E9(R1) STATISTICAL PRINCIPLES 
FOR CLINICAL TRIALS: 
ADDENDUM: ESTIMANDS AND 
SENSITIVITY ANALYSIS IN 
CLINICAL TRIALS 
 
Guidance for Industry 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
Center for Biologics Evaluation and Research (CBER) 
 
May 2021 
ICH 
 
Revision 1
    
E9(R1) STATISTICAL PRINCIPLES 
FOR CLINICAL TRIALS: 
ADDENDUM: ESTIMANDS AND 
SENSITIVITY ANALYSIS IN 
CLINICAL TRIALS 
 
Guidance for Industry 
 
 
Additional copies are available from: 
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https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-regulatory-information-biologics/biologics-guidances     
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
Center for Biologics Evaluation and Research (CBER) 
 
May 2021 
ICH 
 
Revision 1
  Contains Nonbinding Recommendations 
 
FOREWORD 
 
The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for 
Human Use (ICH) has the mission of achieving greater regulatory harmonization worldwide to 
ensure that safe, effective, and high-quality medicines are developed, registered, and maintained 
in the most resource-efficient manner.  By harmonizing the regulatory expectations in regions 
around the world, ICH guidelines have substantially reduced duplicative clinical studies, 
prevented unnecessary animal studies, standardized safety reporting and marketing application 
submissions, and contributed to many other improvements in the quality of global drug 
development and manufacturing and the products available to patients.  
 
ICH is a consensus-driven process that involves technical experts from regulatory authorities and 
industry parties in detailed technical and science-based harmonization work that results in the 
development of ICH guidelines.  The commitment to consistent adoption of these consensus-
based guidelines by regulators around the globe is critical to realizing the benefits of safe, 
effective, and high-quality medicines for patients as well as for industry.  As a Founding 
Regulatory Member of ICH, the Food and Drug Administration (FDA) plays a major role in the 
development of each of the ICH guidelines, which FDA then adopts and issues as guidance to 
industry.  
 
  Contains Nonbinding Recommendations 
 
TABLE OF CONTENTS 
 
 
I.  PURPOSE AND SCOPE (A.1) ........................................................................................ 1 
II.  A FRAMEWORK TO ALIGN PLANNING, DESIGN, CONDUCT, ANALYSIS 
AND INTERPRETATION (A.2) ..................................................................................... 4 
III.  ESTIMANDS (A.3) ........................................................................................................... 5 
A.  Intercurrent Events to be Reflected in the Clinical Question of Interest (A.3.1) ..................... 5 
B.  Strategies for Addressing Intercurrent Events when Defining the Clinical Question of 
Interest (A.3.2) ................................................................................................................................ 6 
C.  Estimand Attributes (A.3.3) .......................................................................................................... 9 
D.  Considerations for Constructing an Estimand (A.3.4) ............................................................. 10 
IV.  IMPACT ON TRIAL DESIGN AND CONDUCT (A.4) ............................................. 13 
V.  IMPACT ON TRIAL ANALYSIS (A.5) ....................................................................... 14 
A.     Main Estimation (A.5.1) .............................................................................................................. 14 
B.     Sensitivity Analysis (A.5.2) .......................................................................................................... 16 
1.    Role of Sensitivity Analysis (A.5.2.1)........................................................................................... 16 
2.     Choice of Sensitivity Analysis (A.5.2.2) ...................................................................................... 17 
C.  Supplemtary Analysis (A.5.3) ..................................................................................................... 17 
VI.  DOCUMENTING ESTIMANDS AND SENSITIVITY ANALYSIS (A.6) ............... 18 
GLOSSARY................................................................................................................................. 19 
i  Contains Nonbinding Recommendations 
 
E9(R1) Statistical Principles for Clinical Trials: Addendum: 
Estimands and Sensitivity Analysis in Clinical Trials 
Guidance for Industry1 
 
 
This guidance represents the current thinking of the Food and Drug Administration (FDA or Agency) on 
this topic.  It does not establish any rights for any person and is not binding on FDA or the public.  You 
can use an alternative approach if it satisfies the requirements of the applicable statutes and regulations.  
To discuss an alternative approach, contact the FDA office responsible for this guidance as listed on the 
title page.   
 
 
 
I.  PURPOSE AND SCOPE (A.1)2 
 
To properly inform decision-making by pharmaceutical companies, regulators, patients, 
physicians, and other stakeholders, clear descriptions of the benefits and risks of a treatment 
(medicine) for a given medical condition should be made available.  Without such clarity, there 
is a concern that the reported treatment effect will be misunderstood.  This addendum presents a 
structured framework to strengthen the dialogue between disciplines involved in the formulation 
of clinical trial objectives, design, conduct, analysis and interpretation, as well as between 
sponsor and regulator regarding the treatment effect or effects of interest that a clinical trial 
should address. 
 
Precision in describing a treatment effect of interest is facilitated by constructing the estimand 
(see Glossary; section III (A.3)) corresponding to a clinical question of interest.  Clarity calls for 
a thoughtful envisioning of intercurrent events (see Glossary; section III.A (A.3.1)) such as 
discontinuation of assigned treatment, use of an additional or alternative treatment, and terminal 
events such as death.  The description of an estimand should reflect the clinical question of 
interest in respect to these intercurrent events, and this addendum introduces strategies to reflect 
different questions of interest that might be posed.  The choice of strategies can influence how 
more conventional attributes of a trial are reflected when describing the clinical question, for 
example, the treatments, population, or the variable (endpoint) of interest. 
 
The statistical analysis of clinical trial data should be aligned to the estimand.  This addendum 
clarifies the role of sensitivity analysis (see Glossary) to explore robustness of conclusions from 
the main statistical analysis. 
 
 
1 This guidance was developed within the Expert Working Group (Efficacy) of the International Council on 
Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) and has been subject to 
consultation by the regulatory parties, in accordance with the ICH process.  This document has been endorsed by the 
ICH Steering Committee at Step 4 of the ICH process, November 2019.  At Step 4 of the process, the final draft is 
recommended for adoption to the regulatory bodies of the ICH regions.  
2 The numbers in parentheses reflect the organizational breakdown of the document endorsed by the ICH Steering 
Committee at Step 4 of the ICH process, November 2019. 
  1 Contains Nonbinding Recommendations 
 
Throughout the addendum, references to the main body of the ICH guidance for industry E9 
Statistical Principles for Clinical Trials (September 1998)3 are made using x.y in parentheses.  
References within this addendum are made using A.x.y in parentheses.  
 
This addendum clarifies and extends ICH E9 in respect to the following topics.   
 
