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Hypothetical strategy is a common strategy for handling intercurrent events (IEs). No current guideline or study considers treatment–IE interaction to target the estimand in any one IE‐handling strategy. Based on the hypothetical strategy, we aimed to (1) assess the performance of three estimators with different considerations for the treatment–IE interaction in a simulation and (2) compare the estimation of these estimators in a real trial. Simulation data were generalized based on realistic clinical trials of Alzheimer's disease. The estimand of interest was the effect of treatment with no IE occurring under the hypothetical strategy. Three estimators, namely, G‐estimation with and without interaction and IE‐ignored estimation, were compared in scenarios where the treatment–IE interaction effect was set as −50% to 50% of the main effect. Bias was the key performance measure. The real case was derived from a randomized trial of methadone maintenance treatment. Only G‐estimation with interaction exhibited unbiased estimations regardless of the existence, direction or magnitude of the treatment–IE interaction in those scenarios. Neglecting the interaction and ignoring the IE would introduce a bias as large as 0.093 and 0.241 (true value, −1.561) if the interaction effect existed. In the real case, compared with G‐estimation with interaction, G‐estimation without interaction and IE‐ignored estimation increased the estimand of interest by 33.55% and 34.36%, respectively. This study highlights the importance of considering treatment–IE interaction in the estimand framework. In practice, it would be better to include the interaction in the estimator by default. [ABSTRACT FROM AUTHOR]

Clinical trials with random assignment of treatment provide evidence about causal effects of an experimental treatment compared to standard care. However, when disease processes involve multiple types of possibly semi-competing events, specification of target estimands and causal inferences can be challenging. Intercurrent events such as study withdrawal, the introduction of rescue medication, and death further complicate matters. There has been much discussion about these issues in recent years, but guidance remains ambiguous. Some recommended approaches are formulated in terms of hypothetical settings that have little bearing in the real world. We discuss issues in formulating estimands, beginning with intercurrent events in the context of a linear model and then move on to more complex disease history processes amenable to multistate modeling. We elucidate the meaning of estimands implicit in some recommended approaches for dealing with intercurrent events and highlight the disconnect between estimands formulated in terms of potential outcomes and the real world. [ABSTRACT FROM AUTHOR]

In clinical trials of longitudinal continuous outcomes, reference based imputation (RBI) has commonly been applied to handle missing outcome data in settings where the estimand incorporates the effects of intercurrent events, e.g. treatment discontinuation. RBI was originally developed in the multiple imputation framework, however recently conditional mean imputation (CMI) combined with the jackknife estimator of the standard error was proposed as a way to obtain deterministic treatment effect estimates and correct frequentist inference. For both multiple and CMI, a mixed model for repeated measures (MMRM) is often used for the imputation model, but this can be computationally intensive to fit to multiple data sets (e.g. the jackknife samples) and lead to convergence issues with complex MMRM models with many parameters. Therefore, a step-wise approach based on sequential linear regression (SLR) of the outcomes at each visit was developed for the imputation model in the multiple imputation framework, but similar developments in the CMI framework are lacking. In this article, we fill this gap in the literature by proposing a SLR approach to implement RBI in the CMI framework, and justify its validity using theoretical results and simulations. We also illustrate our proposal on a real data application. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The ICH E9 addendum outlining the estimand framework for clinical trials was published in 2019 but provides limited guidance around how to handle intercurrent events for non-inferiority studies. Once an estimand is defined, it is also unclear how to deal with missing values using principled analyses for non-inferiority studies. METHODS: Using a tuberculosis clinical trial as a case study, we propose a primary estimand, and an additional estimand suitable for non-inferiority studies. For estimation, multiple imputation methods that align with the estimands for both primary and sensitivity analysis are proposed. We demonstrate estimation methods using the twofold fully conditional specification multiple imputation algorithm and then extend and use reference-based multiple imputation for a binary outcome to target the relevant estimands, proposing sensitivity analyses under each. We compare the results from using these multiple imputation methods with those from the original study. RESULTS: Consistent with the ICH E9 addendum, estimands can be constructed for a non-inferiority trial which improves on the per-protocol/intention-to-treat-type analysis population previously advocated, involving respectively a hypothetical or treatment policy strategy to handle relevant intercurrent events. Results from using the twofold multiple imputation approach to estimate the primary hypothetical estimand, and using reference-based methods for an additional treatment policy estimand, including sensitivity analyses to handle the missing data, were consistent with the original studys reported per-protocol and intention-to-treat analysis in failing to demonstrate non-inferiority. CONCLUSIONS: Using carefully constructed estimands and appropriate primary and sensitivity estimators, using all the information available, results in a more principled and statistically rigorous approach to analysis. Doing so provides an accurate interpretation of the estimand.

Objective: The estimand framework offers a structured approach to define the treatment effect to be estimated in a clinical study. Defining the estimand upfront helps formulating the research question and informs study design, data collection and statistical analysis methods. Since the Trials within Cohorts (TwiCs) design has unique characteristics, the objective of this study is to describe considerations and provide guidance for formulating estimands for TwiCs studies.Methods: The key attributes of an estimand are the target population, treatments that are compared, the endpoint, intercurrent events and their handling, and the population-level summary measure. The estimand framework was applied retrospectively to two TwiCs studies: the SPONGE and UMBRELLA Fit trial. The aim is to demonstrate how the estimand framework can be implemented in TwiCs studies, thereby focusing on considerations relevant for defining the estimand. Three estimands were defined for both studies. For the SPONGE trial, estimators were derived.Results: Intercurrent events considered to occur exclusively or more frequently in TwiCs studies compared to conventional randomized trials included intervention refusal after randomization, misalignment of timing of routine cohort measurements and the intervention period, and participants in the control arm initiating treatments similar to the studied intervention. Considerations for handling refusal after randomization related to decisions on whether the target population should include all eligible participants or the subpopulation that would accept (or undergo) the intervention when offered. Considerations for handling treatment initiation in the control arm and misalignments of timing related to decisions on whether such events should be considered part of treatment policy or whether interest is in a hypothetical scenario where such events do not occur.Conclusion: The TwiCs study design has unique features that pose specific considerations when formulating an estimand. The examples in this study can provide guidance in the definition of estimands in future TwiCs studies.

