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Chimeric antigen receptor (CAR) T-cell therapy is a human gene therapy product where T cells from a patient are genetically modified to enable them to recognize desired target antigen(s) more effectively. In recent years, promising antitumor activity has been seen with autologous CAR T cells. Since 2017, six CAR T-cell therapies for the treatment of hematological malignancies have been approved by the Food and Drug Administration (FDA). Despite the rapid progress of CAR T-cell therapies, considerable statistical challenges still exist for this category of products across all phases of clinical development that need to be addressed. These include (but not limited to) dose finding strategy, implementation of the estimand framework, use of real-world data in contextualizing single-arm CAR T trials, analysis of safety data and long-term follow-up studies. This paper is the first step in summarizing and addressing these statistical hurdles based on the development of the six approved CAR T-cell products. [ABSTRACT FROM AUTHOR]

Intercurrent events and estimands play a key role in defining the treatment effects of interest precisely. Sometimes the median or other quantiles of outcomes in a principal stratum according to potential occurrence of intercurrent events are of interest in randomized clinical trials. Naïve analyses such as those based on the observed occurrence of the intercurrent events lead to biased results. Therefore, we propose principal quantile treatment effect estimators that can nonparametrically estimate the distribution of potential outcomes by principal score weighting without relying on the exclusion restriction assumption. Our simulation studies show that the proposed method works in situations where the median or quantiles may be regarded as the preferred population‐level summary over the mean. We illustrate our proposed method by using data from a randomized controlled trial conducted on patients with nonerosive reflux disease. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. New measures, including the Numeric Rating Scale of Underlying Pain without concurrent Analgesic use—may allow pain randomized controlled trials to target alternate estimands with clinical relevance. Although many individuals with chronic pain use analgesics, the methods used in many randomized controlled trials (RCTs) do not sufficiently account for confounding by differential post-randomization analgesic use. This may lead to underestimation of average treatment effects and diminished power. We introduce (1) a new measure—the Numeric Rating Scale of Underlying Pain without concurrent Analgesic use (NRS-UP(A))—which can shift the estimand of interest in an RCT to target effects of a treatment on pain intensity in the hypothetical situation where analgesic use was not occurring at the time of outcome assessment; and (2) a new pain construct—an individuals' perceived effect of analgesic use on pain intensity (EA). The NRS-UP(A) may be used as a secondary outcome in RCTs of point treatments or nonpharmacologic treatments. Among 662 adults with back pain in primary care, participants' mean value of the NRS-UP(A) among those using analgesics was 1.2 NRS points higher than their value on the conventional pain intensity NRS, reflecting a mean EA value of −1.2 NRS points and a perceived beneficial effect of analgesics. More negative values of EA (ie, greater perceived benefit) were associated with a greater number of analgesics used but not with pain intensity, analgesic type, or opioid dose. The NRS-UP(A) and EA were significantly associated with future analgesic use 6 months later, but the conventional pain NRS was not. Future research is needed to determine whether the NRS-UP(A), used as a secondary outcome may allow pain RCTs to target alternative estimands with clinical relevance. [ABSTRACT FROM AUTHOR]

ICH E9(R1) specifies the importance of precisely defining the treatment effect for clinical trials—to inform patient choices and facilitate evidence-based decision-making. FDA's guidance on covariate adjustment encourages the judicious use of baseline covariates to enhance efficiency. Careful consideration is required when adjusting for covariates in nonlinear models such as logistic regression and Cox regression. For these nonlinear models, including baseline covariates can change the targeted treatment effect (estimand). It is also crucial that the proposed statistical analyses align with the estimand of interest. Covariate-adjusted estimators used for unconditional treatment effect are typically robust to misspecification of the used regression models. Despite extensive literature and recommendations by the FDA on the statistical theory and properties of these methods, the real-life application of such methodologies is still limited. Moreover, a few open questions require further discussion, especially for time-to-event outcomes. In this article, we present causal estimands definition of conditional and unconditional treatment effects in randomized clinical trials. We also evaluate different estimation methods for estimating conditional and unconditional treatment effects for the binary and time-to-event endpoints. We also discuss practical considerations in choosing the conditional versus unconditional effect, implementing the estimation methods, and interpreting results. [ABSTRACT FROM AUTHOR]

AbstractIntroduction/BackgroundMethodsResultsConclusionWe aimed to explore data-generating models to jointly simulate outcomes and intercurrent events for randomised clinical trials to enable the investigation of estimands.We developed four possible data-generating models for the joint distribution of longitudinal continuous clinical outcomes and intercurrent events under the scenario where they are observable: a selection model, a pattern-mixture mixed model, a shared-parameter model and a joint model of longitudinally observed outcomes and a survival model for intercurrent events. We present a case study in a short-term depression trial with repeated measurements of continuous outcomes and two types of intercurrent events, and evaluate the potential and challenges of such data-generating models.Simulating randomised trials with outcomes and intercurrent events is a complex undertaking. We found that the four possible data-generating models can simulate different types of intercurrent events and associated longitudinal outcomes. They can be used to emulate envisaged patterns of intercurrent events and outcomes informed by prior available trial data or expectations. Model and parameter choice for a given application requires further development.The four possible data-generating models could be used to investigate different estimands and their properties in-depth in the design stage. Thereby they are useful tools for the selection of estimands a priori. [ABSTRACT FROM AUTHOR]

