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BACKGROUND: Uncertain ascertainment of events in clinical trials has been noted for decades. To correct possible bias, Clinical Endpoint Committees (CECs) have been employed as a critical element of trials to ensure consistent and high-quality endpoint evaluation, especially for cardiovascular endpoints. However, the efficiency and usefulness of adjudication have been debated. METHODS: The multiple imputation (MI) method was proposed to incorporate endpoint event uncertainty. In a simulation conducted to explain this methodology, the dichotomous outcome was imputed each time with subject-specific event probabilities. As the final step, the desired analysis was conducted based on all imputed data. This proposed method was further applied to real trial data from PARADIGM-HF. RESULTS: Compared with the conventional Cox model with adjudicated events only, the Cox MI method had higher power, even with a small number of uncertain events. It yielded more robust inferences regarding treatment effects and required a smaller sample size to achieve the same power. CONCLUSIONS: Instead of using dichotomous endpoint data, the MI method enables incorporation of event uncertainty and eliminates the need for categorizing endpoint events. In future trials, assigning a probability of event occurrence for each event may be preferable to a CEC assigning a dichotomous outcome. Considerable resources could be saved if endpoint events can be identified more simply and in a manner that maintains study power.

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We investigate selection of critical boundary functions for testing the hypotheses of two time-to-event outcomes as both primary endpoints or a primary and a secondary endpoint in group-sequential clinical trials, where (1) the effect sizes of endpoints are unequal, or (2) one endpoint is for short-term evaluation and the other for long-term evaluation. Bonferroni-Holm and fixed-sequence procedures are considered. We assess the effects of the magnitudes of the hazard ratios and the correlation between the endpoints on statistical powers and provide guidance for consideration.

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While 2-in-1 designs give a flexibility to make a clinical trial either an information generation Phase 2 trial or a full scale confirmatory Phase 3 trial, flexible sample size designs can naturally fit into the 2-in-1 design framework. This study is to show that the CHW design can be blended into a 2-in-1 design to improve the adaptive performance of the design. Commenting on the usual 2-in-1 design, we demonstrated that the CHW design can achieve the goal of a 2-in-1 design with satisfactory statistical power and efficient average sample size for a targeted range of the treatment effect.

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The sequential parallel comparison designhas recently been considered to solve the problem with high placebo response and the required sample size in the psychiatric clinical trials. One feature with this design is that a difference between the placebo group and the drug group may also arise in the variance-covariance structure of the clinical outcome. Provided the heterogeneity of the second moment, the treatment effect estimation at the second stage can be biased for the entire randomized patient population that includes patient responders. Our work presented here aims at how the coverage probability of the interval estimation of treatment effect performs under the unstructured variance-covariance matrix. The interaction between the truncation after the first stage and the heterogeneity of the second moment causes a substantial coverage probability problem. The type I error probability may not be controlled under the weak null due to this bias. This bias can also cause spurious power evaluation under an alternative hypothesis. The coverage probability of the ordinary least square statistic is shown in different scenarios.

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In this rejoinder the authors stipulate further for two challenging issues. First, if placebo non-responders are selected simply by their response meeting a threshold, this selection may have misclassification error and consequently the treatment effect estimate may be biased, regardless of whether the estimand at the second stage is the treatment effect in the entire population or placebo non-responders. Secondly, the weak null hypothesis considered in our article Statistical Inference Problems in Sequential Parallel Comparison Design (2019) is that the expected treatment effects in placebo non-responders and in the entire set of patients entering the trial are both zero, in contrast to the strong null hypothesis that the statistical distribution of the response variable is equal in the compared treatments. The impact of violating the assumption of equal moments other than the mean parameter on statistical operating characteristics in estimation and testing of treatment effect can be substantial. As an example, the ordinary least squares based test detects a treatment difference even if the expected treatment effects in placebo non-responders and the entire population are both zero.