Longitudinal clinical trials for which recurrent events endpoints are of interest are commonly subject to missing event data. Primary analyses in such trials are often performed assuming events are missing at random, and sensitivity analyses are necessary to assess robustness of primary analysis conclusions to missing data assumptions. Control‐based imputation is an attractive approach in superiority trials for imposing conservative assumptions on how data may be missing not at random. A popular approach to implementing control‐based assumptions for recurrent events is multiple imputation (MI), but Rubin's variance estimator is often biased for the true sampling variability of the point estimator in the control‐based setting. We propose distributional imputation (DI) with corresponding wild bootstrap variance estimation procedure for control‐based sensitivity analyses of recurrent events. We apply control‐based DI to a type I diabetes trial. In the application and simulation studies, DI produced more reasonable standard error estimates than MI with Rubin's combining rules in control‐based sensitivity analyses of recurrent events. [ABSTRACT FROM AUTHOR]

Background/aimsTuberculosis remains one of the leading causes of death from an infectious disease globally. Both choices of outcome definitions and approaches to handling events happening post-randomisation can change the treatment effect being estimated, but these are often inconsistently described, thus inhibiting clear interpretation and comparison across trials.MethodsStarting from the ICH E9(R1) addendum's definition of an estimand, we use our experience of conducting large Phase III tuberculosis treatment trials and our understanding of the estimand framework to identify the key decisions regarding how different event types are handled in the primary outcome definition, and the important points that should be considered in making such decisions. A key issue is the handling of intercurrent (i.e. post-randomisation) events (ICEs) which affect interpretation of or preclude measurement of the intended final outcome. We consider common ICEs including treatment changes and treatment extension, poor adherence to randomised treatment, re-infection with a new strain of tuberculosis which is different from the original infection, and death. We use two completed tuberculosis trials (REMoxTB and STREAM Stage 1) as illustrative examples. These trials tested non-inferiority of new tuberculosis treatment regimens versus a control regimen. The primary outcome was a binary composite endpoint, 'favourable' or 'unfavourable', which was constructed from several components.ResultsWe propose the following improvements in handling the above-mentioned ICEs and loss to follow-up (a post-randomisation event that is not in itself an ICE). First, changes to allocated regimens should not necessarily be viewed as an unfavourable outcome; from the patient perspective, the potential harms associated with a change in the regimen should instead be directly quantified. Second, handling poor adherence to randomised treatment using a per-protocol analysis does not necessarily target a clear estimand; instead, it would be desirable to develop ways to estimate the treatment effects more relevant to programmatic settings. Third, re-infection with a new strain of tuberculosis could be handled with different strategies, depending on whether the outcome of interest is the ability to attain culture negativity from infection with any strain of tuberculosis, or specifically the presenting strain of tuberculosis. Fourth, where possible, death could be separated into tuberculosis-related and non-tuberculosis-related and handled using appropriate strategies. Finally, although some losses to follow-up would result in early treatment discontinuation, patients lost to follow-up before the end of the trial should not always be classified as having an unfavourable outcome. Instead, loss to follow-up should be separated from not completing the treatment, which is an ICE and may be considered as an unfavourable outcome.ConclusionThe estimand framework clarifies many issues in tuberculosis trials but also challenges trialists to justify and improve their outcome definitions. Future trialists should consider all the above points in defining their outcomes.

In oncology trials, health-related quality of life (HRQoL), specifically patient-reported symptom burden and functional status, can support the interpretation of survival endpoints, such as progression-free survival. However, applying time-to-event endpoints to patient-reported outcomes (PRO) data is challenging. For example, in time-to-deterioration analyses clinical events such as disease progression are common in many settings and are often handled through censoring the patient at the time of occurrence; however, disease progression and HRQoL are often related leading to informative censoring. Special consideration to the definition of events and intercurrent events (ICEs) is necessary. In this work, we demonstrate time-to-deterioration of PRO estimands and sensitivity analyses to answer research questions using composite, hypothetical, and treatment policy strategies applied to a single endpoint of disease-related symptoms. Multiple imputation methods under both the missing-at-random and missing-not-at-random assumptions are used as sensitivity analyses of primary estimands. Hazard ratios ranged from 0.52 to 0.66 over all the estimands and sensitivity analyses modeling a robust treatment effect favoring the treatment in time to disease symptom deterioration or death. Differences in the estimands include how people who experience disease progression or discontinue the randomized treatment due to AEs are accounted for in the analysis. We use the estimand framework to define interpretable and principled approaches for different time-to-deterioration research questions and provide practical recommendations. Reporting the proportions of patient events and patient censoring by reason helps understand the mechanisms that drive the results, allowing for optimal interpretation. [ABSTRACT FROM AUTHOR]

Although clinical trials are often designed with randomization and well‐controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The pharmaceutical industry has been focused on developing new methods that can yield pertinent causal inferences in trials with ICEs. However, it is difficult to compare the properties of different methods developed in this endeavor as real‐life clinical trial data cannot be easily shared to provide benchmark data sets. Furthermore, different methods consider distinct assumptions for the underlying data‐generating mechanisms, and simulation studies often are customized to specific situations or methods. We develop a novel, general simulation model and corresponding Shiny application in R for clinical trials with ICEs, aptly named the Clinical Trials with Intercurrent Events Simulator (CITIES). It is formulated under the Rubin Causal Model where the considered treatment effects account for ICEs in clinical trials with repeated measures. CITIES facilitates the effective generation of data that resemble real‐life clinical trials with respect to their reported summary statistics, without requiring the use of the original trial data. We illustrate the utility of CITIES via two case studies involving real‐life clinical trials that demonstrate how CITIES provides a comprehensive tool for practitioners in the pharmaceutical industry to compare methods for the analysis of clinical trials with ICEs on identical, benchmark settings that resemble real‐life trials. [ABSTRACT FROM AUTHOR]