First, ICH E9 introduced the Intention-To-Treat (ITT) principle in connection with the effect of a 
treatment policy in a randomized controlled trial, whereby subjects are followed, assessed, and 
analyzed irrespective of their compliance to the planned course of treatment, indicating that 
preservation of randomization provides a secure foundation for statistical tests.  Three 
consequences arising from the ITT principle can be distinguished:  (1) that the trial analysis 
should include all subjects relevant for the research question; (2) that subjects should be included 
in the analysis as randomized; and (3) as taken directly from the definition of the ITT principle 
(see ICH E9 Glossary), that subjects should be followed up and assessed regardless of adherence 
to the planned course of treatment and that those assessments should be used in the analysis.  It 
remains undisputed that randomization is a cornerstone of controlled clinical trials and that 
analysis should aim at exploiting the advantages of randomization to the greatest extent possible.  
However, the question remains whether estimating an effect in accordance with the ITT principle 
always represents the treatment effect of greatest relevance to regulatory and clinical decision-
making.  The framework outlined in this addendum gives a basis for describing different 
treatment effects and some points to consider for the design and analysis of trials to give 
estimates of these treatment effects that are reliable for decision-making. 
 
Second, issues considered generally under data handling and missing data (see Glossary) are 
revisited.  Two important distinctions are made: (1) the addendum distinguishes discontinuation 
of randomized treatment from study withdrawal and (2) the addendum highlights the distinct 
consequences of different intercurrent events.  Discontinuation of randomized treatment 
represents an intercurrent event to be addressed in the precise specification of the trial objective 
through the estimand.  Study withdrawal gives rise to missing data to be addressed in the 
statistical analysis.  Consider, for example, a subject switching treatments in an oncology trial, 
and a subject for whom no outcome event can be observed because the trial is completed.  
Switching treatment represents an intercurrent event, and the clinical question of interest in 
respect to that event should be clear.  Trial completion before an outcome is observed is 
administrative censoring that should be addressed as a missing data problem in the statistical 
analysis.  Having clarity in the estimand gives a basis for planning which data should be 
collected and hence which data, when not collected, present a missing data problem to be 
addressed in the statistical analysis.  In turn, methods to address the problem presented by 
missing data can be selected to align with the estimand.  In addition, regarding the distinct 
consequences of different intercurrent events, events such as discontinuation of treatment, 
switching between treatments, or use of an additional medication may render the later 
measurements of the variable irrelevant or difficult to interpret even when they can be collected.  
Measurements after a subject dies do not exist. 
 
 
3 We update guidances periodically.  For the most recent version of a guidance, check the FDA guidance web page 
at https://www.fda.gov/regulatory-information/search-fda-guidance-documents.  
  2 Contains Nonbinding Recommendations 
 
Third, issues related to the concept of analysis sets are considered in the framework.  Section 
V.B (5.2) of ICH E9 strongly recommends that analysis of superiority trials be based on the full 
analysis set, defined to be as close as possible to including all randomized subjects.  However, 
trials often include repeated measurements on the same subject.  Eliminating some planned 
measurements on some subjects, perhaps because the measurement is considered irrelevant or 
difficult to interpret, can have similar consequences to excluding subjects altogether from the full 
analysis set, i.e. that the initial randomization is not fully preserved.  A consequence of this not 
fully preserving the initial randomization is that the theoretical benefits that randomization 
confers on testing hypotheses about treatment effects and the practical benefits of balancing 
confounding factors at baseline can be diminished.  In addition, a meaningful value of the 
outcome variable might not exist, such as when the subject dies.  Section V.B (5.2) does not 
directly address these issues.  Clarity is introduced by carefully defining the treatment effect of 
interest in a way that determines both the population of subjects to be included in the estimation 
of that treatment effect and the observations from each subject to be included in the analysis, 
considering the occurrence of intercurrent events.  The meaning and role of an analysis of the per 
protocol set is also revisited in this addendum, in particular, whether the impact of protocol 
violations and deviations can be addressed in a way that is less biased and more interpretable 
than naive analysis of the per protocol set. 
 
Finally, the concept of robustness (see section I.B (1.2)) is given expanded discussion under the 
heading of sensitivity analysis.  A distinction is made between the sensitivity of inference to the 
assumptions of a chosen method of analysis and the sensitivity to the choice of analytic approach 
more broadly.  With precise specification of an agreed-upon estimand and a method of analysis 
that is both aligned to the estimand and prespecified to a level of detail that it can be replicated 
precisely by a third party, regulatory interest can focus on sensitivity to deviations from 
assumptions and limitations in the data in respect to a particular analysis. 
 
The principles outlined in this addendum are relevant whenever a treatment effect is estimated or 
a hypothesis related to a treatment effect is tested, whether related to efficacy or safety.  
Although the main focus is on randomized clinical trials, the principles are also applicable for 
single-arm trials and observational studies.  The framework applies to any data type, including 
longitudinal, time-to-first event, and recurrent event data.  Regulatory interest in the application 
of the principles outlined will be greater for confirmatory clinical trials and, when used to 
generate confirmatory conclusions, for data integrated across trials. 
 
The contents of this document do not have the force and effect of law and are not meant to bind the 
public in any way, unless specifically incorporated into a contract. This document is intended only to 
provide clarity to the public regarding existing requirements under the law. FDA guidance 
documents, including this guidance, should be viewed only as recommendations, unless specific 
regulatory or statutory requirements are cited. The use of the word should in Agency guidances 
means that something is suggested or recommended, but not required.  
 
 
  3 Contains Nonbinding Recommendations 
 
II.  A FRAMEWORK TO ALIGN PLANNING, DESIGN, CONDUCT, ANALYSIS 
AND INTERPRETATION (A.2) 
 
Trial planning should proceed in sequence (see Figure 1).  Clear trial objectives should be 
translated into key clinical questions of interest by defining suitable estimands.  An estimand 
defines the target of estimation for a particular trial objective (i.e., what is to be estimated, see 
section III (A.3) of this guidance).  A suitable method of estimation (i.e., the analytic approach, 
referred to as the main estimator, see Glossary) can then be selected (see section V.A (A.5.1)).  
Certain assumptions will underpin the main estimator.  To explore the robustness of inferences 
from the main estimator to deviations from its underlying assumptions, a sensitivity analysis 
should be conducted in the form of one or more analyses, targeting the same estimand (see 
section V.B (A.5.2)). 
Figure 1: Aligning target of estimation, method of estimation, and sensitivity analysis, for a 
given trial objective 
 
 
 
This framework enables proper trial planning that clearly distinguishes between the target of 
estimation (trial objective, estimand), the method of estimation (estimator), the numerical result 
(estimate, see Glossary), and a sensitivity analysis.  This framework will assist sponsors in 
planning trials, assist regulators in their reviews, and enhance the interactions between these 
parties when discussing the suitability of clinical trial designs and, the interpretation of clinical 
trial results. 
 
