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1.
Analyses and Displays Associated with Adverse
Events – Focus on Adverse Events in Phase 2-4
Clinical Trials and Integrated Summary
Documents
Version 1.0
Created xx XXXX 201x
A White Paper by the PhUSE Computational Science Development of Standard Scripts
for Analysis and Programming Working Group
Disclaimer: The opinions expressed in this document are those of the authors and do
not necessarily represent the opinions of PhUSE, members' respective companies or
organizations, or regulatory authorities. The content in this document should not be
interpreted as a data standard and/or information required by regulatory authorities.
NOTE TO REVIEWERS: This is the first draft being sent for broad review. The intent is to get
initial feedback on the proposed tables and figures, so please focus on Section 7. Comments on
the other sections are still welcome.
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2. Table of Contents
Section
Page
1.
Analyses and Displays Associated with Adverse Events
– Focus on Adverse Events in Phase 2-4 Clinical Trials
and Integrated Summary Documents ......................................................................1
2.
Table of Contents ....................................................................................................................2
3.
Revision History ......................................................................................................................4
4.
Purpose ....................................................................................................................................5
5.
Introduction .............................................................................................................................6
6. General Considerations ...........................................................................................................7
6.1. Importance of Visual Displays ...........................................................................................7
6.2. P-values and Confidence Intervals .....................................................................................7
6.3. Conservativeness ................................................................................................................8
6.4. Number of Therapy Groups................................................................................................8
6.5. Multi-phase Clinical Trials .................................................................................................8
6.6. Integrated Analyses ............................................................................................................8
6.7. Adverse Event Definitions .................................................................................................8
6.8. Adverse Event Data Collection ..........................................................................................9
6.9. Adverse Event Categories and Preferred Terms ..............................................................10
6.10. Adverse Event Severity ....................................................................................................10
6.11. Adverse Event Relatedness Assessment by the Investigator ...........................................10
6.12. Calculating Percentages using Population-Specific Denominators .................................11
6.13. Exposure-Adjusted Summaries ........................................................................................11
6.14. Time-to-Event Summaries................................................................................................11
7. Tables and Figures for Individual Studies .............................................................................12
7.1. Recommended Displays ...................................................................................................12
7.2. Discussion...........................................................................................................................1
8. Tables and Figures for Integrated Summaries .........................................................................3
8.1. Recommended Displays .....................................................................................................3
8.2. Discussion...........................................................................................................................3
9. Example SAP Language ..........................................................................................................4
9.1. Individual Study .................................................................................................................4
9.2. Integrated Summary ...........................................................................................................4
10. References ...............................................................................................................................5
11. Acknowledgements .................................................................................................................6
12. Appendix .................................................................................................................................7
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3. Revision History
Version 1.0 was finalized xx XXXX 201x.
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4. Purpose
The purpose of this white paper is to provide advice on displaying, summarizing, and/or
analyzing adverse events, with a focus on Phase 2-4 clinical trials and integrated summary
documents. The intent is to begin the process of developing industry standards with respect to
analysis and reporting for measurements that are common across clinical trials and across
therapeutic areas.
This advice can be used when developing the analysis plan for individual clinical trials,
integrated summary documents, or other documents in which adverse events are of interest.
Although the focus of this white paper pertains to specific safety measurements (common
adverse events, dropouts and other significance adverse events, deaths, etc), some of the content
may apply to other measurements (e.g., different safety measurements and efficacy assessments).
Similarly, although the focus of this white paper pertains to Phase 2-4, some of the content may
apply to Phase 1 or other types of medical research (e.g., observational studies).
Development of standard Tables, Figures, and Listings (TFLs) and associated analyses will lead
to improved standardization from collection through data storage. (You need to know how you
want to analyze and report results before finalizing how to collect and store data.) The
development of standard TFLs will also lead to improved product lifecycle management by
ensuring reviewers receive the desired analyses for the consistent and efficient evaluation of
patient safety and drug effectiveness. Although having standard TFLs is an ultimate goal, this
white paper reflects recommendations only and should not be interpreted as “required” by any
regulatory agency.