Background: The net benefit is an effect measure for any type of endpoint, including the time-to-event outcome, and can provide intuitive and clinically meaningful interpretation. It is defined as the probability of a randomly selected subject from the experimental arm surviving by at least a clinically relevant time longer than a randomly selected subject from the control arm. In oncology clinical trials, an intercurrent event such as treatment switching is common, which potentially causes informative censoring; nevertheless, conventional methods for the net benefit are not able to deal with it. In this study, we proposed a new estimator using the inverse probability of censoring weighting (IPCW) method and illustrated an oncology clinical trial with treatment switching (the SHIVA study) to apply the proposed method under the estimand framework. Methods: The net benefit can be estimated using the survival functions of each treatment group. The proposed estimator was based on the survival functions estimated by the inverse probability of the censoring weighting method that can handle covariate-dependent censoring. The simulation study was undertaken to evaluate the operating characteristics of the proposed estimator under several scenarios; we varied the shapes of the survival curves, treatment effect, covariates effect on censoring, proportion of the censoring, threshold of the net benefit, and sample size. We also applied conventional methods (the scoring rules by Péron or Gehan) and the proposed method to the SHIVA study. Results: Our simulation study showed that the proposed estimator provided less biased results under the covariate-dependent censoring than existing estimators. When applying the proposed method to the SHIVA study, we were able to estimate the net benefit by incorporating the information of the covariates with different estimand strategies to address the intercurrent event of the treatment switching. However, the estimates of the proposed method and those of the aforementioned conventional methods were similar under the hypothetical strategy. Conclusions: We proposed a new estimator of the net benefit that can include covariates to account for the possibly informative censoring. We also provided an illustrative analysis of the proposed method for the oncology clinical trial with treatment switching using the estimand framework. Our proposed new estimator is suitable for handling the intercurrent events that can potentially cause covariate-dependent censoring. [ABSTRACT FROM AUTHOR]

While the ICH E9(R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials" was released in late 2019, the widespread implementation of defining and reporting estimands across clinical trials is still in progress and the engagement of non-statistical functions in this process is also in progress. Case studies are sought after, especially those with documented clinical and regulatory feedback. This paper describes an interdisciplinary process for implementing the estimand framework, devised by the Estimands and Missing Data Working Group (a group with clinical, statistical, and regulatory representation) of the International Society for CNS Clinical Trials and Methodology. This process is illustrated by specific examples using various types of hypothetical trials evaluating a treatment for major depressive disorder. Each of the estimand examples follows the same template and features all steps of the proposed process, including identifying the trial stakeholder(s), the decisions they need to make about the investigated treatment in their specific role and the questions that would support their decision making. Each of the five strategies for handling intercurrent events are addressed in at least one example; the featured endpoints are also diverse, including continuous, binary and time to event. Several examples are presented that include specifications for a potential trial design, key trial implementation elements needed to address the estimand, and main and sensitivity estimator specifications. Ultimately this paper highlights the need to incorporate multi-disciplinary collaborations into implementing the ICH E9(R1) framework. [ABSTRACT FROM AUTHOR]

Over the past decades, the primary interest in vaccine efficacy or immunogenicity evaluation mostly focuses on the biological effect of immunization in complying with the vaccination schedule in a targeted population. The safety questions, which are essential for vaccines as they are generally given to large healthy populations, need to be clearly defined to reflect the risk assessment of interest. ICH E9 (R1) provides a structured framework to clarify the clinical questions and formulate the treatment effect as an estimand. This paper applies the estimand framework to vaccine clinical trials on common clinical questions regarding efficacy, immunogenicity, and safety. [ABSTRACT FROM AUTHOR]

Defining the right question of interest is important to a clinical study. ICH E9 (R1) introduces the framework of an estimand and its five attributes, which provide a basis for connecting different components of a study with its clinical questions. Most of the applications of the estimand framework focus on efficacy instead of safety assessment. In this paper, we expand the estimand framework into the safety evaluation and compare/contrast the similarity and differences between safety and efficacy estimand. Furthermore, we present and discuss applications of a safety estimand to oncology trials and pooled data analyses. At last, we also discuss the potential usage of safety estimand to handle the impacts of COVID-19 pandemic on safety assessment. [ABSTRACT FROM AUTHOR]

The International Council for Harmonization (ICH) E9(R1) addendum recommends choosing an appropriate estimand based on the study objectives in advance of trial design. One defining attribute of an estimand is the intercurrent event, specifically what is considered an intercurrent event and how it should be handled. The primary objective of a clinical study is usually to assess a product's effectiveness and safety based on the planned treatment regimen instead of the actual treatment received. The estimand using the treatment policy strategy, which collects and analyzes data regardless of the occurrence of intercurrent events, is usually utilized. In this article, we explain how missing data can be handled using the treatment policy strategy from the authors' viewpoint in connection with antihyperglycemic product development programs. The article discusses five statistical methods to impute missing data occurring after intercurrent events. All five methods are applied within the framework of the treatment policy strategy. The article compares the five methods via Markov Chain Monte Carlo simulations and showcases how three of these five methods have been applied to estimate the treatment effects published in the labels for three antihyperglycemic agents currently on the market. [ABSTRACT FROM AUTHOR]

The ICH E9 (R1) addendum proposes five strategies to define estimands by addressing intercurrent events. However, mathematical forms of these targeted quantities are lacking, which might lead to discordance between statisticians who estimate these quantities and clinicians, drug sponsors, and regulators who interpret them. To improve the concordance, we provide a unified four‐step procedure for constructing the mathematical estimands. We apply the procedure for each strategy to derive the mathematical estimands and compare the five strategies in practical interpretations, data collection, and analytical methods. Finally, we show that the procedure can help ease tasks of defining estimands in settings with multiple types of intercurrent events using two real clinical trials. [ABSTRACT FROM AUTHOR]

Background: Reasons for treatment discontinuation are important not only to understand the benefit and risk profile of experimental treatments, but also to help choose appropriate strategies to handle intercurrent events in defining estimands. The current case report form (CRF) commonly in use mixes the underlying reasons for treatment discontinuation and who makes the decision for treatment discontinuation, often resulting in an inaccurate collection of reasons for treatment discontinuation. Methods and results: We systematically reviewed and analyzed treatment discontinuation data from nine phase 2 and phase 3 studies for insulin peglispro. A total of 857 participants with treatment discontinuation were included in the analysis. Our review suggested that, due to the vague multiple-choice options for treatment discontinuation present in the CRF, different reasons were sometimes recorded for the same underlying reason for treatment discontinuation. Based on our review and analysis, we suggest an intermediate solution and a more systematic way to improve the current CRF for treatment discontinuations. Conclusion: This research provides insight and directions on how to optimize the CRF for recording treatment discontinuation. Further work needs to be done to build the learning into Clinical Data Interchange Standards Consortium standards. Clinical Trials: Clinicaltrials.gov numbers: NCT01027871 (Phase 2 for type 2 diabetes), NCT01049412 (Phase 2 for type 1 diabetes), NCT01481779 (IMAGINE 1 Study), NCT01435616 (IMAGINE 2 Study), NCT01454284 (IMAGINE 3 Study), NCT01468987 (IMAGINE 4 Study), NCT01582451 (IMAGINE 5 Study), NCT01790438 (IMAGINE 6 Study), NCT01792284 (IMAGINE 7 Study). [ABSTRACT FROM AUTHOR]