  4 Contains Nonbinding Recommendations 
 
The specification of appropriate estimands (see section III (A.3)) will usually be the main 
determinant for aspects of trial design, conduct (see section IV (A.4)) and analysis (see section V 
(A.5)). 
 
 
III.  ESTIMANDS (A.3) 
 
Central questions for drug development and licensing are to establish the existence, and to 
estimate the magnitude, of treatment effects:  how the outcome of treatment compares to what 
would have happened to the same subjects under alternative treatment (i.e., had they not received 
the treatment, or had they received a different treatment).  An estimand is a precise description of 
the treatment effect reflecting the clinical question posed by a given clinical trial objective.  It 
summarizes at a population level what the outcomes would be in the same patients under 
different treatment conditions being compared.  The targets of estimation should be defined in 
advance of a clinical trial.  Once defined, a trial can be designed to enable reliable estimation of 
the targeted treatment effect. 
 
The description of an estimand involves precise specifications of certain attributes, which should 
be developed based not only on clinical considerations but also on how intercurrent events are 
reflected in the clinical question of interest.  Section III.A (A.3.1) introduces intercurrent events.  
Section III.B (A.3.2) introduces strategies to describe the question of interest in respect to 
intercurrent events.  Section III.C (A.3.3) describes the attributes of an estimand, and section 
IV.D (A.3.4) gives considerations for its construction.  It is critically important to understand the 
differences between the strategies and to precisely articulate which are used in constructing the 
estimand.   
 
A.  Intercurrent Events to be Reflected in the Clinical Question of Interest (A.3.1) 
 
Intercurrent events are events occurring after treatment initiation that affect either the 
interpretation or the existence of the measurements associated with the clinical question of 
interest.  It is important to address intercurrent events when describing the clinical question of 
interest to precisely define the treatment effect that is to be estimated. 
 
Intercurrent events should be considered in the description of a treatment effect, because 
measurements of the variable can be influenced by the intercurrent event and the occurrence of 
the intercurrent event may depend on treatment.  For example, two patients might be exposed 
initially to the same treatment and provide the same measure of outcome, but if one patient has 
received additional medication, the information that the two measures give about the treatment 
differs between the two patients.  Furthermore, whether a patient needs to take additional 
medication, and whether a patient can continue taking treatment, may depend on the treatment to 
which the patient is exposed.  Unlike missing data, intercurrent events should not be thought of 
as a drawback to be avoided in clinical trials.  Discontinuation of prescribed treatment, use of 
additional medication, and other such events may occur in clinical practice as they do in clinical 
trials, and their occurrence should be considered explicitly when defining the clinical question of 
interest. 
 
  5 Contains Nonbinding Recommendations 
 
Examples of intercurrent events that can affect interpretation of the measurements include 
discontinuation of assigned treatment and use of an additional or alternative therapy.  Use of an 
additional or alternative therapy can take multiple forms, including change to background or 
concomitant therapy and switching between treatments of interest.  Examples of intercurrent 
events that would affect the existence of the measurements include terminal events such as death 
and leg amputation (when assessing symptoms of diabetic foot ulcers), when these events are not 
part of the variable itself.  Certain clinical events can also be intercurrent events, when their 
occurrence, or nonoccurrence, defines a principal stratum of interest (see section III.B (A.3.2)).  
Examples include tumor shrinkage defining objective response when assessing a treatment effect 
on duration of response in oncology and occurrence of infection when assessing a treatment 
effect on severity of infections occurring after vaccination of initially uninfected subjects. 
 
An intercurrent event might be identified solely by the event itself, such as discontinuation of 
treatment, or might be more granular.  For example, the reason for the event might be specified, 
such as discontinuation of treatment due to toxicity, or due to lack of efficacy; the event might be 
characterized by a certain magnitude or degree, such as the use of additional medication 
exceeding a specified duration or dose; or the timing of the event might be specified, perhaps in 
relation to its proximity to the assessment of the variable.  Some events will affect interpretation 
of the outcome measurements indefinitely, such as discontinuation of treatment, while others will 
affect interpretation only temporarily, such as short-term use of additional treatment.  Indeed, 
additional, or alternative treatments can be diverse, either replacing or supplementing a treatment 
on which the subject is experiencing inadequate benefit, as an alternative when a subject is not 
tolerating the assigned treatment, or as a short-term acute treatment to manage a temporary flare 
in disease symptoms.  In a clinical trial, additional or alternative treatments are often identified 
as, for example, background treatment, rescue medication, prohibited medication, distinguishing 
their different roles and allowing them to be considered separately.  The additional granularity, 
identifying different intercurrent events, is necessary if different strategies are to be used.  If the 
intercurrent event for which a strategy should be selected depends not only on, for example, 
failure to continue with treatment, but also on the reason, magnitude, or timing associated with 
that failure, this additional information should be defined and recorded accurately in the clinical 
trial.  The description of intercurrent events might in theory reflect very specific details of 
treatment and follow-up, such as a single missed dose of a chronic treatment or a dose taken at 
the wrong time of day.  If such specific criteria are not expected to affect interpretation of the 
variable, they would not need to be addressed as intercurrent events. 
 
As indicated above, intercurrent events should be considered when constructing the estimand.  
Because the estimand should be defined in advance of trial design, neither study withdrawal nor 
other reasons for missing data (e.g., administrative censoring in trials with survival outcomes) 
are in themselves intercurrent events.  Subjects who withdraw from the trial may have 
experienced an intercurrent event before withdrawal. 
 
B.  Strategies for Addressing Intercurrent Events when Defining the Clinical 
Question of Interest (A.3.2)  
 
Descriptions of various strategies are listed below, each reflecting a different clinical question of 
interest in respect to a particular intercurrent event.  Whether or not the naming convention is 
  6 Contains Nonbinding Recommendations 
 
used, the choices of strategy should be unambiguously clear once the estimand is constructed.  It 
is not necessary to use the same strategy to address all intercurrent events.  Indeed, different 
strategies will often be used to reflect the clinical question of interest in respect to different 
intercurrent events.  Section III.D (A.3.4) gives some considerations on selecting strategies to 
construct an estimand. 
Treatment Policy Strategy 
 
The occurrence of the intercurrent event is considered irrelevant in defining the treatment effect 
of interest; the values for the variable of interest are used regardless of whether the intercurrent 
event occurs.  For example, when specifying how to address the use of additional medication as 
an intercurrent event, the values of the variable of interest should be used, whether the patient 
takes additional medication or not. 
 
If applied in relation to whether a patient continues treatment, and whether a patient experiences 
changes in other treatments (e.g. background or concomitant treatments), the intercurrent event is 
considered to be part of the treatments being compared.  In that case, this reflects the comparison 
described in the ICH E9 Glossary (under ITT Principle) as the effect of a treatment policy. 
 