Detailed specifications for TFL or dataset development are considered out-of-scope for this
white paper. However, the hope is that specifications and code (utilizing SDTM and ADaM data
structures) will be developed consistent with the concepts outlined in this white paper, and
placed in the publicly available PhUSE Standard Scripts Repository.
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5. Introduction
Industry standards have evolved over time for data collection (CDASH), observed data (SDTM),
and analysis datasets (ADaM). There is now recognition that the next step would be to develop
standard TFLs for common measurements across clinical trials and across therapeutic areas.
Some could argue that perhaps the industry should have started with creating standard TFLs
prior to creating standards for collection and data storage (consistent with end-in-mind
philosophy), however, having industry standards for data collection and analysis datasets
provides a good basis for creating standard TFLs.
The beginning of the effort leading to this white paper came from the PhUSE Computational
Science Collaboration, an initiative between PhUSE, FDA, and Industry where key priorities
were identified to tackle various challenges using collaboration, crowd sourcing, and innovation
(Rosario, et. al. 2012). Several Computational Science (CS) working groups were created to
address a number of these challenges.
The working group titled “Development of Standard Scripts for Analysis and Programming” has
led the development of this white paper, along with the development of a platform for storing
shared code. Most contributors and reviewers of this white paper are industry statisticians, with
input from non-industry statisticians (e.g., FDA and academia) and industry and non-industry
clinicians. Hopefully additional input (e.g., other regulatory agencies) will be received for future
versions of this white paper.
There are several existing documents that contain suggested TFLs for common measurements.
However, many of the documents are now relatively outdated, and generally lack sufficient
detail to be used as support for the entire standardization effort. Nevertheless, these documents
were used as a starting point in the development of this white paper. The documents include:





ICH E3: Structure and Content of Clinical Study Reports
Guideline for Industry: Structure and Content of Clinical Study Reports
Guidance for Industry: Premarketing Risk Assessment
Reviewer Guidance. Conducting a Clinical Safety Review of a New Product Application
and. Preparing a Report on the Review
ICH M4E: Common Technical Document for the Registration of Pharmaceuticals for
Human Use - Efficacy
The Reviewer Guidance is considered a key document. Several recommended displays related to
adverse events are included. This white paper provides additional detail and some recommended
improvements.
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6. General Considerations
This section contains some general considerations for the plan of analyses and displays
associated with adverse events.
6.1. Importance of Visual Displays
Communicating information effectively and efficiently is crucial in detecting safety signals and
enabling decision-making. Current practice, which focuses on tables and listings, has not always
enabled us to communicate information effectively since tables and listings may be very long
and repetitive. Graphics, on the other hand, can provide more effective presentation of complex
data, increasing the likelihood of detecting key safety signals and improving the ability to make
clinical decisions. They can also facilitate identification of unexpected values. For the topic of
adverse events, the use of tables and listings generally is more common for the summary of
safety data. While adverse events can benefit some from visual displays, it may not be as much
as other safety topics.
Standardized presentation of visual information is encouraged. The FDA/Industry/Academia
Safety Graphics Working Group was initiated in 2008. The working group was formed to
develop a wiki and to improve safety graphics best practice. It has recommendations on the
effective use of graphics for three key safety areas: adverse events, ECGs and laboratory
analytes. The working group focused on static graphs, and their recommendations were
considered while developing this white paper. In addition, there has also been advancement in
interactive visual capabilities. The interactive capabilities are beneficial, but are considered outof-scope for this version of the white paper.
6.2. P-values and Confidence Intervals
There has been ongoing debate on the value or lack of value for the inclusion of p-values and/or
confidence intervals in safety assessments (Crowe, et. al. 2009). This white paper does not
attempt to resolve this debate. As noted in the Reviewer Guidance, p-values or confidence
intervals can provide some evidence of the strength of the finding, but unless the trials are
designed for hypothesis testing, these should be thought of as descriptive. Throughout this white
paper, p-values and measures of spread are included in several places. Where these are included,
they should not be considered as hypothesis testing. If a company or compound team decides
that these are not helpful as a tool for reviewing the data, they can be excluded from the display.