Background: After an initial recommendation from the World Health Organisation, trials of patients hospitalised with COVID-19 often include an ordinal clinical status outcome, which comprises a series of ordered categorical variables, typically ranging from 'Alive and discharged from hospital' to 'Dead'. These ordinal outcomes are often analysed using a proportional odds model, which provides a common odds ratio as an overall measure of effect, which is generally interpreted as the odds ratio for being in a higher category. The common odds ratio relies on the assumption of proportional odds, which implies an identical odds ratio across all ordinal categories; however, there is generally no statistical or biological basis for which this assumption should hold; and when violated, the common odds ratio may be a biased representation of the odds ratios for particular categories within the ordinal outcome. In this study, we aimed to evaluate to what extent the common odds ratio in published COVID-19 trials differed to simple binary odds ratios for clinically important outcomes. Methods: We conducted a systematic review of randomised trials evaluating interventions for patients hospitalised with COVID-19, which used a proportional odds model to analyse an ordinal clinical status outcome, published between January 2020 and May 2021. We assessed agreement between the common odds ratio and the odds ratio from a standard logistic regression model for three clinically important binary outcomes: 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital'. Results: Sixteen randomised clinical trials, comprising 38 individual comparisons, were included in this study; of these, only 6 trials (38%) formally assessed the proportional odds assumption. The common odds ratio differed by more than 25% compared to the binary odds ratios in 55% of comparisons for the outcome 'Alive', 37% for 'Alive without mechanical ventilation', and 24% for 'Alive and discharged from hospital'. In addition, the common odds ratio systematically underestimated the odds ratio for the outcome 'Alive' by –16.8% (95% confidence interval: –28.7% to –2.9%, p = 0.02), though differences for the other outcomes were smaller and not statistically significant (–8.4% for 'Alive without mechanical ventilation' and 3.6% for 'Alive and discharged from hospital'). The common odds ratio was statistically significant for 18% of comparisons, while the binary odds ratio was significant in 5%, 16%, and 3% of comparisons for the outcomes 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital', respectively. Conclusion: The common odds ratio from proportional odds models often differs substantially to odds ratios from clinically important binary outcomes, and similar to composite outcomes, a beneficial common OR from a proportional odds model does not necessarily indicate a beneficial effect on the most important categories within the ordinal outcome. [ABSTRACT FROM AUTHOR]

A frequently addressed issue in clinical trials is the comparison of censored paired survival outcomes, for example, when individuals were matched based on their characteristics prior to the analysis. In this regard, a proper incorporation of the dependence structure of the paired censored outcomes is required and, up to now, appropriate methods are only rarely available in the literature. Moreover, existing methods are not motivated by the strive for insights by means of an easy‐to‐interpret parameter. Hence, we seek to develop a new estimand‐driven method to compare the effectiveness of two treatments in the context of right‐censored survival data with matched pairs. With the help of competing risks techniques, the so‐called relative treatment effect is estimated. This estimand describes the probability that individuals under Treatment 1 have a longer lifetime than comparable individuals under Treatment 2. We derive hypothesis tests and confidence intervals based on a studentized version of the estimator, where resampling‐based inference is established by means of a randomization method. In a simulation study, we demonstrate for numerous sample sizes and different amounts of censoring that the developed test exhibits a good power. Finally, we apply the methodology to a well‐known benchmark data set from a trial with patients suffering from diabetic retinopathy. [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]

A Real-World Evidence (RWE) Scientific Working Group (SWG) of the American Statistical Association Biopharmaceutical Section (ASA BIOP) has been reviewing statistical considerations for the generation of RWE to support regulatory decision-making. As part of the effort, the working group is addressing estimands in RWE studies. Constructing the right estimand--the target of estimation--which reflects the research question and the study objective, is one of the key components in formulating a clinical study. ICH E9(R1) describes statistical principles for constructing estimands in clinical trials with a focus on five attributes--population, treatment, endpoints, intercurrent events, and population-level summary. However, defining estimands for clinical studies using real-world data (RWD), that is, RWE studies, requires additional considerations due to, for example, heterogeneity of study population, complexity of treatment regimes, different types and patterns of intercurrent events, and complexities in choosing study endpoints. This article reviews the essential components of estimands and causal inference framework, discusses considerations in constructing estimands for RWE studies, highlights similarities and differences in traditional clinical trial and RWE study estimands, and provides a roadmap for choosing appropriate estimands for RWE studies. [ABSTRACT FROM AUTHOR]

Abstract Background The Identifying Child Anxiety Through Schools-identification to intervention (iCATS-i2i) trial is being conducted to establish whether ‘screening and intervention’, consisting of usual school practice plus a pathway comprising screening, feedback and a brief parent-led online intervention (OSI: Online Support and Intervention for child anxiety), bring clinical and health economic benefits compared to usual school practice and assessment only — ‘usual school practice’, for children aged 8–9 years in the following: (1) the ‘target population’, who initially screen positive for anxiety problems according to a two-item parent-report child anxiety questionnaire — iCATS-2, and (2) the ‘total population’, comprising all children in participating classes. This article describes the detailed statistical analysis plan for the trial. Methods and design iCATS-i2i is a definitive, superiority, pragmatic, school-based cluster randomised controlled trial (with internal pilot), with two parallel groups. Schools are randomised 1:1 to receive either screening and intervention or usual school practice. This article describes the following: trial objectives and outcomes; statistical analysis principles, including detailed estimand information necessary for aligning trial objectives, conduct, analyses and interpretation when there are different analysis populations and outcome measures to be considered; and planned main analyses, sensitivity and additional analyses. Trial registration ClinicalTrials.gov ISRCTN76119074. Registered on 4 January 2022

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.