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Serious and chronic health conditions such as cardiovascular diseases (CVDs) are posing challenges to the health system. Recently clinical trials in these fields have focused on composite endpoints that take into account both disease-related mortality and major disease-related morbidity events. It is the time to the first component of the composite endpoint experienced by a patient that is the traditional study endpoint and treatment aims are to delay the time to the first event and to reduce its frequency. As the name implies, the time-to-first composite event analysis approach focuses only on the first composite event and ignores subsequent events. For a chronic disease, this can lead to a substantial loss of potentially important information. For instance, in chronic heart failure (HF) studies, the traditional composite endpoint of HF-related hospitalizations and CVD death will ignore CVD deaths that are preceded by HF-related hospitalizations. This paper explores the limitations of the traditional time-to-first event approach and discusses the potential value of incorporating all events. The authors argue that endpoints capturing recurrent event information can lead to interpretable measures of treatment effect that better reflect disease burden than traditional time-to-first event endpoints by using the available information beyond the first event. This paper aims to raise awareness of the value and potential pitfalls of alternative treatment effect measures to facilitate meaningful cross-functional conversations among trialists and other stakeholders such as regulators, payers, and treating physicians who all are striving to the same goal-to deliver the most effective treatments to patients.

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This publication describes uniform definitions for cardiovascular and stroke outcomes developed by the Standardized Data Collection for Cardiovascular Trials Initiative and the U.S. Food and Drug Administration (FDA). The FDA established the Standardized Data Collection for Cardiovascular Trials Initiative in 2009 to simplify the design and conduct of clinical trials intended to support marketing applications. The writing committee recognizes that these definitions may be used in other types of clinical trials and clinical care processes where appropriate. Use of these definitions at the FDA has enhanced the ability to aggregate data within and across medical product development programs, conduct meta-analyses to evaluate cardiovascular safety, integrate data from multiple trials, and compare effectiveness of drugs and devices. Further study is needed to determine whether prospective data collection using these common definitions improves the design, conduct, and interpretability of the results of clinical trials.

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This publication describes uniform definitions for cardiovascular and stroke outcomes developed by the Standardized Data Collection for Cardiovascular Trials Initiative and the US Food and Drug Administration (FDA). The FDA established the Standardized Data Collection for Cardiovascular Trials Initiative in 2009 to simplify the design and conduct of clinical trials intended to support marketing applications. The writing committee recognizes that these definitions may be used in other types of clinical trials and clinical care processes where appropriate. Use of these definitions at the FDA has enhanced the ability to aggregate data within and across medical product development programs, conduct meta-analyses to evaluate cardiovascular safety, integrate data from multiple trials, and compare effectiveness of drugs and devices. Further study is needed to determine whether prospective data collection using these common definitions improves the design, conduct, and interpretability of the results of clinical trials.

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BACKGROUND: Conducting clinical trials across multiple regions of the world has become common practice. A multiregional clinical trial (MRCT) presents opportunities as well as challenges. However, regional differences of treatment effects appear in many MRCTs, which make the interpretation of clinical trial results difficult and presents challenges for clinical trial design. Alzheimer disease (AD) is a progressive neurodegenerative disorder that affects approximately 5 million people in the United States and is the sixth leading cause of death in the country. In 2014, AD cost the United States $214 billion, and the cost is expected to rise to $1.2 trillion by 2050. METHODS: In this article, we utilize data from New Drug Applications (NDAs) that have been approved for the treatment of AD to study whether there are differences in treatment effect between US and non-US study sites. Using an analysis of covariance (ANCOVA) model and forest plot, we analyze the treatment difference by region (US and non-US) from 3 separate perspectives: by region for each trial, by region for each endpoint, and by region and trial for each endpoint. RESULTS: Overall, the analyses indicate that treatment effects in clinical trials for AD are generally in the expected direction in both US and non-US sites. There was no clear evidence of heterogeneity in treatment effects between US and non-US sites. CONCLUSIONS: It appears that there is no clear evidence to suggest that MRCTs should not be used to study AD.

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There are several challenging statistical problems identified in the regulatory review of large cardiovascular (CV) clinical outcome trials and central nervous system (CNS) trials. The problems can be common or distinct due to disease characteristics and the differences in trial design elements such as endpoints, trial duration, and trial size. In schizophrenia trials, heavy missing data is a big problem. In Alzheimer trials, the endpoints for assessing symptoms and the endpoints for assessing disease progression are essentially the same; it is difficult to construct a good trial design to evaluate a test drug for its ability to slow the disease progression. In CV trials, reliance on a composite endpoint with low event rate makes the trial size so large that it is infeasible to study multiple doses necessary to find the right dose for study patients. These are just a few typical problems. In the past decade, adaptive designs were increasingly used in these disease areas and some challenges occur with respect to that use. Based on our review experiences, group sequential designs (GSDs) have borne many successful stories in CV trials and are also increasingly used for developing treatments targeting CNS diseases. There is also a growing trend of using more advanced unblinded adaptive designs for producing efficacy evidence. Many statistical challenges with these kinds of adaptive designs have been identified through our experiences with the review of regulatory applications and are shared in this article.

