Defining the scientific questions of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. However, practical experience shows that oftentimes specific choices in the statistical analysis blur the scientific question either in part or even completely, resulting in misalignment between trial objectives, conduct, analysis, and confusion in interpretation. The need for more clarity was highlighted by the Steering Committee of the International Council for Harmonization (ICH) in 2014, which endorsed a Concept Paper with the goal of developing a new regulatory guidance, suggested to be an addendum to ICH guideline E9. Triggered by these developments, we elaborate in this paper what the relevant questions in drug development are and how they fit with the current practice of intention-to-treat analyses. To this end, we consider the perspectives of patients, physicians, regulators, and payers. We argue that despite the different backgrounds and motivations of the various stakeholders, they all have similar interests in what the clinical trial estimands should be. Broadly, these can be classified into estimands addressing (a) lack of adherence to treatment due to different reasons and (b) efficacy and safety profiles when patients, in fact, are able to adhere to the treatment for its intended duration. We conclude that disentangling adherence to treatment and the efficacy and safety of treatment in patients that adhere leads to a transparent and clinical meaningful assessment of treatment risks and benefits. We touch upon statistical considerations and offer a discussion of additional implications. Copyright © 2016 John Wiley & Sons, Ltd.
The analysis of clinical trials aiming to show symptomatic benefits is often complicated by the ethical requirement for rescue medication when the disease state of patients worsens. In type 2 diabetes trials, patients receive glucose-lowering rescue medications continuously for the remaining trial duration, if one of several markers of glycemic control exceeds pre-specified thresholds. This may mask differences in glycemic values between treatment groups, because it will occur more frequently in less effective treatment groups. Traditionally, the last pre-rescue medication value was carried forward and analyzed as the end-of-trial value. The deficits of such simplistic single imputation approaches are increasingly recognized by regulatory authorities and trialists. We discuss alternative approaches and evaluate them through a simulation study. When the estimand of interest is the effect attributable to the treatments initially assigned at randomization, then our recommendation for estimation and hypothesis testing is to treat data after meeting rescue criteria as deterministically 'missing' at random, because initiation of rescue medication is determined by observed in-trial values. An appropriate imputation of values after meeting rescue criteria is then possible either directly through multiple imputation or implicitly with a repeated measures model. Crucially, one needs to jointly impute or model all markers of glycemic control that can lead to the initiation of rescue medication. An alternative for hypothesis testing only are rank tests with outcomes from patients 'requiring rescue medication' ranked worst, and non-rescued patients ranked according to final visit values. However, an appropriate ranking of not observed values may be controversial.
The COVID-19 pandemic has a global impact on the conduct of clinical trials of medical products. This article discusses implications of the COVID-19 pandemic on clinical research methodology aspects and provides points to consider to assess and mitigate the risk of seriously compromising the integrity and interpretability of clinical trials. The information in this article will support discussions that need to occur cross-functionally on an ongoing basis to "integrate all available knowledge from the ethical, the medical, and the methodological perspective into decision making. " This article aims at facilitating: (i) risk assessments of the impact of the pandemic on trial integrity and interpretability; (ii) identification of the relevant data and information related to the impact of the pandemic on the trial that needs to be collected; (iii) short-term decision making impacting ongoing trial operations; (iv) ongoing monitoring of the trial conduct until completion, including the possible involvement of data monitoring committees, and adequately documenting all measures taken to secure trial integrity throughout and after the pandemic, and (v) proper analysis and interpretation of the eventual interim or final trial data.
Recurrent events involve the occurrences of the same type of event repeatedly over time and are commonly encountered in longitudinal studies. Examples include seizures in epileptic studies or occurrence of cancer tumors. In such studies, interest lies in the number of events that occur over a fixed period of time. One considerable challenge in analyzing such data arises when a large proportion of patients discontinues before the end of the study, for example, because of adverse events, leading to partially observed data. In this situation, data are often modeled using a negative binomial distribution with time-in-study as offset. Such an analysis assumes that data are missing at random (MAR). As we cannot test the adequacy of MAR, sensitivity analyses that assess the robustness of conclusions across a range of different assumptions need to be performed. Sophisticated sensitivity analyses for continuous data are being frequently performed. However, this is less the case for recurrent event or count data. We will present a flexible approach to perform clinically interpretable sensitivity analyses for recurrent event data. Our approach fits into the framework of reference-based imputations, where information from reference arms can be borrowed to impute post-discontinuation data. Different assumptions about the future behavior of dropouts dependent on reasons for dropout and received treatment can be made. The imputation model is based on a flexible model that allows for time-varying baseline intensities. We assess the performance in a simulation study and provide an illustration with a clinical trial in patients who suffer from bladder cancer.
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