Intensity‐based multistate models provide a useful framework for characterizing disease processes, the introduction of interventions, loss to followup, and other complications arising in the conduct of randomized trials studying complex life history processes. Within this framework we discuss the issues involved in the specification of estimands and show the limiting values of common estimators of marginal process features based on cumulative incidence function regression models. When intercurrent events arise we stress the need to carefully define the target estimand and the importance of avoiding targets of inference that are not interpretable in the real world. This has implications for analyses, but also the design of clinical trials where protocols may help in the interpretation of estimands based on marginal features. [ABSTRACT FROM AUTHOR]

The ICH E9 addendum introduces the term intercurrent event to refer to events that happen after treatment initiation and that can either preclude observation of the outcome of interest or affect its interpretation. It proposes five strategies for handling intercurrent events to form an estimand but does not suggest statistical methods for estimation. In this article we focus on the hypothetical strategy, where the treatment effect is defined under the hypothetical scenario in which the intercurrent event is prevented. For its estimation, we consider causal inference and missing data methods. We establish that certain "causal inference estimators" are identical to certain "missing data estimators." These links may help those familiar with one set of methods but not the other. Moreover, using potential outcome notation allows us to state more clearly the assumptions on which missing data methods rely to estimate hypothetical estimands. This helps to indicate whether estimating a hypothetical estimand is reasonable, and what data should be used in the analysis. We show that hypothetical estimands can be estimated by exploiting data after intercurrent event occurrence, which is typically not used. for this article are available online. [ABSTRACT FROM AUTHOR]

Chronic diseases are often characterized through the repeated occurrence of unfavorable events. In chronic heart failure (CHF), recurrent hospitalizations characterize disease burden but the risk of death is not negligible. This leads to two main challenges. First, the definition of a clinically interpretable treatment effect measure (estimand) needs careful attention. Second, finding suitable analysis methods (estimators) that target the estimand of interest is difficult. In this article,we study the two estimands using the while-alive strategy described by Schmidli et al. For realistic CHF populations based on joint frailty model assumptions, we calculate the estimand values analytically and discuss their clinical interpretability. We also show that quasi-Poisson regression leads to an asymptotically unbiased estimator for the two estimands using the while-alive strategy, and that Lin-Wei-Yang-Ying is asymptotically unbiased under constant event rate conditional on being alive, by both analytic derivation and numerical simulations. We also investigate these estimators, together with the method-of-moments-estimator, in two CHF clinical trials. The findings in this article support the claim that treatment effect measures can be defined based on recurrent event endpoints that are clinically interpretable and statistically estimable. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic continues to affect the conduct of clinical trials globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain for the investigational product, or from health-related challenges such as COVID-19 infections. Some of these complications lead to unforeseen intercurrent events in the sense that they affect either the interpretation or the existence of the measurements associated with the clinical question of interest. In this article, we demonstrate how the ICH E9(R1) Addendum on estimands and sensitivity analyses provides a rigorous basis to discuss potential pandemic-related trial disruptions and to embed these disruptions in the context of study objectives and design elements. We introduce several hypothetical estimand strategies and review various causal inference and missing data methods, as well as a statistical method that combines unbiased and possibly biased estimators for estimation. To illustrate, we describe the features of a stylized trial, and how it may have been impacted by the pandemic. This stylized trial will then be revisited by discussing the changes to the estimand and the estimator to account for pandemic disruptions. Finally, we outline considerations for designing future trials in the context of unforeseen disruptions. [ABSTRACT FROM AUTHOR]

The ICH E9 addendum on estimands in clinical trials provides a framework for precisely defining the treatment effect that is to be estimated, but says little about estimation methods. Here, we report analyses of a clinical trial in type 2 diabetes, targeting the effects of randomized treatment, handling rescue treatment and discontinuation of randomized treatment using the so-called hypothetical strategy. We show how this can be estimated using mixed models for repeated measures, multiple imputation, inverse probability of treatment weighting, G-formula, and G-estimation. We describe their assumptions and practical details of their implementation using packages in R. We report the results of these analyses, broadly finding similar estimates and standard errors across the estimators. We discuss various considerations relevant when choosing an estimation approach, including computational time, how to handle missing data, whether to include post intercurrent event data in the analysis, whether and how to adjust for additional time-varying confounders, and whether and how to model different types of intercurrent event data separately., (© The Author(s) 2025. Published by Oxford University Press on behalf of The International Biometric Society.)

The medical community has been keen on developing disease-modifying (DM) products that can delay the underlying pathological or pathophysiological disease processes when treating chronic progressive diseases such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis. The DM effects can be established through the delayed-start design, which first randomizes patients to new treatment and control (first phase), and at some timepoint, crosses over the control to new treatment (second phase). If patients initially on control then switched to treatment fail to "catch up" (after a reasonable long time) with patients who received new treatment for the entire trial duration, a DM effect is confirmed. As the delayed-start design has a long duration and includes two phases, many patients can have the intercurrent events during the trial that result in missing observations for the first and/or second phase (e.g., missing data more than 30% of the total enrollment). Therefore, how to handle the intercurrent events becomes an important and inevitable topic when testing the DM effects in the delayed-start design. In this article, we focus on the hypothetical strategy for dealing the intercurrent events and discuss how to implement a few methods for handling missing observations when examining a key hypothesis for establishing the DM effect. Statistical properties of these methods are explored through simulations that are set up based on the real trial experience. [ABSTRACT FROM AUTHOR]

Abstract Background An estimand is a precise description of the treatment effect to be estimated from a trial (the question) and is distinct from the methods of statistical analysis (how the question is to be answered). The potential use of estimands to improve trial research and reporting has been underpinned by the recent publication of the ICH E9(R1) Addendum on the use of estimands in clinical trials in 2019. We set out to assess how well estimands are described in published trial protocols. Methods We reviewed 50 trial protocols published in October 2020 in Trials and BMJ Open. For each protocol, we determined whether the estimand for the primary outcome was explicitly stated, not stated but inferable (i.e. could be constructed from the information given), or not inferable. Results None of the 50 trials explicitly described the estimand for the primary outcome, and in 74% of trials, it was impossible to infer the estimand from the information included in the protocol. The population attribute of the estimand could not be inferred in 36% of trials, the treatment condition attribute in 20%, the population-level summary measure in 34%, and the handling of intercurrent events in 60% (the strategy for handling non-adherence was not inferable in 32% of protocols, and the strategy for handling mortality was not inferable in 80% of the protocols for which it was applicable). Conversely, the outcome attribute was stated for all trials. In 28% of trials, three or more of the five estimand attributes could not be inferred. Conclusions The description of estimands in published trial protocols is poor, and in most trials, it is impossible to understand exactly what treatment effect is being estimated. Given the utility of estimands to improve clinical research and reporting, this urgently needs to change.