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OBJECTIVE: Sexual dysfunction is a significant treatment-emergent adverse reaction to the serotonergic antidepressants (selective serotonin reuptake inhibitors [SSRIs] and serotonin-norepinephrine reuptake inhibitors [SNRIs]). However, the rate of sexual dysfunction is often underestimated in registration trials, which have relied on unsolicited reports. We conducted a literature search to examine the rates of sexual dysfunction with SSRIs/SNRIs when these rates were ascertained by structured questionnaires or standardized instruments. Additionally, we conducted exploratory analyses of major depressive disorder (MDD) registration trial data. DATA SOURCES: For the literature search, we used the PubMed and EMBASE databases, with a cutoff date of April 1, 2011. We included all the SSRIs and SNRIs that at the time had been approved for the treatment of MDD. For each of these drugs, a search was conducted with the following terms: sexual dysfunction, SD, sexual adverse effects, desire, arousal, excitement, and orgasm. For the exploratory analyses of US Food and Drug Administration in-house trial data, we searched our database for short-term (6-8 weeks), randomized, placebo-controlled MDD monotherapy trials of approved drugs included in New Drug Application submissions that used a standardized instrument to assess sexual function. STUDY SELECTION: For the literature search, we initially found a total of 123 nonduplicate articles, some of which included multiple studies. After screening based on our inclusion/exclusion criteria (and to remove duplicate trial-level data), we were left with 7 articles representing 11 unique studies in which sexual dysfunction was assessed with direct questioning or standardized instruments. The Changes in Sexual Functioning Questionnaire-Short-Form (CSFQ-14) and Arizona Sexual Experiences Scale (ASEX) were the only instruments represented. For the exploratory analyses of in-house MDD trial data, we found controlled studies using either the CSFQ-14 (6 trials) or ASEX (5 trials). DATA EXTRACTION: For the literature search, we were able to pool the results for the studies that included direct questioning. For the studies that used standardized instruments to assess sexual function, we simply describe our findings. For the exploratory analyses of in-house MDD trial data, we constructed a dataset containing all subject-level CSFQ-14 or ASEX item scores for each of the trials as well as demographic and other relevant variables. For each treatment or placebo group, analyses were performed on pooled data, including multiple studies, and on individual studies. RESULTS: For our literature search, regardless of which method was used to assess sexual function, the data from these articles were informative and showed the expected effects on sexual function with SSRIs/SNRIs. However, for our exploratory analyses, no trend was observed in CSFQ-14 or ASEX results for individual drugs or drug classes. CONCLUSIONS: These results raise the question as to why the CSFQ-14 and ASEX appeared to perform well in the published studies but not in our exploratory analyses of in-house MDD trial data. We discuss possible reasons and solutions.

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OBJECTIVE: Sexual dysfunction is an important side effect of serotonergic antidepressants, as it often leads to treatment nonadherence. However, sexual dysfunction is often underestimated in clinical trials submitted in support of drug approval. This is because such assessments are based mainly on unsolicited reporting. As a result, the characterization of sexual adverse events has become an important component of many of the development programs for new antidepressants. The purpose of this article is to discuss US Food and Drug Administration's (FDA's) current thinking on possible approaches to characterizing the effects of drugs on sexual function in depression drug trials. PARTICIPANTS: FDA's Division of Psychiatry Products, together with the Division of Biometrics I, in particular the authors of this article. EVIDENCE: The above-referenced FDA divisions conducted a regulatory science forum on measuring sexual dysfunction in depression trials. CONSENSUS PROCESS: Considering the evidence presented and discussed at the forum, we developed our preliminary regulatory views on the scientific issues with regard to study design, study population, use of available scales, testing strategy, and statistical analysis plans. CONCLUSIONS: Sexual dysfunction associated with antidepressants is an important entity that should be adequately assessed during clinical trials with the use of available instruments and described in product labels. It is important to appreciate the need for a positive control to establish assay sensitivity for any trial evaluating the impact of antidepressant medications on sexual function. Methodological improvement and additional data as well as experience with these approaches will be needed prior to further consideration of a formal regulatory guidance document by the FDA.

