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Since the publication of ICH E9 (R1), "Addendum to statistical principles for clinical trials: on choosing appropriate estimands and defining sensitivity analyses in clinical trials," there has been a lot of debate about the hypothetical strategy for handling intercurrent events. Arguments against the hypothetical strategy are twofold: (1) the clinical question has limited clinical/regulatory interest; (2) the estimation may need strong statistical assumptions. In this article, we provide an example of a hypothetical strategy handling use of rescue medications in the acute pain setting. We argue that the treatment effect of a drug that is attributable to the treatment alone is the clinical question of interest and is important to regulators. The hypothetical strategy is important when developing non‐opioid treatment as it estimates the treatment effect due to treatment during the pre‐specified evaluation period whereas the treatment policy strategy does not. Two widely acceptable and non‐controversial clinical inputs are required to construct a reasonable estimator. More importantly, this estimator does not rely on additional strong statistical assumptions and is considered reasonable for regulatory decision making. In this article, we point out examples where estimators for a hypothetical strategy can be constructed without any strong additional statistical assumptions besides acceptable clinical inputs. We also showcase a new way to obtain estimation based on disease specific clinical knowledge instead of strong statistical assumptions. In the example presented, we clearly demonstrate the advantages of the hypothetical strategy compared to alternative strategies including the treatment policy strategy and a composite variable strategy. [ABSTRACT FROM AUTHOR]

Since the introduction of the estimand in therapeutical studies, several adaptions have been developed. This short article highlights the important aspects of the estimand concept. A literature research was conducted to identify different extensions to this framework. Different modified strategies for intercurrent events are presented, as well as examples of methods to implement the estimand in clinical studies. The article reflects that the estimand is an ongoing research field with further exploration. [ABSTRACT FROM AUTHOR]

According to recent regulatory guidance, clinical trial objectives should be translated into estimands, i.e., precise descriptions of that what is to be estimated. Hence, estimands are to be formulated for influenza vaccine immunogenicity (IVI) trials, notably for one of the most popular immunogenicity trials, the non-inferiority trial. In this paper an estimand for this trial design is proposed. An estimand should state how intercurrent events are handled. Intercurrent events are events that occur after the start of the trial and that affect the endpoint's measurement or interpretation or prevent its observation. In IVI trials the intercurrent events of interest are immunological intercurrent events (IIEs). Major IIEs are identified, i.e., protocol deviations occurring during the trial that affect immunogenicity endpoints, and the consequences for the trial data collection are discussed. In the statistical analysis endpoint values that are missing or excluded from the analysis due to IIEs are to be substituted by plausible values, by means of multiple imputing. Replacing values are based on predictors of the endpoint. A distinction is made between mandatory and non-mandatory predictors. Mandatory predictors are predictors necessary to prevent biased prediction. Non-mandatory predictors are predictors that limit the additional variance due to the imputing. The four steps of the multiple imputing are explained, and available software is listed. [ABSTRACT FROM AUTHOR]

Background: The estimands framework represents a significant innovation for the design, conduct, analysis, and interpretation of clinical trials. An aim of the framework is to increase precision and transparency on the handling of intercurrent events (IEs), defined as events occurring after treatment initiation and affecting the endpoint. While the experience in constructing and reporting estimands in the published literature is limited, developers performing confirmatory studies are already making use of the new paradigm, allowing to survey the strategies proposed by applicants and endorsed by regulators. Methods: To identify strategies for handling IEs in confirmatory central nervous system (CNS) trials, we searched scientific advice letters issued by the European Medicines Agency (EMA) between 2017 and 2022. We developed a categorisation of the IEs and classified, according to the strategies defined in the framework, the strategies proposed by the Applicants and recommended by the agency. Strategies proposed and recommended were summarised by category of IEs, and the rationale for the choices was analysed qualitatively. Results: In total, 170 IEs were identified in 52 confirmatory trials. A clear preference for the treatment policy strategy for treatment discontinuation and for the hypothetical strategy for pandemic-related disruptions was identified. For other categories of IEs, there are more mixed patterns. Discussion: This study highlights the multidimensional nature of choosing a strategy for an IE. For different occurring IEs in confirmatory CNS trials different strategies are of regulatory interest, depending on the trial objective, underlying disease properties, rarity of disease, as well as frequency and timing of IEs and their relatedness to the disease.