Inference for the conditional association between an exposure and a time-to-event endpoint, given covariates, is routinely based on partial likelihood estimators for hazard ratios indexing Cox proportional hazards models. This approach is flexible and makes testing straightforward, but is nonetheless not entirely satisfactory. First, there is no good understanding of what it infers when the model is misspecified. Second, it is common to employ variable selection procedures when deciding which model to use. However, the bias and uncertainty that imperfect variable selection adds to the analysis is rarely acknowledged, rendering standard inferences biased and overly optimistic. To remedy this, we propose a nonparametric estimand which reduces to the main exposure effect parameter in a (partially linear) Cox model when that model is correct, but continues to capture the (conditional) association of interest in a well understood way, even when this model is misspecified in an arbitrary manner. We achieve an assumption-lean inference for this estimand based on its influence function under the nonparametric model. This has the further advantage that it makes the proposed approach amenable to the use of data-adaptive procedures (e.g., variable selection, machine learning), which we find to work well in simulation studies and a data analysis. for this article are available online. [ABSTRACT FROM AUTHOR]

Regulatory agencies are advancing the use of systematic approaches to collect patient experience data, including patient-reported outcomes (PROs), in cancer clinical trials to inform regulatory decision-making. Due in part to clinician under-reporting of symptomatic adverse events, there is a growing recognition that evaluation of cancer treatment tolerability should include the patient experience, both in terms of the overall side effect impact and symptomatic adverse events. Methodologies around implementation, analysis, and interpretation of “patient” reported tolerability are under development, and current approaches are largely descriptive. There is robust guidance for use of PROs as efficacy endpoints to compare cancer treatments, but it is unclear to what extent this can be relied-upon to develop tolerability endpoints. An important consideration when developing endpoints to compare tolerability between treatments is the linkage of trial design, objectives, and statistical analysis. Despite interest in and frequent collection of PRO data in oncology trials, heterogeneity in analyses and unclear PRO objectives mean that design, objectives, and analysis may not be aligned, posing substantial challenges for the interpretation of results. The recent ICH E9 (R1) estimand framework represents an opportunity to help address these challenges. Efforts to apply the estimand framework in the context of PROs have primarily focused on efficacy outcomes. In this paper, we discuss considerations for comparing the patient-reported tolerability of different treatments in an oncology trial context. [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 Identifying Child Anxiety Through Schools-identification to intervention (iCATS-i2i) trial is being conducted to establish whether 'screening and intervention', consisting of usual school practice plus a pathway comprising screening, feedback and a brief parent-led online intervention (OSI: Online Support and Intervention for child anxiety), bring clinical and health economic benefits compared to usual school practice and assessment only — 'usual school practice', for children aged 8–9 years in the following: (1) the 'target population', who initially screen positive for anxiety problems according to a two-item parent-report child anxiety questionnaire — iCATS-2, and (2) the 'total population', comprising all children in participating classes. This article describes the detailed statistical analysis plan for the trial. Methods and design: iCATS-i2i is a definitive, superiority, pragmatic, school-based cluster randomised controlled trial (with internal pilot), with two parallel groups. Schools are randomised 1:1 to receive either screening and intervention or usual school practice. This article describes the following: trial objectives and outcomes; statistical analysis principles, including detailed estimand information necessary for aligning trial objectives, conduct, analyses and interpretation when there are different analysis populations and outcome measures to be considered; and planned main analyses, sensitivity and additional analyses. Trial registration: ClinicalTrials.gov ISRCTN76119074. Registered on 4 January 2022 [ABSTRACT FROM AUTHOR]

Duration of response (DOR) and time to response (TTR) are typically evaluated as secondary endpoints in early‐stage clinical studies in oncology when efficacy is assessed by the best overall response and presented as the overall response rate. Despite common use of DOR and TTR in particular in single‐arm studies, the definition of these endpoints and the questions they are intended to answer remain unclear. Motivated by the estimand framework, we present relevant scientific questions of interest for DOR and TTR and propose corresponding estimand definitions. We elaborate on how to deal with relevant intercurrent events which should follow the same considerations as implemented for the primary response estimand. A case study in mantle cell lymphoma illustrates the implementation of relevant estimands of DOR and TTR. We close the paper with practical recommendations to implement DOR and TTR in clinical study protocols. [ABSTRACT FROM AUTHOR]