Diabetes is a serious public health problem and one of the major chronic noncommunicable diseases. A lengthy stepwise treatment, and the need for an individualised approach to antidiabetic therapy, pose serious challenges for medicine developers. For all new hypoglycaemic medicines, there has been a centralised authorisation procedure in the European Union (EU) since 2005, which ensures a unified approach to efficacy and safety assessment. The aim of the study was to analyse current requirements for planning clinical trials of hypoglycaemic medicines containing new active substances (except for insulin products). The recommendations for diagnosis and treatment of type 2 diabetes, prepared by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) in 2019, suggest a step-by-step approach to intensification of treatment to maintain glycaemic targets, which takes account of concomitant cardiovascular or other diseases, and clinical characteristics of patients. The analysis of EASD/ADA documents and scientific literature helped to develop recommendations on the basic principles of planning and conducting clinical trials at the final stages of hypoglycaemic medicine development. The paper describes new approaches to clinical trials, which allow for a more reliable assessment of the treatment effectiveness. The strategy for the assessment of therapeutic effect should be carefully planned, justified, and reflected in variables of interest, clinical trial design, and statistical analysis of the trial results. The main efficacy criterion in confirmatory clinical trials of hypoglycaemic medicines should be the demonstration of benefits in improving glycaemic control. The medicine’s effect on the body weight may be considered as a secondary endpoint. An essential requirement is confirmation of the medicines’ cardiovascular safety, while potential additional benefits are reduction or prevention of risks of cardiovascular disease development. The clinical trial protocol should provide definitions for intercurrent events and hypoglycaemia. A comprehensive safety study of a new hypoglycaemic medicine should involve identification of anticipated or known side effects characteristic of a particular pharmacological class. The provided recommendations may be helpful for medicine developers, and for experts who perform assessment of clinical trial programmes and regulatory submissions for hypoglycaemic medicines.

Intercurrent (post‐treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events. Contrasts that naively condition on intercurrent events do not have a straight‐forward causal interpretation, and the practical relevance of other commonly used approaches is debated. In this work, we discuss how to formulate and choose an estimand, beyond the marginal intention‐to‐treat effect, from the point of view of a decision maker and drug developer. In particular, we argue that careful articulation of a practically useful research question should either reflect decision making at this point in time or future drug development. Indeed, a substantially interesting estimand is simply a formalization of the (plain English) description of a research question. A common feature of estimands that are practically useful is that they correspond to possibly hypothetical but well‐defined interventions in identifiable (sub)populations. To illustrate our points, we consider five examples that were recently used to motivate consideration of principal stratum estimands in clinical trials. In all of these examples, we propose alternative causal estimands, such as conditional effects, sequential regime effects, and separable effects, that correspond to explicit research questions of substantial interest. [ABSTRACT FROM AUTHOR]

The ICH E9(R1) addendum on Estimands and Sensitivity Analyses in Clinical Trials has introduced a new estimand framework for the design, conduct, analysis, and interpretation of clinical trials. We share Pharmaceutical Industry experiences of implementing the estimand framework in the first two years since the final guidance became available with key lessons learned and highlight what else needs to be done to continue the journey in embedding the estimand framework in clinical trials. Emerging best practices and points to consider on strategies for implementing a new estimand thinking process are provided. Whilst much of the focus of implementing ICH E9(R1) to date has been on defining estimands, we highlight some of the important aspects relating to the choice of statistical analysis methods and sensitivity analyses to ensure estimands can be estimated robustly with minimal bias. In particular, we discuss the implications if complete follow-up is not possible when the treatment policy strategy is being used to handle intercurrent events. ICH E9(R1) was introduced just before the start of the COVID-19 pandemic, but a positive outcome from the pandemic has been an acceleration in the adoption of the estimand framework, including differentiating intercurrent events related or not related to the pandemic. In summary, much has been learned on the estimand journey and continued sharing of case studies will help to further advance the understanding and increase awareness across all clinical researchers of the estimand framework. [ABSTRACT FROM AUTHOR]

Purpose: In epidemiological research, measurements affected by medication, for example, blood pressure lowered by antihypertensives, are common. Different ways of handling medication are required depending on the research questions and whether the affected measurement is the exposure, the outcome, or a confounder. This study aimed to review handling of medication use in observational research. Methods: PubMed was searched for etiological studies published between 2015 and 2019 in 15 high‐ranked journals from cardiology, diabetes, and epidemiology. We selected studies that analyzed blood pressure, glucose, or lipid measurements (whether exposure, outcome or confounder) by linear or logistic regression. Two reviewers independently recorded how medication use was handled and assessed whether the methods used were in accordance with the research aim. We reported the methods used per variable category (exposure, outcome, confounder). Results: A total of 127 articles were included. Most studies did not perform any method to account for medication use (exposure 58%, outcome 53%, and confounder 45%). Restriction (exposure 22%, outcome 23%, and confounders 10%), or adjusting for medication use using a binary indicator were also used frequently (exposure: 18%, outcome: 19%, confounder: 45%). No advanced methods were applied. In 60% of studies, the methods' validity could not be judged due to ambiguous reporting of the research aim. Invalid approaches were used in 28% of the studies, mostly when the affected variable was the outcome (36%). Conclusion: Many studies ambiguously stated the research aim and used invalid methods to handle medication use. Researchers should consider a valid methodological approach based on their research question. [ABSTRACT FROM AUTHOR]