Abstract Background The estimand for a clinical trial is a precise definition of the treatment effect to be estimated. Traditionally, estimates of treatment effects are based on either an ITT analysis or a per-protocol analysis. However, there are important clinical questions which are not addressed by either of these analyses. For example, consider a trial where patients take a rescue medication. The ITT analysis includes data after use of rescue, while the per-protocol analysis excludes these patients altogether. Neither of these analyses addresses the important question of what the treatment effect would have been if patients did not take rescue medication. Main text Trial estimands provide a broader perspective compared to the limitations of ITT and per-protocol analysis. Trial treatment effects depend on how events occurring after treatment initiation such as use of alternative medication or discontinuation of the intervention are included in the definition. These events can be accounted for in different ways, depending on the clinical question of interest. Conclusion The estimand framework is an important step forward in improving the clarity and transparency of clinical trials. The centrality of estimands to clinical trials is currently not reflected in methods recommended by the Cochrane group or the CONSORT statement, the current standard for reporting clinical trials in medical journals. We encourage revisions to these guidelines.

Two large‐scale randomized clinical trials compared fenofibrate and placebo in diabetic patients with pre‐existing retinopathy (FIELD study) or risk factors (ACCORD trial) on an intention‐to‐treat basis and reported a significant reduction in the progression of diabetic retinopathy in the fenofibrate arms. However, their analyses involved complications due to intercurrent events, that is, treatment‐switching and interval‐censoring. This article addresses these problems involved in estimation of causal effects of long‐term use of fibrates in a cohort study that followed patients with type 2 diabetes for 8 years. We propose structural nested mean models (SNMMs) of time‐varying treatment effects and pseudo‐observation estimators for interval‐censored data. The first estimator for SNMMs uses a nonparametric maximum likelihood estimator (MLE) as a pseudo‐observation, while the second estimator is based on MLE under a parametric piecewise exponential distribution. Through numerical studies with real and simulated datasets, the pseudo‐observations estimators of causal effects using the nonparametric Wellner–Zhan estimator perform well even under dependent interval‐censoring. Its application to the diabetes study revealed that the use of fibrates in the first 4 years reduced the risk of diabetic retinopathy but did not support its efficacy beyond 4 years. [ABSTRACT FROM AUTHOR]

Abstract Recent examples for unplanned external events are the global COVID-19 pandemic, the war in Ukraine, or most recently Hurricane Ian in Puerto Rico. Disruptions due to unplanned external events can lead to violation of assumptions in clinical trials. In certain situations, randomization tests can provide nonparametric inference that is robust to violation of the assumptions usually made in clinical trials. The ICH E9 (R1) Addendum on estimands and sensitivity analyses provides a guideline for aligning the trial objectives with strategies to address disruptions in clinical trials. In this article, we embed randomization tests within the estimand framework to allow for inference following disruptions in clinical trials in a way that reflects recent literature. A stylized clinical trial is presented to illustrate the method, and a simulation study highlights situations when a randomization test that is conducted under the intention-to-treat principle can provide unbiased results. [ABSTRACT FROM AUTHOR]