Single-arm trials (SATs) may be used to support regulatory submissions in settings where there is a high unmet medical need and highly promising early efficacy data undermine the equipoise needed for randomization. In this context, patient-level real-world data (RWD) may be used to create an external control arm (ECA) to contextualize the SAT results. However, naive comparisons of the SAT with its ECA will yield biased estimates of causal effects if groups are imbalanced with regards to (un)measured prognostic factors. Several methods are available to adjust for measured confounding, but the interpretation of such analyses is challenging unless the causal question of interest is clearly defined, and the estimator is aligned with the estimand. Additional complications arise when patients in the ECA meet the inclusion/exclusion criteria for the SAT at multiple timepoints. In this article, we use a case-study of a pivotal SAT to illustrate how a combination of the target trial and the ICH E9(R1) estimand frameworks can be used to define the target estimand and avoid common methodological pitfalls. We also propose an approach to address the challenge of how to define an appropriate time zero for external controls who meet the SAT inclusion/exclusion criteria at several timepoints. [ABSTRACT FROM AUTHOR]

A Real-World Evidence (RWE) scientific working group of the American Statistical Association Biopharmaceutical Section endeavors to elevate statistical practices of generating real-world evidence to support regulatory decision making. Randomized clinical trials have been the gold standard for evaluating the safety and efficacy of medical products. However, in many cases, their costs, duration, limited generalizability, and ethical or technical infeasibility might have motivated some to consider alternatives, such as real-world studies. Yet, the studies conducted in a real-world setting may be susceptible to the lack of randomization and the presence of confounding bias. To generate robust real-world evidence (RWE) from analyzing real-world data (RWD), we have proposed an RWE Causal Inference Roadmap with targeted learning. Three key steps in the roadmap are (1) define a target estimand that aligns with the causal estimand of the study objective; (2) select an efficient estimator for estimating the target estimand and an estimator of its uncertainty; and (3) evaluate the robustness of conclusions to violations of untestable causal assumptions. In this article, we demonstrate how to apply the roadmap to generate RWE using synthetic data based on a real oncology trial. [ABSTRACT FROM AUTHOR]

We have explored several statistical approaches to impute missing time-to-event data that arise from outcome trials with relatively long follow-up periods. Aligning with the primary estimand, such analyses evaluate the robustness of results by imposing an assumption different from censoring at random (CAR). Although there have been debates over which assumption and which method is more appropriate to be applied to the imputation, we propose to use the collection of retrieved dropouts as the basis of missing data imputation. As retrieved dropouts share a similar disposition, such as treatment discontinuation, with subjects who have missing data, they can reasonably be assumed to characterize the distribution of time-to-event among subjects with missing data. In terms of computational intensity and robustness to violation of underlying distributional assumption, we have compared parametric approaches via MCMC or MLE multivariate sampling procedures to a non-parametric bootstrap approach with respect to baseline hazard function. Each of these approaches follows a process of multiple imputation ("proper imputations"), analysis of complete datasets, and final combination. The type-I error, and power rates are examined under a wide range of scenarios to inform the performance characteristics. A subset of a real unblinded phase III CVOT is used to demonstrate the application of the proposed approaches, compared to the Cox proportional hazards model and jump-to-reference multiple imputation. [ABSTRACT FROM AUTHOR]

Background: Recent work has shown that cluster-randomised trials can estimate two distinct estimands: the participant-average and cluster-average treatment effects. These can differ when participant outcomes or the treatment effect depends on the cluster size (termed informative cluster size). In this case, estimators that target one estimand (such as the analysis of unweighted cluster-level summaries, which targets the cluster-average effect) may be biased for the other. Furthermore, commonly used estimators such as mixed-effects models or generalised estimating equations with an exchangeable correlation structure can be biased for both estimands. However, there has been little empirical research into whether informative cluster size is likely to occur in practice. Method: We re-analysed a cluster-randomised trial comparing two different thresholds for red blood cell transfusion in patients with acute upper gastrointestinal bleeding to explore whether estimates for the participant- and cluster-average effects differed, to provide empirical evidence for whether informative cluster size may be present. For each outcome, we first estimated a participant-average effect using independence estimating equations, which are unbiased under informative cluster size. We then compared this to two further methods: (1) a cluster-average effect estimated using either weighted independence estimating equations or unweighted cluster-level summaries, and (2) estimates from a mixed-effects model or generalised estimating equations with an exchangeable correlation structure. We then performed a small simulation study to evaluate whether observed differences between cluster- and participant-average estimates were likely to occur even if no informative cluster size was present. Results: For most outcomes, treatment effect estimates from different methods were similar. However, differences of >10% occurred between participant- and cluster-average estimates for 5 of 17 outcomes (29%). We also observed several notable differences between estimates from mixed-effects models or generalised estimating equations with an exchangeable correlation structure and those based on independence estimating equations. For example, for the EQ-5D VAS score, the independence estimating equation estimate of the participant-average difference was 4.15 (95% confidence interval: −3.37 to 11.66), compared with 2.84 (95% confidence interval: −7.37 to 13.04) for the cluster-average independence estimating equation estimate, and 3.23 (95% confidence interval: −6.70 to 13.16) from a mixed-effects model. Similarly, for thromboembolic/ischaemic events, the independence estimating equation estimate for the participant-average odds ratio was 0.43 (95% confidence interval: 0.07 to 2.48), compared with 0.33 (95% confidence interval: 0.06 to 1.77) from the cluster-average estimator. Conclusion: In this re-analysis, we found that estimates from the various approaches could differ, which may be due to the presence of informative cluster size. Careful consideration of the estimand and the plausibility of assumptions underpinning each estimator can help ensure an appropriate analysis methods are used. Independence estimating equations and the analysis of cluster-level summaries (with appropriate weighting for each to correspond to either the participant-average or cluster-average treatment effect) are a desirable choice when informative cluster size is deemed possible, due to their unbiasedness in this setting. [ABSTRACT FROM AUTHOR]