Some teams may find p-values and/or confidence intervals useful to facilitate focus, but have
concerns that lack of “statistical significance” provides unwarranted dismissal of a potential
signal. Conversely, there are concerns that due to multiplicity issues, there could be overinterpretation of p-values adding potential concern for too many outcomes. Similarly, there are
concerns that the lower- or upper-bound of confidence intervals will be over-interpreted. It is
important for the users of these TFLs to be educated on these issues.
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6.3. Conservativeness
The focus of this white paper pertains to clinical trials in which there is comparator data. As
such, the concept of “being conservative” is different than when assessing a safety signal within
an individual subject or a single arm. A seemingly conservative approach may end up not being
conservative in the end. For example, for studies that collect safety data during an off-drug
follow-up period, one might consider it conservative to include the adverse events reported in the
follow-up period. However, this approach may result in smaller odds ratios than including only
the exposed period in the analysis. A conservative approach for defining outcomes, from a single
arm perspective, is one that would lead to a higher number of patients reaching a threshold.
However, a conservative approach for defining outcomes may actually make it more difficult to
identify safety signals with respect to comparing treatment with a comparator (see Section
7.1.7.3.2 in the Reviewer Guidance). Thus, some of the approaches recommended in this white
paper may appear less conservative than alternatives, but the intent is to propose methodology
that can identify meaningful safety signals for a treatment relative to a comparator group.
6.4. Number of Therapy Groups
The example TFLs show one treatment arm versus comparator in this version of the white paper.
Most TFLs can be easily adapted to include multiple treatment arms or a single arm.
6.5. Multi-phase Clinical Trials
The example TFLs show one treatment arm versus comparator within a controlled phase of a
study. Discussion around additional phases (e.g., open-label extensions) is considered out-ofscope in this version of the white paper. Many of the TFLs recommended in this white paper can
be adapted to display data from additional phases.
6.6. Integrated Analyses
For submission documents, TFLs are generally created from using data from multiple clinical
trials. Determining which clinical trials to combine for a particular set of TFLs can be complex.
Section 7.4.1 of the Reviewer Guidance contains a discussion of points to consider. Generally,
when p-values are computed, adjusting for study is important. Creating visual displays or tables
in which comparisons are confounded with study is discouraged. Understanding whether the
overall representation accurately reflects the review across individual clinical trial results is
important.
6.7. Adverse Event Definitions
As discussed in the Reviewer Guidance, an adverse event is any untoward medical occurrence
associated with the use of a drug in humans, whether or not considered drug related. The use of
a similar term, adverse reaction, is used to refer to an undesirable effect, reasonably associated
with the use of a drug that may occur as part of the pharmacological action of the drug or may be
unpredictable in its occurrence. Adverse reactions do not include all adverse events observed
during the use of a drug, only those which there is some basis to believe a causal relationship
exists between the drug and the occurrence of the adverse event.
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Serious adverse events are adverse events occurring at any dose, whether or not considered to
be drug-related, that results in any of the following outcomes:






Death
A life-threatening adverse experience
Inpatient hospitalization or prolongation of existing hospitalization
A persistent or significant disability/incapacity
A congenital anomaly or birth defect
Important medical events that may not result in death, be life-threatening, or require
hospitalization may be considered serious adverse drug events when, based upon
appropriate medical judgment, they may jeopardize the patient and may require medial or
surgical intervention to prevent one of the outcomes listed above
Non-serious adverse events are all adverse events which do not meet the above criteria for
“serious”.
[In development] The precise definition of a treatment emergent adverse event varies across
the guidance documents, and all lack necessary detail for consistent implementation across the
industry. As a point of note, the Reviewer Guidance document is silent on the definition of the
treatment-emergent. Several other authors have discussed more precise definitions for treatmentemergence (Mary to insert references). Recommending a specific definition is considered outof-scope for this version of the white paper. It is assumed treatment-emergent adverse events
are identified in analysis datasets and available for summaries/analyses. The specific detailed
definition should be documented (protocol, Statistical Analysis Plan, study report methods
section, etc.)
6.8. Adverse Event Data Collection
There are several ways in which adverse event data can be collected for a study/product. Some
methods for obtaining common adverse events include open-ended questioning, specific
solication of particular adverse events, and checklists. Especially for open-ended questioning,
the associated instructions for when to include an event for collection to the sponsor becomes
important for truly understanding “adverse events” and “treatment-emergent adverse events”.