Background: The estimand for a clinical trial is a precise definition of the treatment effect to be estimated. Traditionally, estimates of treatment effects are based on either an ITT analysis or a per-protocol analysis. However, there are important clinical questions which are not addressed by either of these analyses. For example, consider a trial where patients take a rescue medication. The ITT analysis includes data after use of rescue, while the per-protocol analysis excludes these patients altogether. Neither of these analyses addresses the important question of what the treatment effect would have been if patients did not take rescue medication. Main text: Trial estimands provide a broader perspective compared to the limitations of ITT and per-protocol analysis. Trial treatment effects depend on how events occurring after treatment initiation such as use of alternative medication or discontinuation of the intervention are included in the definition. These events can be accounted for in different ways, depending on the clinical question of interest. Conclusion: The estimand framework is an important step forward in improving the clarity and transparency of clinical trials. The centrality of estimands to clinical trials is currently not reflected in methods recommended by the Cochrane group or the CONSORT statement, the current standard for reporting clinical trials in medical journals. We encourage revisions to these guidelines. [ABSTRACT FROM AUTHOR]

Estimands play an important role for aligning study objectives, study design and analyses through a precise definition of the quantity of interest. For COVID-19 studies, apart from intercurrent events, high volume of missing data has been observed. We explore their impact on several estimands through a synthetic COVID-19 data generated from a discrete-time multi-state model. We compare estimators of these estimands based on their ability to closely match the true response rates and retain assumed power. The final choice of the estimand then needs to be aligned with clinically meaningful quantities of interest to patients, clinicians, regulators and payers. [ABSTRACT FROM AUTHOR]

The ICH E9(R1) guidance on estimands is a key tool for the creation and review of protocol design and analysis planning, for both industry and regulatory statisticians. The framework has been described as useful for improving study design, intercurrent event handling, data collection, analysis, and interpretation to align the estimand with the primary clinical question to add clarity and precision to support regulatory decision-making. In this article, we describe our experience as regulatory statisticians in review of Investigational New Drug protocols and statistical analysis plans, with an emphasis on trials used to support substantial evidence of effectiveness in New Drug Applications and Biologic License Applications. Our intent is to describe our experience with this powerful and effective framework tool, to align the clinical trial's primary objective with its analysis outcomes and interpretation. [ABSTRACT FROM AUTHOR]

The ICH E9 (R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials (Step 4)" was finalized in November 2019 and subsequently implemented by many regulatory agencies, including FDA (May 2021). This article is based on a session organized to cover experience implementing the estimand framework, including its use, impact on drug/biologic development, common challenges and ways to address them, as well as keys to productive interdisciplinary collaboration. [ABSTRACT FROM AUTHOR]

Background: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a double-blind drug trial, some participants may not receive any dose of study medication. Many trials use a 'modified intention-to-treat' approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear (a) the estimand being targeted by such an approach and (b) the assumptions necessary for such an approach to be unbiased. Methods: Using potential outcome notation, we demonstrate that a modified intention-to-treat analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The modified intention-to-treat estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm (i.e. if someone began the intervention, they would also have begun the control, and vice versa). Results: We identify two key criteria in determining whether the modified intention-to-treat estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, as the decision to start treatment cannot be influenced by the allocation, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well, implying that a modified intention-to-treat analysis which excludes participants who do not begin treatment is an unbiased estimator for the principal stratum effect in these settings. We also give two examples where these criteria will not be satisfied (one comparing an active intervention vs usual care, where we cannot identify which usual care participants would have initiated the active intervention, and another comparing two active interventions in an unblinded manner, where knowledge of the assigned treatment arm may affect the participant's choice to begin or not), implying that a modified intention-to-treat estimator will be biased in these settings. Conclusion: A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not. [ABSTRACT FROM AUTHOR]

Treatment effects on recurrent events are of primary clinical interest in many diseases such as asthma, multiple sclerosis or chronic heart failure. When death makes it impossible to experience further events, defining an appropriate measure of the treatment effect, the estimand, is challenging. For example in chronic heart failure, patients may experience repeated hospitalizations, but are also at an increased risk of death. For a test treatment which reduces mortality compared to a control treatment, one may observe more hospitalizations under the test treatment simply because patients with a high risk of hospitalizations may die earlier under the control treatment. Many statistical analysis procedures have been proposed for such data, however it is often unclear which estimands these imply. As emphasized in the causal inference literature and the E9(R1) guideline by the International Council of Harmonization, the definition of the estimand of interest should precede any statistical analysis. We focus here on estimands for recurrent events terminated by death that have a causal interpretation. We also discuss whether common statistical analyses imply causal estimands of interest. Chronic heart failure is used as a motivating example. [ABSTRACT FROM AUTHOR]