Decentralized clinical trials (DCTs), that is, studies integrating elements of telemedicine and mobile/local healthcare providers allowing for home‐based assessments, are an important concept to make studies more resilient and more patient‐centric by taking into consideration participant's views and shifting trial activities to better meet the needs of trial participants. There are however, not only advantages but also challenges associated with DCTs. An area to be addressed by appropriate statistical methodology is the integration of data resulting from a possible mix of home and clinic assessments at different visits for the same variable, especially in adjusting for sources of possible systematic differences. One source of systematic bias may be how a participant perceives their disease and treatment in their home versus in a clinical setting. In this paper, we will discuss these issues with a focus on Neuroscience when participants have the choice between home and clinic assessments to illustrate how to identify systematic biases and describe appropriate approaches to maintain clinical trial scientific rigor. We will describe the benefits and challenges of DCTs in Neuroscience and then describe the relevance of home versus clinic assessments using the estimand framework. We outline several options to enable home assessments in a study. Results of simulations will be presented to help deciding between design and analysis options in a simple scenario where there might be differences in response between clinic and home assessments. [ABSTRACT FROM AUTHOR]

AbstractWhen choosing estimands and estimators in randomized clinical trials, caution is warranted, as intercurrent events, such as, due to patients who switch treatment after disease progression, are often strongly associated with patients’ time-varying prognostic factors. Consequently, for patients who did experience intercurrent events, there are typically no comparable patients who did not. Statistical analyses may then easily lure one into making large implicit extrapolations, which often go unnoticed. We will illustrate this problem of implicit extrapolations using a real oncology case study, with a right-censored time-to-event endpoint, in which patients can cross over from the control to the experimental treatment after disease progression, for ethical reasons. We address this by developing an estimator for the survival risk ratio contrasting the survival probabilities at each time t if all patients would take experimental treatment with the survival probabilities at those times t if all patients would take control treatment up to time t, using randomization as an instrumental variable to avoid reliance on no unmeasured confounders assumptions. This doubly robust estimator can handle time-varying treatment switches and right-censored survival times. Insight into the rationale behind the estimator is provided and the approach is demonstrated by reanalyzing the oncology trial. [ABSTRACT FROM AUTHOR]

Anti-cancer therapy has been a significant focus of research. Developing and marketing various types and mechanisms of anti-cancer therapies benefit a variety of patients significantly. The long-term benefit to patients in evaluating the risk-benefit ratio of anti-cancer therapy has become a significant concern. This paper discusses the evaluation of long-term efficacy within the estimand framework and summarizes the various strategies for addressing potential intercurrent events. Non-proportional hazards of survival data may arise with novel anti-cancer therapies, leading to potential bias in conventional evaluation methods. This paper reviews statistical methods for addressing this issue, including novel endpoints, hypothesis testing, and efficacy estimation methods. We also discuss the influences of treatment switching. Although advanced methods have been developed to address the non-proportional hazard, they still have limitations that require continued collaborative efforts to resolve issues. [ABSTRACT FROM AUTHOR]

In single-arm clinical trials with external controls, usually the estimand of interest is defined as the average treatment effect on the treated (ATT), and external controls are leveraged to provide an estimator of the estimand. Recently, virtual control approaches have been proposed to predict the outcomes for experimental study subjects as if they were receiving the control treatment, resulting in so-called “virtual controls” for comparison with their observed outcomes under the experimental intervention. We consider the virtual control approaches within the causal inference framework, discussing the properties of the vanilla virtual control approach and the targeted virtual control approach. We illustrate via simulation that the targeted virtual control approach is doubly robust, whereas the vanilla virtual control approach is not. We demonstrate the targeted virtual control approach is the same as the targeted maximum likelihood estimator (TMLE) when targeted at the ATT estimand. Therefore, through the notion of virtual controls, we offer a tangible way to understand and interpret TMLE when targeted at the ATT estimand. [ABSTRACT FROM AUTHOR]

Purpose: To illustrate the utility of the self‐controlled study design for studies without an active comparator, we compared the results of a cohort design study with a non‐user comparator with those of a self‐controlled design study in evaluating the risk of varenicline on cardiovascular outcomes, using a Japanese medical claims database. Methods: The participating smokers were identified from health‐screening results collected between May 2008 and April 2017. Using a non‐user‐comparator cohort study design, we estimated the hazard ratios (HRs) and 95% confidence intervals (CIs) of varenicline on initial hospitalization with cardiovascular outcomes using Cox's model adjusted for patients' sex, age, medical history, medication history, and health‐screening results. Using a self‐controlled study design, the within‐subject HR was estimated using a stratified Cox's model adjusted for medical history, medication history, and health‐screening results. The estimate from a recent meta‐analysis was considered the gold standard (risk ratio: 1.03). Results: We identified 460 464 smokers (398 694 males [86.6%]; mean (standard deviation) age: 42.9 [10.8] years) in the database. Of these, 11 561 had been dispensed varenicline at least once, and 4511 had experienced cardiovascular outcomes. The estimate of the non‐user‐comparator cohort study design exceeded the gold standard (HR [95% CI]: 2.04 [1.22–3.42]), whereas that of the self‐controlled study design was close to the gold standard (within‐subject HR [95% CI]: 1.12 [0.27–4.70]). Conclusions: The self‐controlled study design is useful alternative to a non‐user‐comparator cohort design when evaluating the risk of medications relative to their non‐use, based on a medical information database. [ABSTRACT FROM AUTHOR]