In general, the treatment policy strategy cannot be implemented for intercurrent events that are 
terminal events, since values for the variable after the intercurrent event do not exist.  For 
example, an estimand based on this strategy cannot be constructed with respect to a variable that 
cannot be measured because of death. 
Hypothetical Strategies 
 
A scenario is envisaged in which the intercurrent event would not occur: the value of the variable 
to reflect the clinical question of interest is the value that the variable would have taken in the 
hypothetical scenario defined. 
 
A wide variety of hypothetical scenarios can be envisaged, but some scenarios are likely to be of 
more clinical or regulatory interest than others.  For example, it may be of clinical or regulatory 
importance to consider the effect of a treatment under different conditions from those of the trial 
that can be carried out.  Specifically, when additional medication must be made available for 
ethical reasons, a treatment effect of interest might concern the outcomes if the additional 
medication was not available. A very different hypothetical scenario might postulate that 
intercurrent events would not occur, or that different intercurrent events would occur.  For 
example, for a subject that will suffer an adverse event and discontinue treatment, it might be 
considered whether the same subject would not have the adverse event or could continue 
treatment in spite of the adverse event.  The clinical and regulatory interest of such hypotheticals 
is limited and would usually depend on a clear understanding of why and how the intercurrent 
event or its consequences would be expected to be different in clinical practice than in the 
clinical trial. 
 
If a hypothetical strategy is proposed, it should be made clear what hypothetical scenario is 
envisaged.  For example, wording such as if the patient does not take additional medication 
might lead to confusion as to whether the patient hypothetically does not take additional 
  7 Contains Nonbinding Recommendations 
 
medication because it is not available or because the particular patient is supposed not to require 
it. 
 
Composite Variable Strategies 
 
This strategy relates to the variable of interest (see section III.C (A.3.3)).  An intercurrent event 
is considered to be informative about the patients outcome and is therefore incorporated into the 
definition of the variable.  For example, a patient who discontinues treatment because of toxicity 
may be considered not to have been successfully treated.  If the outcome variable was already 
success or failure, discontinuation of treatment for toxicity would simply be considered another 
mode of failure.  Composite variable strategies do not need to be limited to dichotomous 
outcomes, however.  For example, in a trial measuring physical functioning, a variable might be 
constructed using outcomes on a continuous scale, with subjects who die being attributed a value 
reflecting the lack of ability to function.  Composite variable strategies can be viewed as 
implementing the ITT principle in some cases when the original measurement of the variable 
might not exist or might not be meaningful, but where the intercurrent event itself meaningfully 
describes the patients outcome, such as when the patient dies. 
 
Terminal events, such as death, are perhaps the most salient examples of the use of a composite 
strategy.  If a treatment saves lives, its effect on various measures in surviving patients may be of 
interest, but it would be inappropriate to say that the summary measure of interest was only the 
average value of some numerical measure in survivors.  The outcome of interest is survival along 
with the numerical measures.  For example, progression-free survival in oncology trials measures 
the treatment effect on a combination of the growth of the tumor and survival. 
 
While-on-Treatment Strategies 
 
For this strategy, response to treatment before the occurrence of the intercurrent event is of 
interest.  Terminology for this strategy will depend on the intercurrent event of interest, e.g., 
while alive, when considering death as an intercurrent event. 
 
If a variable is measured repeatedly, its values up to the time of the intercurrent event may be 
considered relevant for the clinical question, rather than the value at the same fixed timepoint for 
all subjects.  The same applies to the occurrence of a binary outcome of interest up to the time of 
the intercurrent event.  For example, subjects with a terminal illness may discontinue a purely 
symptomatic treatment because they die, yet the success of the treatment can be measured based 
on the effect on symptoms before death.  Alternatively, subjects might discontinue treatment, and 
in some circumstances, it will be of interest to assess the risk of an adverse drug reaction while 
the patient is exposed to treatment. 
 
Like the composite variable strategy, the while-on-treatment strategy can hence be thought of as 
having an impact on the definition of the variable, in this case, by restricting the observation time 
of interest to the time before the intercurrent event.  Particular care is important if the occurrence 
of the intercurrent event differs between the treatments being compared (see section III.C 
(A.3.3)). 
 
  8 Contains Nonbinding Recommendations 
 
Principal Stratum Strategies 
 
This strategy relates to the population of interest (see section III.C (A.3.3)).  The target 
population might be taken to be the principal stratum (see Glossary) in which an intercurrent 
event would occur. Alternatively, the target population might be taken to be the principal stratum 
in which an intercurrent event would not occur.  The clinical question of interest relates to the 
treatment effect only within the principal stratum.  For example, there might be an interest in 
knowing a treatment effect on severity of infections in the principal stratum of patients becoming 
infected after vaccination.  Alternatively, a toxicity might prevent some patients from continuing 
the test treatment, but there might be an interest in knowing the treatment effect among patients 
who are able to tolerate the test treatment. 
 
It is important to distinguish principal stratification (see Glossary), which is based on potential 
intercurrent events (for example, subjects who would discontinue therapy if assigned to the test 
product), from subsetting based on actual intercurrent events (subjects who discontinue therapy 
on their assigned treatment).  The subset of subjects who experience an intercurrent event on the 
test treatment will often be a different subset from those who experience the same intercurrent 
event on control.  Treatment effects defined by comparing outcomes in these subsets confound 
the effects of the different treatments with the differences in outcomes possibly because of the 
differing characteristics of the subjects. 
 
C.  Estimand Attributes (A.3.3) 
 
The attributes below are used to construct the estimand, defining the treatment effect of interest.   
 
The treatment condition of interest and, as appropriate, the alternative treatment condition to 
which comparison will be made (referred to as treatment through the remainder of this 
document).  These treatments might be individual interventions; might be combinations of 
interventions administered concurrently, e.g., as add-on to standard of care; or might consist of 
an overall regimen involving a complex sequence of interventions (see Treatment Policy 
Strategy and Hypothetical Strategies in section III.B (A.3.2)). 
 
The population of patients targeted by the clinical question.  This attribute might be represented 
by the entire trial population, a subgroup defined by a particular characteristic measured at 
baseline, or a principal stratum defined by the occurrence (or nonoccurrence, depending on 
context) of a specific intercurrent event (see Principal Stratum Strategies in section III.B 
(A.3.2)). 
 
The variable (or endpoint) to be obtained for each patient that is used to address the clinical 
question.  The specification of the variable might include whether the patient experiences an 
intercurrent event (see Composite Variable Strategies and While-on-Treatment Strategies in 
section III.B (A.3.2)). 
 
Precise specifications of treatment, population, and variable are likely to address many of the 
intercurrent events considered in sponsor and regulator discussions of the clinical question of 
interest.  The clinical question of interest in respect to any other intercurrent events will 
  9 Contains Nonbinding Recommendations 
 
usually be reflected using the strategies introduced as treatment policy, hypothetical or while on 
treatment. 
 