This research was motivated by studying anti-drug antibody (ADA) formation and its potential impact on long-term benefit of a biologic treatment in a randomized controlled trial, in which ADA status was not only unobserved in the control arm but also in a subset of patients from the experimental treatment arm. Recent literature considers the principal stratum estimand strategy to estimate treatment effect in groups of patients defined by an intercurrent status, that is, in groups defined by a post-randomization variable only observed in one arm and potentially associated with the outcome. However, status information might be missing even for a nonnegligible number of patients in the experimental arm. For this setting, a novel weighted principal stratum approach, namely weighted imputation regression (WRI), is presented: Data from patients with missing intercurrent event status were re-weighted based on baseline covariates and additional longitudinal information. A theoretical justification of the WRI method is provided for different types of outcomes, and assumptions allowing for causal conclusions on treatment effect are specified and investigated. Simulations demonstrated that the WRI method yielded valid inference and was robust against certain violations of assumptions. The method was shown to perform well in a clinical study with ADA status as an intercurrent event. [ABSTRACT FROM AUTHOR]

Estimands aim to incorporate intercurrent events in design, data collection and estimation of treatment effects in clinical trials. Our aim was to understand what estimands may correspond to efficacy analyses commonly employed in clinical trials conducted before publication of ICH E9(R1). We re‐analysed six clinical trials evaluating a new anti‐depression treatment. We selected the following analysis methods—ANCOVA on complete cases, following last observation carried forward (LOCF) imputation and following multiple imputation; mixed‐models for repeated measurements without imputation (MMRM), MMRM following LOCF imputation and following jump‐to‐reference imputation; and pattern‐mixture mixed models. We included a principal stratum analysis based on the predicted subset of the study population who would not discontinue due to adverse events or lack of efficacy. We translated each analysis into the implicitly targeted estimand, and formulated corresponding clinical questions. We could map six estimands to analysis methods. The same analysis method could be mapped to more than one estimand. The major difference between estimands was the strategy for intercurrent events, with other attributes mostly the same across mapped estimands. The quantitative differences in MADRS10 population‐level summaries between the estimands were 4–8 points. Not all six estimands had a clinically meaningful interpretation. Only a few analyses would target the same estimand, hence only few could be used as sensitivity analyses. The fact that an analysis could estimate different estimands emphasises the importance of prospectively defining the estimands targeting the primary objective of a trial. The fact that an estimand can be targeted by different analyses emphasises the importance of prespecifying precisely the estimator for the targeted estimand. [ABSTRACT FROM AUTHOR]

Context: Missing data are common in hospice/palliative care randomized trials due to high drop-out because of the demographic of interest. It can introduce bias in the estimate of the treatment effect and its precision.Objectives: The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) released updated guidance on statistical principles for clinical trials introducing the estimand framework to align trial objectives, trial conduct, statistical analysis and interpretation of results. Our objective is to present how the estimand framework can be used to guide the handling of missing data in palliative care trials.Methods: We outline the estimand framework by highlighting the five elements of an estimand: treatment, population, variable, summary measure and intercurrent event handling. We list common intercurrent events in palliative care trials and present the five strategies for handling intercurrent events outlined in the ICH guidance.Results: We describe common intercurrent events in palliative care trials and discuss and justify what analytic strategies could be followed with each. We provide an example using a palliative care trial comparing two opioids for pain relieve in participants with cancer pain.Conclusion: When planning a palliative care trial, the estimand should be explicitly stated, including how intercurrent events will be handled in the analysis. This should be informed by the scientific objectives of the trial. The estimand guides the handling of missing data during the conduct and analysis of the trial. Defining an estimand is not a statistical activity, but a multi-disciplinary process involving all stakeholders. [ABSTRACT FROM AUTHOR]