We consider treatment effect estimation in a randomized clinical trial with longitudinally measured quantitative or categorical outcomes. To handle missing data, we usually assume missing at random and then conduct sensitivity analysis for missing not at random. In the literature, several reference‐based imputation methods, including "jump to reference" (J2R) and "copy reference" (CR), have been commonly used for conducting sensitivity analysis. J2R assumes the mean effect profile of patients who discontinue the investigative treatment jumps to that of the patients in the reference group after discontinuation, while CR assumes the conditional mean effect profile given the status prior to the time of discontinuation copies that of the patients in the reference group after discontinuation. In this article, we propose a novel, wide class of reference‐based imputation methods for conducting sensitivity analysis, which includes J2R and CR as two extreme ends. The framework is motivated by the thought that the investigative treatment may have no, partially, or fully carried‐over effect after deviation from the assigned treatment (eg, discontinue the assigned treatment or change to a different treatment). Further, we show that the proposed reference‐based imputation methods can be implemented through sequential modeling. This property ensures that the methods can be applied to clinical trials with either quantitative or categorical outcomes. We use both causal‐inference arguments and numerical examples to demonstrate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: Rescue medication (RM) consumption is commonly used as a secondary outcome in placebo-controlled trials of chronic pain, but its validity has yet to be established. If participants randomized to placebo take more RM than those randomized to an active drug, the difference in pain between the 2 groups may be reduced, potentially masking effects of the active drug. This study assessed proportional RM consumption in the placebo and active groups according to results of 42 randomized controlled trials of neuropathic pain (NeP), and 29 trials of low back pain, which were included in 2 systematic reviews and meta-analyses. Trial results were assessed based on effect size, statistical significance, and whether the drug was recommended as first-line treatment by the systematic reviews. In trials indicating effect of the investigational drug, RM consumption was generally higher in the placebo groups than in the active groups. In trials reporting a small or a medium effect size of the investigational drug, subjects receiving placebo consumed 17% to 30% more RM than subjects receiving active drug, potentially leading to underestimation of the effects of the investigational drugs. Few trials reported a large effect size. Differences in RM consumption between participants receiving placebo and those receiving active drug were seldom taken in account by the individual trials and not at all by the systemic reviews when making treatment recommendations for NeP or low back pain. Elaboration on analytical methods to assess treatment effects in chronic pain trials using RM is warranted. [ABSTRACT FROM AUTHOR]

Abstract When designing a clinical trial, explicitly defining the treatment estimands of interest (that which is to be estimated) can help to clarify trial objectives and ensure the questions being addressed by the trial are clinically meaningful. There are several challenges when defining estimands. Here, we discuss a number of these in the context of trials of treatments for patients hospitalised with COVID-19 and make suggestions for how estimands should be defined for key outcomes. We suggest that treatment effects should usually be measured as differences in proportions (or risk or odds ratios) for outcomes such as death and requirement for ventilation, and differences in means for outcomes such as the number of days ventilated. We further recommend that truncation due to death should be handled differently depending on whether a patient- or resource-focused perspective is taken; for the former, a composite approach should be used, while for the latter, a while-alive approach is preferred. Finally, we suggest that discontinuation of randomised treatment should be handled from a treatment policy perspective, where non-adherence is ignored in the analysis (i.e. intention to treat).

Accumulation of knowledge on Crohn’s disease, and development of biological products intended for the treatment of its underlying cause formed the basis for the development of objective methods for assessing the intensity of the pathological process, which in turn affected scientific approaches to the planning of clinical trials in this field. To date, many international recommendations related to planning, conduct of clinical trials, and analysis of their results, have been updated. Considerable experience has been gained with clinical trials of medicines intended for the treatment of Crohn’s disease. Therefore, the methodological approach to the planning of pivotal clinical studies needs to be reviewed. The aim of the study was to develop requirements for planning and expert evaluation of clinical trials conducted with the aim of obtaining marketing authorisation for medicinal products for the treatment of Crohn’s disease. The paper analyses regulations, recommendations, and scientific literature on the treatment of Crohn’s disease and describes the methodology for planning clinical trials. It describes the evolution of approaches to clinical research planning since biological medicines appeared. The authors substantiate the need for an integrated concept of clinical research, which covers goals, estimated therapeutic effect, design, and choice of the statistical analysis method. They also provide scientific arguments in favour of a combined primary endpoint including endoscopic remission and the assessment of treatment results by the patient. The paper lists patient eligibility criteria in terms of “inducing and/or maintaining remission of the disease”. The authors analyse the main intercurrent events, their influence on the therapeutic effect, and propose approaches to the planning of endpoints, including assessment of intercurrent events. The paper highlights the fact that the principles of planning and conducting Phase III clinical trials need to be consistent with the evidence-based strategies of reducing the risk of incorrect assessment of efficacy and safety of new medicines, and that the obtained results have to meet the requirements of the regulatory authorities at the stage of marketing authorisation.

The current COVID-19 pandemic poses numerous challenges for ongoing clinical trials and provides a stress-testing environment for the existing principles and practice of estimands in clinical trials. The pandemic may increase the rate of intercurrent events (ICEs) and missing values, spurring a great deal of discussion on amending protocols and statistical analysis plans to address these issues. In this article, we revisit recent research on estimands and handling of missing values, especially the ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials. Based on an in-depth discussion of the strategies for handling ICEs using a causal inference framework, we suggest some improvements in applying the estimand and estimation framework in ICH E9 (R1). Specifically, we discuss a mix of strategies allowing us to handle ICEs differentially based on reasons for ICEs. We also suggest ICEs should be handled primarily by hypothetical strategies and provide examples of different hypothetical strategies for different types of ICEs as well as a road map for estimation and sensitivity analyses. We conclude that the proposed framework helps streamline translating clinical objectives into targets of statistical inference and automatically resolves many issues with defining estimands and choosing estimation procedures arising from events such as the pandemic. [ABSTRACT FROM AUTHOR]

The estimand framework included in the addendum to the ICH E9 guideline facilitates discussions to ensure alignment between the key question of interest, the analysis, and interpretation. Therapeutic knowledge and drug mechanism play a crucial role in determining the strategy and defining the estimand for clinical trial designs. Clinical trials in patients with hematological malignancies often present unique challenges for trial design due to complexity of treatment options and existence of potential curative but highly risky procedures, for example, stem cell transplant or treatment sequence across different phases (induction, consolidation, maintenance). Here, we illustrate how to apply the estimand framework in hematological clinical trials and how the estimand framework can address potential difficulties in trial result interpretation. This paper is a result of a cross‐industry collaboration to connect the International Conference on Harmonisation (ICH) E9 addendum concepts to applications. Three randomized phase 3 trials will be used to consider common challenges including intercurrent events in hematologic oncology trials to illustrate different scientific questions and the consequences of the estimand choice for trial design, data collection, analysis, and interpretation. Template language for describing estimand in both study protocols and statistical analysis plans is suggested for statisticians' reference. [ABSTRACT FROM AUTHOR]