Finally, a population-level summary for the variable should be specified, providing a basis for 
comparison between treatment conditions. 
 
When defining a treatment effect of interest, it is important to ensure that the definition identifies 
an effect because of treatment and not because of potential confounders such as differences in 
duration of observation or patient characteristics. 
 
D.  Considerations for Constructing an Estimand (A.3.4) 
 
The clinical questions of interest and associated estimands should be specified at the initial 
stages of planning any clinical trial.  Precise specification of objectives for most trials should 
reflect discontinuation of treatment and use of additional or alternative treatments.  In some 
settings, terminal events, such as death, should be addressed.  Some trial objectives can only be 
described with reference to clinical events, for example, the duration of response in subjects who 
achieve a response. 
 
The construction of an estimand should consider what is of clinical relevance for the particular 
treatment in the particular therapeutic setting.  Considerations include the disease under study; 
the clinical context (e.g., the availability of alternative treatments); the administration of 
treatment (e.g., one-off dosing, short-term treatment, or chronic dosing); and the goal of 
treatment (e.g., prevention, disease modification, symptom control).  Also important is whether 
an estimate of the treatment effect can be derived that is reliable for decision-making.  For 
example, a clinical question on the treatment effect on clinical outcome regardless of which other 
therapies are used before that outcome is experienced, differs from a clinical question on the 
treatment effect had no additional medication been available.  Depending on the setting, either 
treatment effect might represent a clinical question of interest.  However, in both cases, a clinical 
trial designed to estimate these treatment effects will often include the possibility to use 
additional medications if medically necessary.  For the former question, values after the use of 
additional treatment will be relevant.  For the latter question, values after the additional treatment 
are not directly relevant since the values also reflect the impact of that additional medication.  It 
should be agreed that reliable estimation is possible before the choice of estimand is finalized.  
This includes, for the latter question, the methods to replace observations that should not be used 
in the analysis. 
 
When constructing the estimand, it is important to have a clear understanding of the treatment to 
which the clinical question of interest pertains (see section III.C (A.3.3)).  Clear specifications 
for the treatments of interest might already reflect multiple relevant intercurrent events.  
Specifically, a treatment might already reflect the clinical question of interest in respect to 
changes in background treatment, concomitant medications, use of additional or later-line 
therapies, treatment-switching and conditioning regimens.  For example, it is possible to specify 
treatment as intervention A added to background therapy B, dosed as required.  In that case, 
changes to the dose of background therapy B would not need to be considered as an intercurrent 
event.  However, the use of an additional therapy would be considered as an intercurrent event.  
  10 Contains Nonbinding Recommendations 
 
If the use of any additional medication is also reflected, using the treatment policy strategy for 
example, then treatment might be specified as intervention A added to background therapy B, 
dosed as required, and with additional medication, as required.  Alternatively, if the treatment is 
specified as intervention A, then both changes in background therapy and use of additional 
therapy would be addressed as intercurrent events.   
 
Discussions should also consider whether specifications for the population and variable attributes 
should be used to reflect the clinical question of interest in respect to any intercurrent events.  
Strategies can then be considered for any other intercurrent events.  Usually an iterative process 
should be used to reach an estimand that is of clinical relevance for decision-making, and for 
which a reliable estimate can be made.  Some estimands, in particular those for which the 
measurements taken are relevant to the clinical question, can often be robustly estimated, making 
few assumptions.  Other estimands may call for methods of analysis with more specific 
assumptions that may be more difficult to justify and that may be more sensitive to plausible 
changes in those assumptions (see section V.A (A.5.1)).  If significant issues exist to develop an 
appropriate trial design or to derive an adequately reliable estimate for a particular estimand, an 
alternative estimand, trial design, and method of analysis should be considered. 
 
Avoiding or oversimplifying the process of discussing and constructing an estimand risks 
misalignment between trial objectives, trial design, data collection, and method of analysis.  
Although an inability to derive a reliable estimate might preclude certain choices of strategy, it is 
important to proceed sequentially from the trial objective and an understanding of the clinical 
question of interest, and not for the choice of data collection and method of analysis to determine 
the estimand. 
 
The experimental situation should also be considered.  If the management of subjects (e.g., dose 
adjustment for intolerance, rescue treatment for inadequate response, burden of clinical trial 
assessments) under a clinical trial protocol is justified to be different from that which is 
anticipated in clinical practice, this might be reflected in the construction of the estimand. 
 
Once constructed, the estimand should define a target of estimation clearly and unambiguously.  
Consider an intercurrent event of discontinuation of treatment; it is of utmost importance to 
distinguish between treatment effects of interest based on the principal stratum of patients who 
would be able to continue if administered the test treatment and the effect during continued 
treatment.  Furthermore, neither of these should be taken to represent an effect if all patients can 
continue with treatment.   
 
As stated above, when using the hypothetical strategy, some conditions are likely to be more 
acceptable for regulatory decision-making than others.  The hypothetical conditions described 
should therefore be justified for the quantification of an interpretable treatment effect that is 
relevant to inform the decisions to be taken by regulators, and the use of the medicine in clinical 
practice.  The question of what the values for the variable of interest would have been if rescue 
medication had not been available may be an important one.  In contrast, the question of what the 
values for the variable of interest would have been under the hypothetical condition that subjects 
who discontinued treatment because of adverse drug reaction had in fact continued with 
treatment, might not be justifiable as being of clinical or regulatory interest.  A clinical question 
  11 Contains Nonbinding Recommendations 
 
of interest based on the effect if all subjects had been able to continue with treatment is not well-
defined without a thorough discussion of the hypothetical conditions under which it is supposed 
that they would have continued.  The inability to tolerate a treatment may itself constitute 
evidence of an inability to achieve a favorable outcome.   
 
Characterizing beneficial effects using estimands based on the treatment policy strategy might 
also be more generally acceptable to support regulatory decision-making, specifically in settings 
where estimands based on alternative strategies might be considered of greater clinical interest, 
but main and sensitivity estimators cannot be identified that are agreed upon to support a reliable 
estimate or robust inference.  An estimand based on the treatment policy strategy might offer the 
possibility to obtain a reliable estimate of a treatment effect that is still relevant.  In this situation, 
it is recommended to also include those estimands that are considered to be of greater clinical 
relevance and to present the resulting estimates along with a discussion of the limitations, in 
terms of trial design or statistical analysis, for that specific approach.  When constructing 
estimands based on the treatment policy strategy, inference can be complemented by defining an 
additional estimand and analysis pertaining to each intercurrent event for which the strategy is 
used, for example, contrasting both the treatment effect on a symptom score and the proportion 
of subjects using additional medication under each treatment.  Similarly, an estimand using a 
while-on-treatment strategy should usually be accompanied by the additional information on the 
time to intercurrent event distributions, and an estimand based on a principal stratum would 
usefully be accompanied by information on the proportion of patients in that stratum, if 
available.   
 
