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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 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]

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]