The scientific foundation of a modern clinical trial is randomization – each patient is randomized to a treatment group, and statistical comparisons are made between treatment groups. Because the study units are individual patients, this ‘one patient, one vote’ principle needs to be followed – both in study design and in data analysis. From the physicians' point of view, each patient is equally important, and they need to be treated equally in data analysis. It is critical that statistical analysis should respect design and study design is based on randomization. Hence from both statistical and medical points of view, data analysis needs to follow this ‘one patient, one vote’ principle. Under ICH E9 (R1), five strategies are recommended to establish ‘estimand’. This paper discusses how to implement these strategies using the ‘one patient, one vote’ principle.

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.

Abstract Background Clinical trials aim to draw conclusions about the effects of treatments, but a trial can address many different potential questions. For example, does the treatment work well for patients who take it as prescribed? Or does it work regardless of whether patients take it exactly as prescribed? Since different questions can lead to different conclusions on treatment benefit, it is important to clearly understand what treatment effect a trial aims to investigate—this is called the ‘estimand’. Using estimands helps to ensure trials are designed and analysed to answer the questions of interest to different stakeholders, including patients and public. However, there is uncertainty about whether patients and public would like to be involved in defining estimands and how to do so. Public partners are patients and/or members of the public who are part of, or advise, the research team. We aimed to (i) co-develop a tool with public partners that helps explain what an estimand is and (ii) explore public partner’s perspectives on the importance of discussing estimands during trial design. Methods An online consultation meeting was held with 5 public partners of mixed age, gender and ethnicities, from various regions of the UK. Public partner opinions were collected and a practical tool describing estimands, drafted before the meeting by the research team, was developed. Afterwards, the tool was refined, and additional feedback sought via email. Results Public partners want to be involved in estimand discussions. They found an introductory tool, to be presented and described to them by a researcher, helpful for starting a discussion about estimands in a trial design context. They recommended storytelling, analogies and visual aids within the tool. Four topics related to public partners’ involvement in defining estimands were identified: (i) the importance of addressing questions that are relevant to patients and public in trials, (ii) involving public partners early on, (iii) a need for education and communication for all stakeholders and (iv) public partners and researchers working together. Conclusions We co-developed a tool for researchers and public partners to use to facilitate the involvement of public partners in estimand discussions.

Abstract Active-control trials, where an experimental treatment is compared with an established treatment, are performed when the inclusion of a placebo control group is deemed to be unethical. For time-to-event outcomes, the primary estimand is usually the rate ratio, or the closely-related hazard ratio, comparing the experimental group with the control group. In this article we describe major problems in the interpretation of this estimand, using examples from COVID-19 vaccine and HIV pre-exposure prophylaxis trials. In particular, when the control treatment is highly effective, the rate ratio may indicate that the experimental treatment is clearly statistically inferior even when it is worthwhile from a public health perspective. We argue that it is crucially important to consider averted events as well as observed events in the interpretation of active-control trials. An alternative metric that incorporates this information, the averted events ratio, is proposed and exemplified. Its interpretation is simple and conceptually appealing, namely the proportion of events that would be averted by using the experimental treatment rather than the control treatment. The averted events ratio cannot be directly estimated from the active-control trial, and requires an additional assumption about either: (a) the incidence that would have been observed in a hypothetical placebo arm (the counterfactual incidence) or (b) the efficacy of the control treatment (relative to no treatment) that pertained in the active-control trial. Although estimation of these parameters is not straightforward, this must be attempted in order to draw rational inferences. To date, this method has been applied only within HIV prevention research, but has wider applicability to treatment trials and other disease areas.

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]

Purpose: Ideally, the objectives of a pharmacoepidemiologic comparative effectiveness or safety study should dictate its design and data analysis. This paper discusses how defining an estimand is instrumental to this process. Methods: We applied the ICH‐E9 (Statistical Principles for Clinical Trials) R1 addendum on estimands – which originally focused on randomized trials – to three examples of observational pharmacoepidemiologic comparative effectiveness and safety studies. Five key elements specify the estimand: the population, contrasted treatments, endpoint, intercurrent events, and population‐level summary measure. Results: Different estimands were defined for case studies representing three types of pharmacological treatments: (1) single‐dose treatments using a case study about the effect of influenza vaccination versus no vaccination on mortality risk in an adult population of ≥60 years of age; (2) sustained‐treatments using a case study about the effect of dipeptidyl peptidase 4 inhibitor versus glucagon‐like peptide‐1 agonist on hypoglycemia risk in treatment of uncontrolled diabetes; and (3) as needed treatments using a case study on the effect of nitroglycerin spray as‐needed versus no nitroglycerin on syncope risk in treatment of stabile angina pectoris. Conclusions: The case studies illustrated that a seemingly clear research question can still be open to multiple interpretations. Defining an estimand ensures that the study targets a treatment effect that aligns with the treatment decision the study aims to inform. Estimand definitions further help to inform choices regarding study design and data‐analysis and clarify how to interpret study findings. [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]