The method of obtaining information should be carefully considered as there may be limitiations
in interpretation depending on the collection approach. Across studies, consideration should be
made to proactively collect adverse event data consistently. Recommending a specific method
for collection and/or specific instructions associated with collection is considered out-of-scope
for this white paper. We do hope CDISC/CDASH efforts continue to progress to assist in
achieving more consistency in adverse event collection.
[In development] Details around the collection of serious adverse events (SAEs) also varies
across current practices. Certain SAEs are provided to sponsors and regulatory agencies
expeditiously per regulations. However, collection into the clinical trial database can vary.
Events meeting regulatory-defined seriousness (per FDA/ICH; reference to be added) are
considered serious without controversy. As noted in the xxx guidance, other adverse events of
interest can be defined as “serious”. Some sponsors include additional events for some
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compounds, while others always stay with the regulatory-defined definition. A flag for
seriousness if often embedded with adverse event collection. In some cases, the date in which
the event became serious is included, in other collection methods it’s not. Etc.
6.9. Adverse Event Categories and Preferred Terms
[In development] Investigator reported adverse events usually include verbatim terms. One
commonly accepted approach to placing verbatim terms into appropriate adverse event cateogies
prior to analyses includes mapping to a standard dictionary of preferred term, the most granular
of these approaches being MedDRA. The TFL analyses proposed in this document generally
assume that adverse events are appropriately mapped to adverse event categories and preferred
terms in advance of analysis. As noted in xxx guidance, too granular of categorization can result
in under-estimation (e.g., xxxx and xxx are different PTs but can be representing the same event
in practice). Thus, grouping some events may be warrated for safety signal detection and for
providing incidence rates for labeling Etc.
[Add CTCAE]
[Maybe something on SMQs – generally used for adverse events of special interest – usually
AESIs are out-of-scope for these white papers, but since the use of SMQs is common, an generic
display is included? For sponsor-defined groups of terms, a similar display can be used.
Looking at all SMQs exploratory way – out-of-scope]
6.10. Adverse Event Severity
[In development] Most common – mild/moderate/severe. CTCAE grades available for some.
For purposes of this white paper, dispays will be provided for either method.
6.11. Adverse Event Relatedness Assessment by the Investigator
[In development] As discussed in the Reviewer Guidance, the relatedness assessment made by
the investigator is generally not considered useful. For purposes of this white paper, we assume
at least one regulatory agency and/or sponsor will want a summary of events considered related
by the investigator, thus a recommended display is included. The collection of relatedness is not
defined in CDISC/CDASH and is defined by the sponsor. Thus, collection varies (yes/no,
yes/no/unknown, no/probable/possibly/likely, missing allowed, missing not allowed, etc.) and
will likely continue to vary unless CDISC/CDASH efforts continue to progress. For this version
of the white paper, we assume relatedness was either collected as yes/no or the collected
categories are grouped to yes/no. We assume unknown and missing are not allowed. Any
derivations into defining relatedness as yes/no, if required, should be documented (protocol,
Statistical Analysis Plan, study report methods section, etc.).
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6.12. Calculating Percentages using Population-Specific
Denominators
[In development] Some events only occur in a particular gender…. Some events only occur in
pediatric subjects, geriatric, etc….. MedDRA provides a list of gender-specific adverse events
and pediatric-specific adverse events. Theoretically, percentages for many events can be created
using a denominator from only those demographics that can have the event. In practice, we
recommend only attempting such adjustments for gender and pediatric (when both pediatric and
adults are included in a study) as a consistent identification is provided by MedDRA…. Review
Guidance highlights gender as something to create gender denominators for…
6.13. Exposure-Adjusted Summaries
[In development] Include situations where such summaries are warranted for signal detection vs
not warranted
6.14. Time-to-Event Summaries
[In development] Include situations where such summaries are warranted for signal detection vs
not warranted
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7. Tables and Figures for Individual Studies
7.1. Recommended Displays
As noted in ICH E3, a brief summary of adverse events is expected. To provide the basis for a
brief summary, an overview table is very helpful (Table 7.1) and is recommended. It provides a
high level summary of the more detailed tables for adverse events. For a more detailed summary
of adverse events, Tables 7.2-7.4 and Figure 7.1 are recommended. Table 7.2 provides a
summary of all treatment-emergent adverse events (TEAEs), and is sorted by System Organ
Class as recommended in ICH E3 and in the example table in the Reviewer Guidance. Table 7.3
(a side-by-side table of all TEAEs next to the TEAEs considered related by the investigator) is
recommended primarily to support shared learning that at least one regulatory agency will expect
it (See Section 6.11). As noted in the Reviewer Guidance and ICH E3, a summary of the most
common TEAEs is expected. The definition of common TEAEs, according to the Reviewer
Guidance, are those TEAEs generally occurring at a rate of 1 percent of more, however a higher
cut off than 1 percent may be considered if doing so does not lose important information.