Abstract Background An estimand defines the target of estimation for a clinical trial through specification of the treatment, target population, variable, population-level summary and of the strategies for intercurrent events. A carefully defined estimand aligns the clinical trial design and analysis with the scientific question of interest and adequately accounts for so-called intercurrent events. The ICH E9(R1) addendum suggests five estimand strategies. We evaluated to what extent current practice in drug development and regulatory assessment fits in the estimand framework. Methods We systematically evaluated what estimands, especially what strategies for intercurrent events are advised in European Medicines Agency disease guidelines, used in sponsors’ trials and additionally requested by the European Medicines Agency in assessment dossiers. We selected four therapeutic areas: nervous system, oncology, cardiovascular diseases and respiratory diseases. For each, we evaluated all disease guidelines with approved drugs, the dossiers of the most recently approved drugs matching the guidelines and corresponding regulatory questions. Results Strategies for intercurrent events were present in 18 (53%) of 34 guidelines, in all 34 sponsor documentations and in 15 (44%) of 34 sets of regulatory questions. Treatment policy was advised in 13 (38%) guidelines and was applied in 9 corresponding sponsor documentations. Of these 9, it was the sole strategy in 4 cases and accompanied by another strategy in 5 cases. Hypothetical strategy was not advised in guidelines. However, it was the leading strategy applied in 25 (74%) sponsor documentations. Composite strategy was advised in 3 (9%) guidelines and applied accompanied by another strategy in 2 corresponding sponsor documentations. While on treatment strategy was not advised in guidelines, but was applied in 2 sponsor documentations. Principal stratum strategy was advised in 2 guidelines but not applied in corresponding sponsor documentations. Of the regulatory questions, treatment policy was present in 2 cases (6%), hypothetical in 6 cases (18%), composite in 6 cases (18%) and while on treatment in 1 case (3%). Conclusions Estimand attributes are present in guidelines, sponsor documentations and regulatory questions, but not described as estimands. Treatment policy was most often advised in guidelines, but hypothetical was the leading strategy applied in sponsor documentations. Thus, results indicate not a full concordance between the regulatory target of estimation and what is actually estimated. The lack of concordance was mostly due to limitations in collection of intercurrent events data to enable a treatment policy strategy. There is, therefore, a need to better define estimands at the design stage and throughout the applications dossiers and assessment reports.

A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post‐randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called intercurrent events in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post‐hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions. [ABSTRACT FROM AUTHOR]

The win ratio is a general method of comparing locations of distributions of two independent, ordinal random variables, and it can be estimated without distributional assumptions. In this paper we provide a unified theory of win ratio estimation in the presence of stratification and adjustment by a numeric variable. Building step by step on the estimate of the crude win ratio we compare corresponding tests with well known non-parametric tests of group difference (Wilcoxon rank-sum test, Fligner–Policello test, van Elteren test, test based on the regression on ranks, and the rank analysis of covariance test). We show that the win ratio gives an interpretable treatment effect measure with corresponding test to detect treatment effect difference under minimal assumptions. [ABSTRACT FROM AUTHOR]

