAR2001 -> 01MAR2001, 22xxx2001-> 22JAN2001
For Stop Date – assume last day of month or last month of year
E.g. xxMAR2001 -> 31MAR2001, 22xxx2001-> 22DEC2001
The other algorithm for dates determined whether a medication was
a prior (P) or a current (C) medication, or both. Typically a separate
summary table will be produced for each of these 2 categories.
After the date estimation was completed, the rules illustrated in
Figure 4 below could be applied. Day 1 is defined in this example
as the first day the patient took study treatment. For the purposes
of the rules, a date is “given” if either a complete or partial date was
provided. A date is missing if the date was completely missing, and
hence not estimated.
Figure 4: Rules for Determining Prior or Concurrent Medications (or
both) for Summary Purposes
DAY 1
PRIOR (P)
CONCURRENT (C)
Designation:
Start and
Stop Dates
given
Period of
Medication
P
3
WHO Collaborating Centre for International Drug Monitoring, The
Uppsala Monitoring Centre, Stora Torget 3, S-753 20 Uppsala,
Sweden, www.who-umc.org
4
ICH Harmonized Tripartite Guideline: Clinical Safety Data
Management: Definitions and Standards for Reporting, October 27,
1994; published in Federal Register, March 1, 1995, 60 FR 11284
P/C
C
Single event on Day 1
Start Date
missing,
Stop Date
given
P/C
P
P/C
ACKNOWLEDGMENTS
ICH Harmonized Tripartite Guideline: General Considerations for
Clinical Trials; July 17, 1997
C
Start Date
given,
Stop Date
missing
P/C
P/C
C
Start Date
missing,
Stop Date
missing
Algorithms - prior-con.vsd, Page 1 of 1
© Pfizer Inc., 17 September 2001
P/C
DESIGNATING CONCOMITANT MEDICATIONS AS 'PRIOR' OR 'CONCURRENT'
LESSONS LEARNED
One of the reasons this large global project succeeded was the
commitment at the highest level of management. This project was
recognized as essential to the future of the business, and priorities
were adjusted accordingly. Face to face meetings with the
members of the various teams were conducted, and dedicated
resources had blocks of protected time to work on this initiative.
Overall the high level of commitment led to a higher quality product
in a short period of time.
In order for this approach to succeed, it was critical that members at
each site suspend their preference for their historical processes,
and instead focus on creating the best practices for all sites. The
willingness of the teams to embrace this mindset was crucial to
effective decision-making, and fostered an environment of learning.
The participation of clinical colleagues is crucial in setting
standards, not only on the data standards teams but also at the
highest level of sponsorship. Because processes will be changing
and the majority of colleagues were not directly involved in the
decision making, robust communication and clear definition of the
rationale for each decision is essential. This will assist colleagues
in accepting the new processes and integrating them into their
business
CONCLUSION
Pfizer invested a tremendous amount of time, effort, and capital into
this global standardization project believing that the rewards of
standardizing the analysis, reporting, and collection of data in the
future will be well worth the cost. It also gave the organization the
opportunity to drill down into the details and ensure that the Pfizer
data definitions and calculations were consistent across the
company. These standards put Pfizer in a position to analyze and
report data the instant the database is finalized.
REFERENCES
1
2
http://www.cdisc.org
http://www.meddramsso.com
ICH Harmonized Tripartite Guideline: Structure and Content of
Clinical Study Reports; November 30 1995 (provides guidance
regarding concomitant medications and recording of information, as
medications being taken at time of AE vs. treatment administered to
treat the AE; pp 9, 16, 24)
ICH Harmonized Tripartite Guideline: Clinical Safety Data
Management: Periodic Safety Update Reports for Marketed
Treatments; November 6, 1996
Defining Treatment-Emergent Adverse Events with the Medical
Dictionary for Regulatory Activities (MedDRA) Mary E Nilsson, MS
Senior Statistician, Statistical and Mathematical Sciences
Stephanie C. Koke, MS, Associate Communications Consultant,
Global Scientific Information and Communications. Lilly Research
Laboratories, Indianapolis, Indiana, Drug Information Journal Vol.
35 pp.1289-1299 2001
CONTACT INFORMATION
Your comments and questions are valued and encouraged.
Contact the authors at:
Jennifer Tjernagel
Pfizer Global Research and Development
2800 Plymouth Road
Ann Arbor, MI 48105
Work Phone: (734) 622-3599
Fax: (734) 622-2017
Email: [email protected]
Deborah Harper
Pfizer Global Research and Development
2800 Plymouth Road
Ann Arbor, MI 48105
Work Phone: (734) 622-7657
Fax: (734) 622-5622
Email: [email protected]