Although a table is typically produced for the most common TEAEs (by simply subsetting on the
table of all TEAEs), Figure 7.1 is recommended as a more visual, user-friendly way to view the
data. Table 7.4a or Table 7.4b is recommended to display maximum severity or maximum
CTCAE grade (depending on collection). Although the Reviewer Guidance does not indicate an
expectation for such a display, it is noted in ICH E3 and is frequently useful for overall
interpretation of adverse event data.
As noted in the Reviewer Guidance and ICH E3, a listing of deaths is expected and is
recommended (Listing 7.1). Although the guidance documents do not specifically refer to a
summary of serious adverse events (only a listing), we believe a summary table of all serious
events during the treatment period is very helpful for data interpretation (Table 7.5). If the
number of SAEs is expected to be extremely small, then a listing would be sufficient. The
recommended summary table displays all preferred terms by decreasing frequency without
sorting by System Organ Class. The table could be sorted by System Organ Class if deemed
useful (e.g., a lot of SAEs across a number of System Organ Classes).
To further understand the severity of adverse events, a summary of adverse events leading to
treatment discontinuation is recommended (Table 7.6). The guidance documents do not
specifically refer to a summary table (only a listing), but it is commonly created across the
industry and is considered very useful in understanding tolerability.
Table 7.7 is recommended when there is a topic of special interest that can be defined by the use
of a Standardized MedDRA Query (SMQ). SMQs were created to define common medical
concepts of interest by grouping relevant MedDRA preferred terms (reference). Although topics
of special interest are considered out-of-scope for this white paper, it is included since it’s
common for compounds to have at least one SMQ of interest.
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Table 7.1 Overview of Subject Incidence of Adverse Events
Safety Population
Treatment Emergent Adverse Events
Treatment-Emergent Adverse Events Related to Study Treatment
Serious Adverse Events
Adverse Events leading to Discontinuation of Investigational Product
Fatal Adverse Events
Treatment A
(N=XX)
n (%a)
Treatment B
(N=XX)
n (%a)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
xx (xx.x)
P-value b
xx
xx
xx
xx
xx
Notes: Subjects may be counted in more than one row.
aDenominator
bP-value
for each % is the treatment column N
from Fisher’s Exact Test
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Table 7.2 Summary of Treatment-Emergent Adverse Events by Preferred Term in Descending Frequency within System Organ Class
Safety Population
Treatment 1
Treatment 2
(N = XX)
(N = XX)
n (%a)
n (%a)
P-valueb
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #1]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #2]c
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
System Organ Class
Preferred Term
Number of subjects reporting treatmentemergent adverse events
[System Organ Class #1]
[System Organ Class #2]
[Preferred Term #1]d
….
aDenominator
for each % is the treatment column N
from Fisher’s Exact Test
cDenominator adjusted because gender-specific event for males: N=XX (Treatment 1), N=XX (Treatment 2)
dDenominator adjusted because gender-specific event for females: N=XX (Treatment 1), N=XX (Treatment 2)
bP-value
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Table 7.3 Summary of Treatment-Emergent Adverse Events and Treatment Emergent Adverse Events Considered Related per
Investigator by Preferred Term in Descending Frequency within System Organ Class
Safety Population
Treatment-Emergent Adverse Events
Treatment Emergent Adverse Events
Considered Related per Investigator
Treatment 1
Treatment 2
Treatment 1
Treatment 2
System Organ Class
(N = XX)
(N = XX)
(N = XX)
(N = XX)
Preferred Term
n (%a)
n (%a)
n (%a)
n (%a)
P-valueb
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #1]
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #2]c
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
xx (xx.x)
xx (xx.x)
xx
Number of subjects reporting
P-valueb
adverse events
[System Organ Class #1]
[System Organ Class #2]
[Preferred Term #1]d
….