Background: Missing data are not intercurrent events; missing data are consequences of intercurrent events. When planning clinical trials with potential missing data, a conventional approach is two stage: (1) to calculate a required sample size N without considering missing data; (2) to adjust the sample size using N ∗ = N / (1 - r) , where r is the expected missing data rate. However, this approach is not estimand oriented. Methods: Clinical trial design should be aligned with estimand. Sample size calculation is a key step in clinical trial design, so methods for sample size calculation should be aligned with estimand. Results: ICH E9(R1) summarizes five strategies for dealing with intercurrent events. We consider five basic approaches for sample size calculation when planning clinical trials with intercurrent events, with each approach aligned with one of these five strategies. We extend the approaches to scenarios where some combination of multiple strategies is applied to deal with intercurrent events. Conclusion: Being aligned with estimands and strategies for dealing with intercurrent events, these methods can be used for sample size calculations when planning clinical trials with intercurrent events. [ABSTRACT FROM AUTHOR]

The interpretation of randomized clinical trial results is often complicated by intercurrent events. For instance, rescue medication is sometimes given to patients in response to worsening of their disease, either in addition to the randomized treatment or in its place. The use of such medication complicates the interpretation of the intention‐to‐treat analysis. In view of this, we propose a novel estimand defined as the intention‐to‐treat effect that would have been observed, had patients on the active arm been switched to rescue medication if and only if they would have been switched when randomized to control. This enables us to disentangle the treatment effect from the effect of rescue medication on a patient's outcome, while tempering the strong extrapolations that are typically needed when inferring what the intention‐to‐treat effect would have been in the absence of rescue medication. We propose a novel inverse probability weighting method for estimating this effect in settings where the decision to initiate rescue medication is made at one prespecified time point. This estimator relies on specific untestable assumptions, in view of which we propose a sensitivity analysis. We use the method for the analysis of a clinical trial conducted by Janssen Pharmaceuticals, in which patients with type 2 diabetes mellitus can switch to rescue medication for ethical reasons. Monte Carlo simulations confirm that the proposed estimator is unbiased in moderate sample sizes. [ABSTRACT FROM AUTHOR]

Summary: The estimand framework requires a precise definition of the clinical question of interest (the estimand) as different ways of accounting for "intercurrent" events post randomization may result in different scientific questions. The initiation of subsequent therapy is common in oncology clinical trials and is considered an intercurrent event if the start of such therapy occurs prior to a recurrence or progression event. Three possible ways to account for this intercurrent event in the analysis are to censor at initiation, consider recurrence or progression events (including death) that occur before and after the initiation of subsequent therapy, or consider the start of subsequent therapy as an event in and of itself. The new estimand framework clarifies that these analyses address different questions ("does the drug delay recurrence if no patient had received subsequent therapy?" vs "does the drug delay recurrence with or without subsequent therapy?" vs "does the drug delay recurrence or start of subsequent therapy?"). The framework facilitates discussions during clinical trial planning and design to ensure alignment between the key question of interest, the analysis, and interpretation. This article is a result of a cross‐industry collaboration to connect the International Council for Harmonisation E9 addendum concepts to applications. Data from previously reported randomized phase 3 studies in the renal cell carcinoma setting are used to consider common intercurrent events in solid tumor studies, and to illustrate different scientific questions and the consequences of the estimand choice for study design, data collection, analysis, and interpretation. [ABSTRACT FROM AUTHOR]

Summary: The International Council for Harmonisation (ICH) guideline E9 Statistical Principles for Clinical Trials (1) was issued in 1998. In October 2014, an addendum to ICH E9 was proposed on statistical principles relating to estimands and sensitivity analyses. The final version of the addendum to ICH E9 (R1) (2) was issued in November 2019. This virtual edition of Pharmaceutical Statistics takes a closer look at some of the progress that has been made since 2018 when implementing the estimand framework within clinical research. The articles discussed in this virtual issue are not new, but a compilation from previous issues. This specific article will act as a refresher for those not familiar with the topic and discuss the ABCs of estimands and their proposed deployment for improving the quality of clinical research. An overview of the more recent Pharmaceutical Statistics articles on estimands will be provided, signifying areas where progress have been made. The articles should be considered as contributions to the ongoing discussions rather than the final word. Finally, a personal perspective on the estimand success story and remaining challenges with proposed solutions will be discussed. [ABSTRACT FROM AUTHOR]

Summary: Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is relevant to the clinical research question is the first step in analyzing data. The tripartite estimands, which evaluate the treatment differences in the proportion of patients with ICEs due to adverse events, the proportion of patients with ICEs due to lack of efficacy, and the primary efficacy outcome for those who can adhere to study treatment under the causal inference framework, are of interest to many stakeholders in understanding the totality of treatment effects. In this manuscript, we discuss the details of how to estimate tripartite estimands based on a causal inference framework and how to interpret tripartite estimates through a phase 3 clinical study evaluating a basal insulin treatment for patients with type 1 diabetes. [ABSTRACT FROM AUTHOR]

Summary: The term "intercurrent events" has recently been used to describe events in clinical trials that may complicate the definition and calculation of the treatment effect estimand. This paper focuses on the use of an attributable estimand to address intercurrent events. Those events that are considered to be adversely related to randomized treatment (eg, discontinuation due to adverse events or lack of efficacy) are considered attributable and handled with a composite estimand strategy, while a hypothetical estimand strategy is used for intercurrent events not considered to be related to randomized treatment (eg, unrelated adverse events). We explore several options for how to implement this approach and compare them to hypothetical "efficacy" and treatment policy estimand strategies through a series of simulation studies whose design is inspired by recent trials in chronic obstructive pulmonary disease (COPD), and we illustrate through an analysis of a recently completed COPD trial. [ABSTRACT FROM AUTHOR]

In clinical trials in populations with mild cognitive impairment, it is common for participants to initiate concurrent symptomatic medications for Alzheimer's disease after randomization to the experimental therapy. One strategy for addressing this occurrence is to exclude any observations that occur after the concurrent medication is initiated. The rationale for this approach is that these observations might reflect a symptomatic benefit of the concurrent medication that would adversely bias efficacy estimates for an effective experimental therapy. We interrogate the assumptions underlying such an approach by estimating the effect of newly prescribed concurrent medications in an observational study, the Alzheimer's Disease Neuroimaging Initiative. [ABSTRACT FROM AUTHOR]