Background: Clinical trials aim to draw conclusions about the effects of treatments, but a trial can address many different potential questions. For example, does the treatment work well for patients who take it as prescribed? Or does it work regardless of whether patients take it exactly as prescribed? Since different questions can lead to different conclusions on treatment benefit, it is important to clearly understand what treatment effect a trial aims to investigate—this is called the 'estimand'. Using estimands helps to ensure trials are designed and analysed to answer the questions of interest to different stakeholders, including patients and public. However, there is uncertainty about whether patients and public would like to be involved in defining estimands and how to do so. Public partners are patients and/or members of the public who are part of, or advise, the research team. We aimed to (i) co-develop a tool with public partners that helps explain what an estimand is and (ii) explore public partner's perspectives on the importance of discussing estimands during trial design. Methods: An online consultation meeting was held with 5 public partners of mixed age, gender and ethnicities, from various regions of the UK. Public partner opinions were collected and a practical tool describing estimands, drafted before the meeting by the research team, was developed. Afterwards, the tool was refined, and additional feedback sought via email. Results: Public partners want to be involved in estimand discussions. They found an introductory tool, to be presented and described to them by a researcher, helpful for starting a discussion about estimands in a trial design context. They recommended storytelling, analogies and visual aids within the tool. Four topics related to public partners' involvement in defining estimands were identified: (i) the importance of addressing questions that are relevant to patients and public in trials, (ii) involving public partners early on, (iii) a need for education and communication for all stakeholders and (iv) public partners and researchers working together. Conclusions: We co-developed a tool for researchers and public partners to use to facilitate the involvement of public partners in estimand discussions. [ABSTRACT FROM AUTHOR]

Interpretation of safety data for clinical trials that were ongoing at the onset of the COVID-19 pandemic or were started subsequent to the beginning of the pandemic may be affected in a variety of ways. Pandemic-related issues can influence the extent of study participation and introduce data collection gaps. A SARS-CoV-2 infection among study subjects as a post-randomization event may introduce a number of confounding factors that can alter the frequency of adverse events, in some cases appearing as an increase in the frequency of an adverse event associated with a study drug relative to a comparator. The authors discuss clinical challenges and statistical concerns, specifically the estimand framework, including examples for consideration, to address these challenges in safety evaluation wrought by the COVID-19 pandemic. Our aim is to shed light on the importance of starting an early dialogue among the drug development team on the evaluation of safety, critical for benefit-risk evaluation throughout the drug development process. [ABSTRACT FROM AUTHOR]

ICH E9(R1) introduces the estimand framework to strengthen dialogues between sponsors and regulators during drug development. A well-structured benefit-risk assessment (BRA) framework also intends to facilitate communication among stakeholders. However, the estimand in ICH E9(R1) is written mainly from the perspective of a single measure of treatment effect in clinical trials. There is lack of systematic discussion on estimand in the context of BRA. This paper initiates the BRA discussion under the estimand framework. By identifying two types of BRA approaches, we summarize and discuss completed clinical trials, using the estimand language for BRA. Benefits and challenges of using estimand for BRA are also discussed. [ABSTRACT FROM AUTHOR]

Many clinical trials include time-to-event or survival data as an outcome. To compare two survival distributions, the log-rank test is often used to produce a P-value for a statistical test of the null hypothesis that the two survival curves are identical. However, such a P-value does not provide the magnitude of the difference between the curves regarding the treatment effect. As a result, the P-value is often accompanied by an estimate of the hazard ratio from the proportional hazards model or Cox model as a measurement of treatment difference. However, one of the most important assumptions for Cox model is that the hazard functions for the two treatment groups are proportional. When the hazard curves cross, the Cox model could lead to misleading results and the log-rank test could also perform poorly. To address the problem of crossing curves in survival analysis, we propose the use of the win ratio method put forward by Pocock et al. as an estimand for analysing such data. The subjects in the test and control treatment groups are formed into all possible pairs. For each pair, the test treatment subject is labelled a winner or a loser if it is known who had the event of interest such as death. The win ratio is the total number of winners divided by the total number of losers and its standard error can be estimated using Bebu and Lachin method. Using real trial datasets and Monte Carlo simulations, this study investigates the power and type I error and compares the win ratio method with the log-rank test and Cox model under various scenarios of crossing survival curves with different censoring rates and distribution parameters. The results show that the win ratio method has similar power as the log-rank test and Cox model to detect the treatment difference when the assumption of proportional hazards holds true, and that the win ratio method outperforms log-rank test and Cox model in terms of power to detect the treatment difference when the survival curves cross. [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]