aDenominator
for each % is the treatment column N
from Fisher’s Exact Test
cDenominator adjusted because gender-specific event for males: N=XX (Treatment 1), N=XX (Treatment 2)
dDenominator adjusted because gender-specific event for females: N=XX (Treatment 1), N=XX (Treatment 2)
bP-value
Preferred Terms within System Orqan Class are sorted in descending order of frequency based on the treatment-emergent adverse event
category
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Figure 7.1 Summary of Common Treatment-Emergent Adverse Events by Treatment
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Table 7.4a Summary of Treatment-Emergent Adverse Events by Maximum Severity
Safety Population
Maximum
Treatment 1
Treatment 2
(N = XX)
(N = XX)
n (%a)
n (%a)
P-valueb
Preferred Term
Grade
Number of subjects reporting treatment-
Mild
xx (xx.x)
xx (xx.x)
--
Moderate
xx (xx.x)
xx (xx.x)
--
Severe
xx (xx.x)
xx (xx.x)
xx
Total
xx (xx.x)
xx (xx.x)
xx
Mild
xx (xx.x)
xx (xx.x)
--
Moderate
xx (xx.x)
xx (xx.x)
--
Severe
xx (xx.x)
xx (xx.x)
xx
Total
xx (xx.x)
xx (xx.x)
xx
Mild
xx (xx.x)
xx (xx.x)
--
Moderate
xx (xx.x)
xx (xx.x)
--
Severe
xx (xx.x)
xx (xx.x)
xx
Total
xx (xx.)
xx (xx.)
xx
emergent adverse events
[System Organ Class Class #1]
[Preferred Term #1]
[Preferred Term #2]
aDenominator
for each % is the treatment column N
from Fisher’s Exact Test
cDenominator adjusted because gender-specific event for males: N=XX (Treatment 1), N=XX (Treatment 2) [For applicable PTs]
dDenominator adjusted because gender-specific event for females: N=XX (Treatment 1), N=XX (Treatment 2) [For applicable PTs]
bP-value
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Table 7.4b Summary of Treatment-Emergent Adverse Events by Maximum CTCAE Grade
Safety Population
Maximum
Treatment 1
Treatment 2
(N = XX)
(N = XX)
n (%a)
n (%a)
P-valueb
Preferred Term
CTCAE Grade
Number of subjects reporting treatment-
Grade ≥ 2
xx (xx.x)
xx (xx.x)
--
Grade ≥ 3
xx (xx.x)
xx (xx.x)
xx
Grade ≥ 4
xx (xx.x)
xx (xx.x)
xx
Fatal
xx(xx.x)
xx (xx.x)
xx
Grade ≥ 2
xx (xx.x)
xx (xx.x)
--
Grade ≥ 3
xx (xx.x)
xx (xx.x)
xx
Grade ≥ 4
xx (xx.x)
xx (xx.x)
xx
Fatal
xx(xx.x)
xx (xx.x)
xx
Grade ≥ 2
xx (xx.x)
xx (xx.x)
--
Grade ≥ 3
xx (xx.x)
xx (xx.x)
xx
Grade ≥ 4
xx (xx.x)
xx (xx.x)
xx
Fatal
xx(xx.x)
xx (xx.x)
xx
emergent adverse events
[System Organ Class Class #1]
[Preferred Term #1]
[Preferred Term #2]
aDenominator
for each % is the treatment column N
from Fisher’s Exact Test
cDenominator adjusted because gender-specific event for males: N=XX (Treatment 1), N=XX (Treatment 2) [For applicable PTs]
dDenominator adjusted because gender-specific event for females: N=XX (Treatment 1), N=XX (Treatment 2) [For applicable PTs]
bP-value
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Listing 7.1 Listing of Treatment-Emergent Fatal Adverse Events by Treatment Group
Safety Population
Treatment = <<Treatment 1>>
Subject ID
Age
Sex
Race
Date of First
Date of Last
Date of
Preferred
Verbatim
Deemed
Study Drug
Study Drug
Death
Term of Fatal
Term of
Related to
Administration
Administration
Event
Fatal Event
Treatment?