Background: The estimands framework represents a significant innovation for the design, conduct, analysis, and interpretation of clinical trials. An aim of the framework is to increase precision and transparency on the handling of intercurrent events (IEs), defined as events occurring after treatment initiation and affecting the endpoint. While the experience in constructing and reporting estimands in the published literature is limited, developers performing confirmatory studies are already making use of the new paradigm, allowing to survey the strategies proposed by applicants and endorsed by regulators., Methods: To identify strategies for handling IEs in confirmatory central nervous system (CNS) trials, we searched scientific advice letters issued by the European Medicines Agency (EMA) between 2017 and 2022. We developed a categorisation of the IEs and classified, according to the strategies defined in the framework, the strategies proposed by the Applicants and recommended by the agency. Strategies proposed and recommended were summarised by category of IEs, and the rationale for the choices was analysed qualitatively., Results: In total, 170 IEs were identified in 52 confirmatory trials. A clear preference for the treatment policy strategy for treatment discontinuation and for the hypothetical strategy for pandemic-related disruptions was identified. For other categories of IEs, there are more mixed patterns., Discussion: This study highlights the multidimensional nature of choosing a strategy for an IE. For different occurring IEs in confirmatory CNS trials different strategies are of regulatory interest, depending on the trial objective, underlying disease properties, rarity of disease, as well as frequency and timing of IEs and their relatedness to the disease., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Abstract–The COVID-19 pandemic has a global impact on the conduct of clinical trials of medical products. This article discusses implications of the COVID-19 pandemic on clinical research methodology aspects and provides points to consider to assess and mitigate the risk of seriously compromising the integrity and interpretability of clinical trials. The information in this article will support discussions that need to occur cross-functionally on an ongoing basis to "integrate all available knowledge from the ethical, the medical, and the methodological perspective into decision making." This article aims at facilitating: (i) risk assessments of the impact of the pandemic on trial integrity and interpretability; (ii) identification of the relevant data and information related to the impact of the pandemic on the trial that needs to be collected; (iii) short-term decision making impacting ongoing trial operations; (iv) ongoing monitoring of the trial conduct until completion, including the possible involvement of data monitoring committees, and adequately documenting all measures taken to secure trial integrity throughout and after the pandemic, and (v) proper analysis and interpretation of the eventual interim or final trial data. [ABSTRACT FROM AUTHOR]

Background: An estimand defines the target of estimation for a clinical trial through specification of the treatment, target population, variable, population-level summary and of the strategies for intercurrent events. A carefully defined estimand aligns the clinical trial design and analysis with the scientific question of interest and adequately accounts for so-called intercurrent events. The ICH E9(R1) addendum suggests five estimand strategies. We evaluated to what extent current practice in drug development and regulatory assessment fits in the estimand framework.Methods: We systematically evaluated what estimands, especially what strategies for intercurrent events are advised in European Medicines Agency disease guidelines, used in sponsors' trials and additionally requested by the European Medicines Agency in assessment dossiers. We selected four therapeutic areas: nervous system, oncology, cardiovascular diseases and respiratory diseases. For each, we evaluated all disease guidelines with approved drugs, the dossiers of the most recently approved drugs matching the guidelines and corresponding regulatory questions.Results: Strategies for intercurrent events were present in 18 (53%) of 34 guidelines, in all 34 sponsor documentations and in 15 (44%) of 34 sets of regulatory questions. Treatment policy was advised in 13 (38%) guidelines and was applied in 9 corresponding sponsor documentations. Of these 9, it was the sole strategy in 4 cases and accompanied by another strategy in 5 cases. Hypothetical strategy was not advised in guidelines. However, it was the leading strategy applied in 25 (74%) sponsor documentations. Composite strategy was advised in 3 (9%) guidelines and applied accompanied by another strategy in 2 corresponding sponsor documentations. While on treatment strategy was not advised in guidelines, but was applied in 2 sponsor documentations. Principal stratum strategy was advised in 2 guidelines but not applied in corresponding sponsor documentations. Of the regulatory questions, treatment policy was present in 2 cases (6%), hypothetical in 6 cases (18%), composite in 6 cases (18%) and while on treatment in 1 case (3%).Conclusions: Estimand attributes are present in guidelines, sponsor documentations and regulatory questions, but not described as estimands. Treatment policy was most often advised in guidelines, but hypothetical was the leading strategy applied in sponsor documentations. Thus, results indicate not a full concordance between the regulatory target of estimation and what is actually estimated. The lack of concordance was mostly due to limitations in collection of intercurrent events data to enable a treatment policy strategy. There is, therefore, a need to better define estimands at the design stage and throughout the applications dossiers and assessment reports. [ABSTRACT FROM AUTHOR]