As the regulatory environment becomes progressively receptive toward utilizing real-world evidence, a spectrum of real-world data incorporation techniques in trial conduct and analysis has seen increasing interest and adoption in different stages of drug development. Of particular interest is leveraging external control data to augment the control arm in a concurrent randomized controlled trial, where patients are enrolled in both investigational treatment arm and the control arm. Yet despite the emerging literature in external data borrowing in a hybrid trial setting, very little discussion focuses on delineating what should be matched and what is actually being estimated, especially when a variety of matching schemes can be considered. In general, external control can be matched in four different ways: (1) matching with the intersection between investigational treatment and concurrent control, (2) matching with the union of concurrent investigational treatment and concurrent control, (3) matching with concurrent control alone, and (4) matching with investigational treatment alone. In this article, the formulation of estimands for different matching schemes are detailed to describe what these matching methods facilitate to answer. Simulation studies are also conducted to evaluate the performance characteristics under different matching schemes, estimation methods, effect size assumptions, and missingness of confounders. [ABSTRACT FROM AUTHOR]

For the analysis of a time‐to‐event endpoint in a single‐arm or randomized clinical trial it is generally perceived that interpretation of a given estimate of the survival function, or the comparison between two groups, hinges on some quantification of the amount of follow‐up. Typically, a median of some loosely defined quantity is reported. However, whatever median is reported, is typically not answering the question(s) trialists actually have in terms of follow‐up quantification. In this paper, inspired by the estimand framework, we formulate a comprehensive list of relevant scientific questions that trialists have when reporting time‐to‐event data. We illustrate how these questions should be answered, and that reference to an unclearly defined follow‐up quantity is not needed at all. In drug development, key decisions are made based on randomized controlled trials, and we therefore also discuss relevant scientific questions not only when looking at a time‐to‐event endpoint in one group, but also for comparisons. We find that different thinking about some of the relevant scientific questions around follow‐up is required depending on whether a proportional hazards assumption can be made or other patterns of survival functions are anticipated, for example, delayed separation, crossing survival functions, or the potential for cure. We conclude the paper with practical recommendations. [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]

An important goal of precision medicine is to identify biomarkers that are predictive, and tailor the treatment according to the biomarker levels of individual patients. Differentiating prognostic versus predictive biomarkers impacts important decision makings for patients and treating physicians. Using Hazard Ratio (HR) can mistake a purely prognostic biomarker for a predictive one leading to a disheartening possibility of depriving patients of beneficial treatment as demonstrated in the OAK trial. This stems from the illogical issue of HR at population level where marginal HR in the overall population can be larger than those in both subgroups. Instead of trying to circumvent this issue by discouraging comparisons between marginal and conditional HRs, we propose to directly fix it by using alternative logic-respecting efficacy estimands such as ratio of medians, ratio and difference of restricted mean survival times and milestone probabilities. These measures are straightforward, easy to interpret and clinically meaningful. More importantly, they will guarantee agreement between marginal and conditional efficacy and provide cohesive message around efficacy profile of the drug in the presence of subgroups. A step further is the application of Subgroup Mixable Estimation (SME) principle to ensure logical estimates when analyzing real clinical trial data. Detailed guidance is provided for the aforementioned logic-respecting estimands using either parametric, semiparametric or nonparametric approaches. Simultaneous inference can be provided with proper multiplicity adjustment to facilitate joint decision making with user-friendly apps. [ABSTRACT FROM AUTHOR]

Active-control trials, where an experimental treatment is compared with an established treatment, are performed when the inclusion of a placebo control group is deemed to be unethical. For time-to-event outcomes, the primary estimand is usually the rate ratio, or the closely-related hazard ratio, comparing the experimental group with the control group. In this article we describe major problems in the interpretation of this estimand, using examples from COVID-19 vaccine and HIV pre-exposure prophylaxis trials. In particular, when the control treatment is highly effective, the rate ratio may indicate that the experimental treatment is clearly statistically inferior even when it is worthwhile from a public health perspective. We argue that it is crucially important to consider averted events as well as observed events in the interpretation of active-control trials. An alternative metric that incorporates this information, the averted events ratio, is proposed and exemplified. Its interpretation is simple and conceptually appealing, namely the proportion of events that would be averted by using the experimental treatment rather than the control treatment. The averted events ratio cannot be directly estimated from the active-control trial, and requires an additional assumption about either: (a) the incidence that would have been observed in a hypothetical placebo arm (the counterfactual incidence) or (b) the efficacy of the control treatment (relative to no treatment) that pertained in the active-control trial. Although estimation of these parameters is not straightforward, this must be attempted in order to draw rational inferences. To date, this method has been applied only within HIV prevention research, but has wider applicability to treatment trials and other disease areas. [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]

Ill‐defined research questions could be particularly problematic in an epidemiological setting where measurements fluctuate over time due to intercurrent events, such as medication use. When a research question fails to specify how medication use should be handled methodologically, arbitrary decisions may be made during the analysis phase, which likely leads to a mismatch between the intended question and the performed analysis. The mismatch can result in vastly different or meaningless interpretations of estimated effects. Thus, a research question such as "what is the effect of X on Y?" requires further elaboration, and it should consider whether and how medication use has affected the measurements of interest. In our study, we will discuss how well‐defined questions can be formulated when medication use is involved in observational studies. We will distinguish between a situation where an exposure is affected by medication use and where the outcome of interest is affected by medication use. For each setting, we will give examples of different research questions that could be asked depending on how medication use is considered in the estimand and discuss methodological considerations under each question. [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]