(Study Day)
(Study Day)
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Table 7.5 Summary of Serious Adverse Events
Safety Population
Treatment 1
Treatment 2
(N = XX)
(N = XX)
n (%a)
n (%a)
P-valueb
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #1]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #2]
xx (xx.x)
xx (xx.x)
xx
Preferred Term
Number of subjects reporting serious
adverse events
…
aDenominator
for each % is the treatment column N
from Fisher’s Exact Test
cDenominator adjusted because gender-specific event for males: N=XX (Treatment 1), N=XX (Treatment 2) [For applicable
PTs]
dDenominator adjusted because gender-specific event for females: N=XX (Treatment 1), N=XX (Treatment 2) [For
applicable PTs]
bP-value
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Table 7.6 Summary of Adverse Events Leading to Treatment Discontinuation
Safety Population
Treatment 1
Treatment 2
(N = XX)
(N = XX)
n (%a)
n (%a)
P-valueb
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #1]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #2]
xx (xx.x)
xx (xx.x)
xx
Preferred Term
Number of Subjects reporting adverse
events leading to treatment discontinuation
…
aDenominator
for each % is the treatment column N
from Fisher’s Exact Test
cDenominator adjusted because gender-specific event for males: N=XX (Treatment 1), N=XX (Treatment 2) [For applicable PTs]
dDenominator adjusted because gender-specific event for females: N=XX (Treatment 1), N=XX (Treatment 2) [For applicable PTs]
bP-value
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Table 7.7a Summary of AESI
Safety Population
Treatment 1
Treatment 2
(N = XX)
(N = XX)
n (%a)
n (%a)
P-valueb
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #1]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #2]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #3]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #n]
xx (xx.x)
xx (xx.x)
xx
Preferred Term
[Event Cluster]
……
aDenominator
bP-value
for each % is the treatment column N
from Fisher’s Exact Text
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Table 7.7b Summary of AESI
Safety Population
Treatment 1
Treatment 2
(N = XX)
(N = XX)
n (%a)
n (%a)
P-valueb
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #1]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #2]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #n]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #1]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #2]
xx (xx.x)
xx (xx.x)
xx
[Preferred Term #n]
xx (xx.x)
xx (xx.x)
xx
Preferred Term
[Event Cluster]
Broadly Defined Preferred Terms
Narrowly Defined Preferred Terms
……
aDenominator
bP-value
for each % is the treatment column N
from Fisher’s Exact Text
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7.2. Discussion
There are certainly many ways to display adverse event data, and existing guidances vary in their
recommendations and /or lack detail. Hopefully the recommendations in this white paper will
provide added details to facilitate consistent implementation in clinical study reports and
integrated summary documents.
There was some debate as to whether an overview table (Table 7.1) should be
recommended. The same information can be found in the other recommended tables, so it can be
viewed as superfluous. However, since the structure of a clinical study report and integrated
summary document includes a brief overview of adverse events, the overview table is
recommended as something that can be included in the section. Otherwise, study teams may end
up hand-generating such a table, which is unnecessary if deemed of value during analysis
planning.
The need for a summary of treatment-emergent adverse events (TEAEs) is consistently
highlighted in guidance documents. We assume at least one display that includes all TEAEs is
needed. Since guidance documents consistently highlight the need for a display of “common”
TEAEs, we also assume a display for common TEAEs is needed. Since the guidance documents
specifically say displays sorted by System Organ Class (SOC) are desired (e.g., the example
table in the Reviewer Guidance), we assume at least one display sorted by SOC is needed. We
also assume a display not sorted by SOC is desired to facilitate the identification of the most
common TEAEs, which is typically outlined in the text of clinical study reports and integrated
summary documents. With all these assumptions in mind, and with the desire to not over-burden
study teams with creating a bunch of similar tables with essentially the same information, we
recommend a table of all TEAEs sorted by SOC (Table 7.2) and a figure of common TEAEs not
sorted by SOC (Figure 7.1).