This paper provides examples of defining estimands in real-world scenarios following ICH E9(R1) guidelines. Detailed discussions on choosing the estimands and estimators can be found in our companion papers. Three scenarios of increasing complexity are illustrated. The first example is a proof-of-concept trial in major depressive disorder where the estimand is chosen to support the sponsor decision on whether to continue development. The second and third examples are confirmatory trials in severe asthma and rheumatoid arthritis respectively. We discuss the intercurrent events expected during each trial and how they can be handled so as to be consistent with the study objectives. The estimands discussed in these examples are not the only acceptable choices for their respective scenarios. The intent is to illustrate the key concepts rather than focus on specific choices. Emphasis is placed on following a study development process where estimands link the study objectives with data collection and analysis in a coherent manner, thereby avoiding disconnect between objectives, estimands, and analyses. [ABSTRACT FROM AUTHOR]

The National Research Council (NRC) Expert Panel Report on Prevention and Treatment of Missing Data in Clinical Trials highlighted the need for clearly defining objectives and estimands. That report sparked considerable discussion and literature on estimands and how to choose them. Importantly, consideration moved beyond missing data to include all postrandomization events that have implications for estimating quantities of interest (intercurrent events, aka ICEs). The ICH E9(R1) draft addendum builds on that research to outline key principles in choosing estimands for clinical trials, primarily with focus on confirmatory trials. This paper provides additional insights, perspectives, details, and examples to help put ICH E9(R1) into practice. Specific areas of focus include how the perspectives of different stakeholders influence the choice of estimands; the role of randomization and the intention-to-treat principle; defining the causal effects of a clearly defined treatment regimen, along with the implications this has for trial design and the generalizability of conclusions; detailed discussion of strategies for handling ICEs along with their implications and assumptions; estimands for safety objectives, time-to-event endpoints, early-phase and one-arm trials, and quality of life endpoints; and realistic examples of the thought process involved in defining estimands in specific clinical contexts. [ABSTRACT FROM AUTHOR]

The National Research Council's report on the prevention and treatment of missing data highlighted the need to clearly specify causal estimands. This focus fundamentally changed how the missing data problem was perceived and addressed in clinical trials. The recent ICH E9(R1) addendum is another major step in promoting the use of the causal estimands framework that should further influence how clinical trial protocols and statistical analysis plans are written and implemented. The language of potential outcomes that is widely accepted in the causal inference literature is not widely recognized in the clinical trialists community and was not used in defining causal estimands in the NRC report or the ICH E9(R1). In this article, we attempt to bridge the gap between the causal inference community and clinical trialists to further advance the use of causal estimands in clinical trial settings. We illustrate how concepts from causal literature, such as potential outcomes and dynamic treatment regimens, can facilitate defining and implementing causal estimands and may provide a unifying language to describing the targets for both observational and randomized clinical trials. [ABSTRACT FROM AUTHOR]

In clinical endpoint bioequivalence studies, the observed per-protocol (PP) population (compliers and completers in general) is usually used in the primary analysis for equivalence assessment. However, intercurrent events, ie, missingness and noncompliance, are not properly handled. The resulting estimand is not causal. Previously, we proposed the first causal framework to assess equivalence in the presence of missing data and noncompliance. We proposed a causal survivor average causal effect (SACE) estimand for the difference of means (DOM). In equivalence assessment, DOM is not as widely used as the ratio of means (ROM). However, no existing formula links the observed PP estimand to the SACE estimand for ROM as exists for DOM. Herein, we propose a similar causal framework for ROM using the principal stratification approach, one of the strategies recommended by the International Conference on Harmonisation (ICH) E9 R1 addendum. We quantify the bias of the observed ROM PP estimand for the SACE estimand, which provides a basis to identify three conditions under which the two estimands are equal. We propose a sensitivity analysis method to evaluate the robustness of the current PP estimator to estimate the SACE estimand. We extend Fieller's confidence interval for the SACE estimand using ROM, which can be applied to many settings. Simulation demonstrates that the PP estimator is biased in either directions and may inflate type 1 error and/or change power when the three identified conditions are violated. Our work can be applied to comparative clinical biosimilar studies. [ABSTRACT FROM AUTHOR]

Objectives: To apply the estimand framework in time to deterioration (TTD) analysis of patient-reported outcomes (PROs), and identify the appropriate statistical methods to deal with intercurrent event (IEs) such as death., Study Design and Setting: Data from phase II randomized trial were used. We estimated TTD using European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 questionnaire with death as the IE, by applying Kaplan-Meier (K.M.) estimator and Cox proportional hazards (PH) model. The Fine-Gray approach was explored, accounting for death as a competing risk. The estimands targeted by the aforementioned methods were defined., Results: We analyzed the data of 64 patients with available questionnaires at baseline. The most notable differences in TTD estimates were observed for deterioration in physical functioning: the hazard ratios were 0.44 [95% CI 0.22-0.90] and 0.62 [95% CI 0.36-1.07] by either ignoring death (31 events) or considering it as deterioration (58 events), respectively (Cox-PH model). When considering death as a competing event (Fine-Gray model), the sub-HRs was 0.51 [95% CI 0.26-1.01]., Conclusion: Depending on the proportion and distribution of deaths occurring before deterioration between arms, the Fine-Gray competing risks model should be considered rather than KM estimator and Cox PH model to reflect the patient's experience of the disease and treatment burden., Competing Interests: Declaration of competing interest AA received honorary from Astrazeneca, AMGEN, IPSEN, Kite/Gilead, and had a advisory role for Astrazeneca and Kite/Gilead, outside the submitted work. CL received honorary from MSD, Amgen, Roche, Servier, and travel expenses from Roche and MSD, outside the submitted work. JBB received honorary from Amgen, AstraZeneca, Bayer, Merck Serono, Pierre Fabre, Roche, Sanofi, Servier, Viatris and has a advisory role for Amgen, Acobiom, BMS, GSK, Merck Serono, MSD, Servier, Pierre Fabre, Incyte, outside the submitted work. FE had a consultancy or advisory role for AbbVie, Incyte, Janssen, Novartis, and Syros, outside the submitted work. Other authors have no conflict of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)