The considerations informing the construction of estimand to support regulatory decision-making 
based on a non-inferiority or equivalence objective may differ from those for the choice of 
estimand for a superiority objective.  As explained in ICH E9, the problem facing the regulator in 
its decision-making is different when based on non-inferiority or equivalence studies compared 
to superiority studies.  In section III.B (A.3.2), it is stated that such trials are not conservative in 
nature and the importance of minimizing the number of protocol violations and deviations, 
nonadherence, and study withdrawals is indicated.  In section V.B.1 (A.5.2.1), it is described that 
the result of the Full Analysis Set (FAS) is generally not conservative and that its role in such 
trials should be considered very seriously.  Estimands that are constructed with one or more 
intercurrent events accounted for using the treatment policy strategy present similar issues for 
non-inferiority and equivalence trials as those related to analysis of the FAS under the ITT 
principle.  Responses in both treatment groups can appear more similar following 
discontinuation of randomized treatment or use of another medication for reasons that are 
unrelated to the similarity of the initially randomized treatments.  Estimands could be 
constructed to directly address those intercurrent events that can lead to the attenuation of 
differences between treatment arms (e.g., discontinuations from treatment and use of additional 
medications).  When selecting strategies, it might be important to distinguish between trials 
designed to detect whether differences exist between treatments containing the same or similar 
active substance (e.g., comparison of a biosimilar to a reference treatment) and trials where a 
non-inferiority or equivalence hypothesis is used to establish and quantify evidence of efficacy.  
An estimand can be constructed to target a treatment effect that prioritizes sensitivity to detect 
differences between treatments, if appropriate for regulatory decision-making. 
 
  12 Contains Nonbinding Recommendations 
 
 
IV.  IMPACT ON TRIAL DESIGN AND CONDUCT (A.4) 
 
The design of a trial should be aligned to the estimands that reflect the trial objectives.  A trial 
design that is suitable for one estimand might not be suitable for other estimands of potential 
importance.  Clear definitions for the estimands on which quantification of treatments effects 
will be based should inform the choices that are made in relation to trial design.  This includes 
determining the inclusion and exclusion criteria that identify the target population; the 
treatments, including the medications that are allowed and those that are prohibited in the 
protocol; and other aspects of patient management and data collection.  If interest lies, for 
example, in understanding the treatment effect regardless of whether a particular intercurrent 
event occurs, a trial in which the variable is collected for all subjects is appropriate.  
Alternatively, if the estimands that support regulatory decision-making do not require the 
collection of the variable after an intercurrent event, then the benefits of collecting such data for 
other estimands should be weighed against any complications and potential drawbacks of the 
collection. 
 
Efforts should be made to collect all data that are relevant to support estimation, including data 
that inform the characterization, occurrence, and timing of intercurrent events.  Data cannot 
always be collected.  Certainly, subjects cannot be retained in a trial against their will, and in 
some trials, missing data for some subjects is inevitable by design, such as administrative 
censoring in trials with survival outcomes.  On the contrary, the occurrence of intercurrent 
events, such as discontinuation of treatment, treatment switching, or use of additional 
medication, does not imply that the variable cannot be measured thereafter, though the measures 
may not be relevant.  For terminal events such as death, the variable cannot be measured after the 
intercurrent event, but neither should these data generally be regarded as missing. 
 
Not collecting any data needed to assess an estimand results in a missing data problem for 
subsequent statistical inference.  The validity of statistical analyses may rest upon untestable 
assumptions, and depending on the proportion of missing data, this may undermine the 
robustness of the results (see section V (A.5)).  A prospective plan to collect informative reasons 
for why data intended for collection are missing may help to distinguish the occurrence of 
intercurrent events from missing data.  This in turn may improve the analysis and may also lead 
to a more appropriate choice of sensitivity analysis.  For example, loss to follow-up may more 
accurately be recorded as treatment discontinuation due to lack of efficacy.  When that reason for 
discontinuation has been defined as an intercurrent event, this can be reflected through the 
strategy chosen to account for that intercurrent event and not as a missing data problem.  To 
reduce missing data, measures can be implemented to retain subjects in the trial.  However, 
measures to reduce or avoid intercurrent events that would normally occur in clinical practice 
risk reducing the external validity of the trial.  For example, selection of the trial population or 
use of titration schemes or concomitant medications to mitigate the impact of toxicity might not 
be suitable if those same measures would not be implemented in clinical practice. 
 
Randomization and blinding remain cornerstones of controlled clinical trials.  Design techniques 
for avoiding bias are addressed in section II.C (2.3) of ICH E9.  Certain estimands may warrant, 
or may benefit from, use of trial designs such as run-in or enrichment designs, randomized 
  13 Contains Nonbinding Recommendations 
 
withdrawal designs, or titration designs.  It might be of interest to identify the principal stratum 
of subjects who can tolerate a treatment using a run-in period, in advance of randomizing those 
subjects between test treatment and control.  Dialogue between regulator and sponsor should 
consider whether the proposed run-in period is appropriate to identify the target population, and 
whether the choices made for the subsequent trial design (e.g., washout period, randomization) 
support the estimation of the target treatment effect and associated inference.  These 
considerations might limit the use of these trial designs and use of that particular strategy. 
 
A precise description of the treatment effects of interest should inform sample size calculations.  
Particular care should be taken when making reference to historical studies that might, implicitly 
or explicitly, have reported estimated treatment effects or variability based on a different 
estimand.  When all subjects contribute information to the analysis, and when the impact of the 
strategy to reflect intercurrent events is included in the effect size that is targeted and the 
expected variance, it is not usually necessary to additionally inflate the calculated sample size by 
the expected proportion of subject withdrawals from the trial. 
 
Section VII.B (7.2) of ICH E9 addresses issues related to summarizing data across clinical trials.  
The importance of having consistent definitions for the variables of interest is highlighted, and 
this can be extended to the construction of estimands.  Hence, in situations when synthesizing 
evidence from across a clinical trial program is envisaged at the planning stage, a suitable 
estimand should be constructed, included in the trial protocols, and reflected in the choices made 
for the design of the contributing trials.  Similar considerations apply to the design of a meta-
analysis, using estimated effect sizes from completed trials to determine non-inferiority margins, 
or the use of external control groups for the interpretation of single-arm trials.  A naive 
comparison between data sources, or integration of data from multiple trials without 
consideration and specification of the estimand that is addressed in each data presentation or 
statistical analysis, could be misleading. 
 