We considered a table that includes grouping MedDRA preferred terms by High Level Terms
and System Organ Class (Table 12.1). We decided that the inclusion of High Level Terms is
best used during interactive data reviews (out-of-scope) and/or topics of special interest (also
out-of-scope). For purposes of clinical study reports and integrated summary documents, a table
of preferred terms nested within SOC (Table 7.2) is recommended and consistent with the
example table in the Reviewer Guidance.
There was a fair amount of discussion related to a display of TEAEs considered related to study
drug by the investigator. As noted in Section 6, we assume at least one reviewer from at least
one regulatory agency would be interested in the data, which is why a display is
recommended. The exact same display recommended for all TEAEs was strongly considered
(with the added requirement to only include TEAEs considered releted to study drug by the
investigator in the table), as it would be the easiest to implement and it reflects current practice
across the industry. We also considered a display that would include all the different levels of
relatedness, if multiple levels are included in collection (Table 12.2). However, since shared
learning indicates at least one regulatory agency will expect a table that displays all TEAEs and
TEAEs considered related to study drug side-by-side, Table 7.3 is recommended instead. We
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don’t believe this table structure is currently implemented across the industry, but is now
recommended based on shared learning.
Regarding the display of TEAEs by severity (or CTCAE grade), a figure was considered (Figure
12.1). Although we are trying to move more toward visual displays, we decided that a table was
better when displaying severity levels for all TEAEs. The figure can be utilized for topics of
special interest if deemed useful.
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8. Tables and Figures for Integrated Summaries
8.1. Recommended Displays
[To be developed after receiving comments on Section 7]
Consider TEAE – grouped PTs
Consider exposure-adjusted TEAEs (for commone ones)
8.2. Discussion
[To be developed after receiving comments on Section 7]
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9. Example SAP Language
9.1. Individual Study
[To be developed after Sections 7 and 8 are closer to final]
9.2. Integrated Summary
[To be developed after Sections 7 and 8 are closer to final]
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10. References
Amit O, Heiberger RM, and Lane PW. Graphical approaches to the analysis of safety data from
clinical trials. Pharmaceut. Statist. 2008; 7: 20–35. doi: 10.1002/pst.254.
Crowe, B., Brueckner, A., Beasley, C., & Kulkarni, P. (2013). Current Practices, Challenges, and
Statistical Issues With Product Safety Labeling. Statistics in Biopharmaceutical Research, 5(3),
180-193.
Crowe BJ, Xia A, Berlin JA, Watson DJ, Shi H, Lin SL, et. al. Recommendations for safety
planning, data collection, evaluation and reporting during drug, biologic and vaccine
development: a report of the safety planning, evaluation, and reporting team. Clinical Trials
2009; 6: 430-440..
McGill R, Tukey JW, and Larsen WA. Variations of Box Plots. The American Statistician
1978; 32(1): 12-16. doi:10.2307/2683468.JSTOR 2683468.
Nilsson ME and Koke SC. Defining treatment-emergent adverse events with the Medical
Dictionary for Regulatory Activities. Drug Information Journal 2001;35:1289-1299.
Rosario LA, Kropp TJ, Wilson SE, Cooper CK. Join FDA/PhUSE Working Groups to help
harness the power of computational science. Drug Information Journal 2012; 46: 523-524.
The FDA/Industry/Academia Safety Graphics Working Group [Reference to be added]
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11. Acknowledgements
The key contributors include: xxxx.
Additional contributors and members of the white paper project within the PhUSE Development
of Standard Scripts for Analysis and Programming Working Group include:
Acknowledgement to others who provided text for various sections, review comments, and/or
participated in discussions related to methodology: .
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12. Appendix
Table 12.1 Treatment-Emerge Advserse Events – PTs nested within HLT
Table 12.2 Treatment-Emergent Adverse Events by Relatedness Category (model after severity
table – a way to show all the data when more than 2 categories are collected, but discarded due to
shared learning expectations)
Figure 12.1 Treatment Emergent Adverse Event by Maximum Severity
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