Draft ICH E9(R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials" provides different strategies for addressing intercurrent events in defining an estimand and describing the treatment effect that is targeted. The set of considered intercurrent events will depend on the specific therapeutic setting and trial objectives. This article considers a case study of a long-term prevention trial investigating the treatment effect of a new drug in asymptomatic subjects who are at risk for developing Alzheimer's dementia to illustrate the definition of different estimands, which correspond to different scientific questions of interest. The potential intercurrent events are identified. This article shows how the selection of various strategies for intercurrent events translates into different estimators. A simulation investigation is included, which explores the properties of several estimators aligned with estimands that apply a treatment policy strategy for the intercurrent event of treatment discontinuation. Different scenarios are considered for the on-treatment versus the off-treatment mean efficacy trajectory, under a potential range of retrieval rates of off-treatment data. This simulation exercise illustrates how the selection of the estimators for an estimand could have a strong impact on the estimates of the treatment effect and, consequently, on the decision making in a clinical trial. [ABSTRACT FROM AUTHOR]

In the second half of 2014, the Steering Committee of the International Council for Harmonisation endorsed the formation of an expert working group to develop an addendum to the International Council for Harmonisation E9 guideline (Statistical Principles for Clinical Trials). The addendum was to focus on two clinical trial topics: estimands and sensitivity analysis. A draft of the addendum, referred to as E9/R1, was developed by the expert working group and made available for public comments across the International Council for Harmonisation regions in the second half of 2017. A structured framework for clinical trial design and analysis proposed in the draft addendum are briefly described, including four key inputs for developing objective-driven estimands and strategies for tackling one of the inputs ('intercurrent events'). The proposed framework aligns each clinical trial objective with the corresponding statistical target of estimation (estimand), trial design and data to be collected, main method of estimation/inference, and sensitivity analysis to pressure test key analytic assumption(s) in the main analysis. A case study from the diabetes therapeutic area illustrates how the framework can be implemented in practice. International Council for Harmonisation E9/R1 is expected to enable better planning, conduct, analysis, and interpretation of randomised clinical trials. This will facilitate improvements in new drug applications and strengthen understanding of decision making by regulatory authorities and advisory committees. [ABSTRACT FROM AUTHOR]

Missing data are inevitable in longitudinal clinical trials due to intercurrent events (ICEs) such as treatment interruption or premature discontinuation for different reasons. Missing at random (MAR) assumption is usually unverifiable and sensitivity analyses are often requested under missing not at random (MNAR) assumption. Return to baseline (RTB) imputation is a commonly used MNAR method. In practice, not all dropout missingness can be assumed MNAR. For example, missingness or dropouts due to COVID-19 can be reasonably assumed MAR. Therefore, traditional RTB is not applicable when there is both MAR and MNAR dropout missingness. Here we propose a hybrid strategy for RTB imputation which can handle missing data due to MAR and MNAR dropouts at the same time. Standard multiple imputation approach is proposed and an analytic likelihood based approach is derived to improve efficiency. [ABSTRACT FROM AUTHOR]

In longitudinal clinical trials, missing data are inevitable due to intercurrent events (ICEs) such as treatment interruption or premature discontinuation for different reasons. The COVID-19 pandemic has had substantial impact on clinical trials since early 2020 as it may result in missing data due to missed visits and premature discontinuations. The missing data due to COVID-19 can reasonably be assumed as missing at random (MAR). We propose a combined hypothetical strategy for sensitivity analyses to handle missing data due to both COVID-19 and non-COVID reasons. We modify the commonly used missing not at random (MNAR) methods, reference based imputation (RBI) and tipping point analysis, under this strategy. We propose the standard multiple imputation approach and derive an analytic likelihood based approach to implement the proposed methods to improve efficiency in applications. The proposed strategy and methods are applicable to a more general scenario when there are missing data due to both MAR and MNAR reasons. [ABSTRACT FROM AUTHOR]