More generally, a trial is likely to have multiple objectives translated into multiple estimands, 
each associated with statistical testing and estimation.  The multiplicity issues arising should be 
addressed. 
 
 
V.  IMPACT ON TRIAL ANALYSIS (A.5) 
 
A.     Main Estimation (A.5.1) 
 
An estimand for the effect of treatment relative to a control will be estimated by comparing the 
outcomes in a group of subjects on the treatment to those in a similar group of subjects on the 
control.  For a given estimand, an aligned method of analysis, or estimator, should be 
implemented that is able to provide an estimate on which reliable interpretation can be based.  
The method of analysis will also support calculation of confidence intervals and tests for 
statistical significance.  An important consideration for whether an interpretable estimate will be 
available is the extent of assumptions that are made in the analysis.  Key assumptions should be 
stated explicitly together with the estimand and accompanying main and sensitivity estimators.  
Assumptions should be justifiable and implausible assumptions should be avoided.  The 
  14 Contains Nonbinding Recommendations 
 
robustness of the results to potential departures from the underlying assumptions should be 
assessed through an estimand-aligned sensitivity analysis (see section V.B (A.5.2)).  Estimation 
that relies on many or strong assumptions calls for more extensive sensitivity analysis.  When the 
impact of deviations from assumptions cannot be comprehensively investigated through 
sensitivity analysis, that particular combination of estimand and method of analysis might not be 
acceptable for decision-making.   
 
All methods of analysis rely on assumptions, and different methods may rely on different 
assumptions even when aligned to the same estimand.  Nevertheless, some kinds of assumption 
are inherent in all methods of analysis aligned to estimands that use each of the different 
strategies outlined, for example, the methodology for predicting the outcomes that would have 
been observed in the hypothetical scenario, or for identifying a suitable target population in a 
principal stratum strategy.  Some examples are given below related to the different strategies 
used to reflect the occurrence of intercurrent events.  The issues highlighted will be key 
components of discussion between sponsor and regulator in advance of an estimand, main 
analysis, and sensitivity analysis being agreed upon. 
 
Analysis aligned with a treatment policy strategy to address a given intercurrent event may entail 
stronger or weaker assumptions depending on the design and conduct of the trial.  When most 
subjects are followed up even after the respective intercurrent event (e.g., discontinuation of 
treatment), the remaining problem of missing data may be relatively minor.  In contrast, when 
observation is terminated after an intercurrent event, which is obviously undesirable in respect to 
this strategy, the assumption that (unobserved) outcomes for discontinuing subjects are similar to 
the (observed) outcomes for those who remain on treatment will often be implausible.  An 
alternative approach to handle the missing data should be justified, and sensitivity analysis will 
be expected. 
 
Analysis aligned to a hypothetical strategy involves outcomes different from those actually 
observed, for example, outcomes if rescue medication had not been given when in fact it was.  
Observations before the rescue medication and observations on subjects who did not need rescue 
medication may be informative, but only under strong assumptions.  
 
A composite variable strategy can avoid statistical assumptions about data after an intercurrent 
event by considering occurrence of the intercurrent event as a component of the outcome.  The 
potential concern relates less to assumptions for estimation, and more to the interpretation of the 
estimated treatment effect.  For the estimand to be interpretable, if scores are assigned for failure 
because the intercurrent event occurs, these should meaningfully reflect the lack of benefit to the 
patient (e.g., death may be reflected differently than discontinuation of treatment because of 
adverse event). 
 
Estimands constructed based on a while-on-treatment strategy can be estimated provided 
outcomes are collected up to the time of the intercurrent event.  Again, the crucial assumptions 
concern interpretation.  Consider, for example, discontinuation of treatment.  Outcomes while on 
treatment may be improved, but the treatment may also shorten or lengthen the treatment period 
by provoking or delaying discontinuations.  Both these effects should be considered in 
interpreting and assessing clinical benefit. 
  15 Contains Nonbinding Recommendations 
 
 
Analysis aligned to a principal stratum strategy usually requires strong assumptions.  For 
example, some principal stratification methods infer this from baseline characteristics of the 
subjects, but the correctness of this inference may be difficult to assess.  This difficulty cannot be 
avoided by simplified methods, however.  For example, simply comparing subjects who do not 
have an intercurrent event on the test treatment to those who do not have an event on control, 
assuming intercurrent events are unrelated to treatment, is very difficult to justify. 
 
Even after defining estimands that address intercurrent events in an appropriate manner and 
making efforts to collect the data needed for estimation (see section IV (A.4)), some data may 
still be missing, including, e.g., administrative censoring in trials with survival outcomes.  
Failure to collect relevant data should not be confused with the choice not to collect, or to collect 
and not to use, data made irrelevant by an intercurrent event.  For example, data that were 
intended to be collected after discontinuation of trial medication to inform an estimand based on 
the treatment policy strategy are missing if uncollected; however, the same data points might be 
irrelevant for another strategy, and therefore, for the purpose of that second estimand, are not 
missing if uncollected.  When efforts to collect data are not successful, it becomes necessary to 
make assumptions to handle the missing data in the statistical analysis.  Handling of missing data 
should be based on clinically plausible assumptions and, where possible, guided by the strategies 
employed in the description of the estimand.  The approach taken may be based on observed 
covariates and post-baseline data from individual subjects and from other similar subjects.  
Criteria to identify similar subjects might include whether or not the intercurrent event has 
occurred.  For example, for subjects who discontinue treatment without further data being 
collected, a model may use data from other subjects who discontinued treatment but for whom 
data collection has continued. 
 
B.     Sensitivity Analysis (A.5.2) 
 
1.    Role of Sensitivity Analysis (A.5.2.1) 
 
Inferences based on a particular estimand should be robust to limitations in the data and 
deviations from the assumptions used in the statistical model for the main estimator.  This 
robustness is evaluated through a sensitivity analysis.  Sensitivity analysis should be planned for 
the main estimators of all estimands that will be important for regulatory decision-making and 
labeling in the product information.  This can be a topic for discussion and agreement between 
sponsor and regulator. 
 
The statistical assumptions that underpin the main estimator should be documented.  One or 
more analyses, focused on the same estimand, should then be prespecified to investigate these 
assumptions with the objective of verifying whether the estimate derived from the main 
estimator is robust to departures from its assumptions.  This might be characterized as the extent 
of departures from assumptions that change the interpretation of the results in terms of their 
statistical or clinical significance (e.g., tipping point analysis). 
 
Distinct from sensitivity analysis, when investigations are conducted with the intent of exploring 
robustness of departures from assumptions, other analyses that are conducted to more fully 
  16 Contains Nonbinding Recommendations 
 
investigate and understand the trial data can be termed supplementary analysis (see Glossary; 
section V.C (A.5.3)).  When the primary estimand(s) of interest are agreed upon between sponsor 
and regulator, the main estimator is prespecified unambiguously, and the sensitivity analysis 
verifies that the estimate derived is reliable for interpretation, supplementary analyses should 
generally be given lower priority in assessment. 
 
2.     Choice of Sensitivity Analysis (A.5.2.2) 
 
When planning and conducting a sensitivity analysis, altering multiple aspects of the main 
analysis simultaneously can make it challenging to identify which assumptions, if any, are 
responsible for any potential differences seen.  It is therefore desirable to adopt a structured 
approach, specifying the changes in assumptions that underlie the alternative analyses, rather 
than simply comparing the results of different analyses based on different sets of assumptions.  
The need for analyses varying multiple assumptions simultaneously should then be considered 
on a case-by-case basis.  A distinction between testable and untestable assumptions may be 
useful when assessing the interpretation and relevance of different analyses. 
 
The importance of sensitivity analysis in respect to missing data is established and retains its 
importance in this framework.  Missing data should be defined and considered in respect to a 
particular estimand (see section IV (A.4)).  The distinction between data that are missing in 
respect to a specific estimand and data that are not directly relevant to a specific estimand gives 
rise to separate sets of assumptions to be examined in sensitivity analysis.   
 
C.  Supplementary Analysis (A.5.3) 
  
Interpretation of trial results should focus on the main estimator for each agreed-upon estimand, 
providing that the corresponding estimate is verified to be robust through the sensitivity analysis.  
Supplementary analyses for an estimand can be conducted in addition to the main and sensitivity 
analysis to provide additional insights into the understanding of the treatment effect.  They 
generally play a lesser role for interpretation of trial results.  The need for, and utility of, 
supplementary analyses should be considered for each trial. 
 
Section V.B.3 (5.2.3) of ICH E9 indicates that it is usually appropriate to plan for analyses based 
on both the FAS and the Per Protocol Set (PPS) so that differences between them can be the 
subject of explicit discussion and interpretation.  Consistent results from analyses based on the 
FAS and the PPS is indicated as increasing confidence in the trial results.  It is also described in 
Section V.B.2. (5.2.2) that results based on a PPS might be subject to severe bias.  In respect to 
the framework presented in this addendum, it may not be possible to construct a relevant 
estimand to which analysis of the PPS is aligned.  As noted above, analysis of the PPS does not 
achieve the goal of estimating the effect in any principal stratum, for example, in those subjects 
able to tolerate and continue to take the test treatment, because it may not compare similar 
subjects on different treatments.   
 
Protocol violations and deviations might exclude subjects from the PPS, for example, by having 
a visit outside a time window, without an intercurrent event necessarily having occurred.  
Likewise, subjects could experience an intercurrent event, such as death, without having deviated 
  17 Contains Nonbinding Recommendations 
 
from the protocol.  Notwithstanding the differences between violations and deviations from the 
protocol and intercurrent events, events likely to affect the interpretation or existence of 
measurements are considered in the description of the estimand.  Estimands might be constructed 
with aligned methods of analysis that better address the objective usually associated with the 
analysis of the PPS.  If so, analysis of the PPS might not add additional insights. 
 
VI.  DOCUMENTING ESTIMANDS AND SENSITIVITY ANALYSIS (A.6) 
 
A trial protocol should define and specify explicitly a primary estimand that corresponds to the 
primary trial objective.  The protocol and the analysis plan should prespecify the main estimator 
that is aligned with the primary estimand and leads to the primary analysis, together with a 
suitable sensitivity analysis to explore the robustness under deviations from its assumptions.  
Estimands for secondary trial objectives (e.g., related to secondary variables) that are likely to 
support regulatory decisions should also be defined and specified explicitly, each with a 
corresponding main estimator and a suitable sensitivity analysis.  Additional exploratory trial 
objectives may be considered for exploratory purposes, leading to additional estimands. 
 
The choice of the primary estimand will usually be the main determinant for aspects of trial 
design, conduct, and analysis.  Following usual practices, these aspects should be well 
documented in the trial protocol.  If secondary estimands are of key interest, these considerations 
may be extended to support these as needed and should be documented as well.  Beyond these 
aspects, the conventional considerations for trial design, conduct, and analysis remain the same. 
 
Although it is to the benefit of the sponsor to have clarity on what is being estimated, it is not a 
regulatory requirement to document an estimand for each exploratory objective. 
 
Results from the main, sensitivity, and supplementary analyses should be reported systematically 
in the clinical trial report, specifying whether each analysis was prespecified, introduced while 
the trial was still blinded, or performed post hoc.  Summaries of the number and timings of each 
intercurrent event in each treatment group should be reported. 
 
Changes to the estimand during the trial can be problematic and can reduce the credibility of the 
trial.  Addressing intercurrent events that were not foreseen at the design stage, and are identified 
during the conduct of the trial, should discuss not only the choices made for the analysis, but the 
effect on the estimand (i.e., on the description of the treatment effect that is being estimated) and 
the interpretation of the trial results.  A change to the estimand should usually be reflected 
through amendment to the protocol. 
   
  18 Contains Nonbinding Recommendations 
 
GLOSSARY 
Estimand: 
A precise description of the treatment effect reflecting the clinical question posed by the trial 
objective.  It summarizes at a population-level what the outcomes would be in the same patients 
under different treatment conditions being compared. 
 
Estimate: 
A numerical value computed by an estimator. 
 
Estimator: 
A method of analysis to compute an estimate of the estimand using clinical trial data. 
 
Intercurrent Events: 
Events occurring after treatment initiation that affect either the interpretation or the existence of 
the measurements associated with the clinical question of interest.  Intercurrent events should be 
addressed when describing the clinical question of interest to precisely define the treatment 
effect that is to be estimated. 
 
Missing Data: 
Data that would be meaningful for the analysis of a given estimand but were not collected.  They 
should be distinguished from data that do not exist or data that are not considered meaningful 
because of an intercurrent event. 
 
Principal Stratification: 
Classification of subjects according to the potential occurrence of an intercurrent event on all 
treatments.  With two treatments, there are four principal strata with respect to a given 
intercurrent event: subjects who would not experience the event on either treatment, subjects 
who would experience the event on treatment A but not B, subjects who would experience the 
event on treatment B but not A, and subjects who would experience the event on both treatments.   
 
Principal Stratum: 
In this document, a principal stratum refers to any of the strata (or combination of strata) defined 
by principal stratification. 
 
Sensitivity Analysis: 
A series of analyses conducted with the intent to explore the robustness of inferences from the 
main estimator to deviations from its underlying modeling assumptions and limitations in the 
data. 
 
Supplementary Analysis: 
A general description for analyses that are conducted in addition to the main and sensitivity 
analysis with the intent to provide additional insights into the understanding of the treatment 
effect.   
 
  19