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An invited panel session was conducted in the 2012 Joint Statistical Meetings, San Diego, California, USA, to stimulate the discussion on multiplicity issues in confirmatory clinical trials for drug development. A total of 11 expert panel members were invited and 9 participated. Prior to the session, a case study was previously provided to the panel members to facilitate the discussion, focusing on the key components of the study design and multiplicity. The Phase 3 development program for this new experimental treatment was based on a single randomized controlled trial alone. Each panelist was asked to clarify if he or she responded as if he or she were a pharmaceutical drug sponsor, an academic panelist or a health regulatory scientist.

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Adaptive designs have generated a great deal of attention to clinical trial communities. The literature contains many statistical methods to deal with added statistical uncertainties concerning the adaptations. Increasingly encountered in regulatory applications are adaptive statistical information designs that allow modification of sample size or related statistical information and adaptive selection designs that allow selection of doses or patient populations during the course of a clinical trial. For adaptive statistical information designs, a few statistical testing methods are mathematically equivalent, as a number of articles have stipulated, but arguably there are large differences in their practical ramifications. We pinpoint some undesirable features of these methods in this work. For adaptive selection designs, the selection based on biomarker data for testing the correlated clinical endpoints may increase statistical uncertainty in terms of type I error probability, and most importantly the increased statistical uncertainty may be impossible to assess.

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This regulatory research provides possible approaches for improvement to conventional subgroup analysis in a fixed design setting. The interaction-to-overall effects ratio is recommended in the planning stage for potential predictors whose prevalence is at most 50% and its observed ratio is recommended in the analysis stage for proper subgroup interpretation if sample size is only planned to target the overall effect size. We illustrate using regulatory examples and underscore the importance of striving for balance between safety and efficacy when considering a regulatory recommendation of a label restricted to a subgroup. A set of decision rules gives guidance for rigorous subgroup-specific conclusions.

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The authors consider a statistically valid method that has little concern caused by down weighting in stage 2 or slight loss in efficiency and possibly improves power performance for a trial design that allows for adaptation of statistical information. In addition to the need for a priori sound data-based planning that accounts for limited uncertainty, the criteria for statistical information adaptation are scientifically sound and can foster transparent trial logistics that are necessary for such adaptation in confirmatory trials. A major rationale of the proposed conditional adaptive weighted test method is to increase the probability of success of an adequate and well-controlled confirmatory trial that may otherwise fall short of statistical significance despite initial rigorous planning for statistical information.

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There is a growing interest in pursuing adaptive enrichment for drug development because of its potential to achieve the goal of personalized medicine. There are many versions of adaptive enrichment proposed across many disease indications. Some are exploratory adaptive enrichment and others aim at confirmatory adaptive enrichment. In this paper, we give a brief overview on adaptive enrichment and the methodologies that are growing in statistical literature. A case example is provided to illustrate a regulatory experience that led to drug approval. There were two design elements used for adaptation in this case example: population adaptation and statistical information adaptation. We articulate the challenges in the implementation of a confirmatory adaptive enrichment trial. The challenges include logistic aspects on the appropriate choice of study population for adaptation and the ability to follow the pre-specified rules for statistical information or sample size adaptation. We assess the consistency of treatment effect before and after adaptation using the approach laid out in Wang et al. (2013). We provide the rationales for what would be an appropriate treatment effect estimate for reporting in the drug label. We discuss and articulate design considerations for adaptive enrichment among a dual-composite null hypothesis, a flexible dual-independent null hypothesis and a rigorous dual-independent null hypothesis.

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Multiple comparisons have drawn a great deal of attention in evaluation of statistical evidence in clinical trials for regulatory applications. As the clinical trial methodology is increasingly more complex to properly take into consideration many practical factors, the multiple testing paradigm widely employed for regulatory applications may not suffice to interpret the results of an individual trial and of multiple trials. In a large outcome trial, an increasing need of studying more than one dose complicates a proper application of multiple comparison procedures. Additional challenges surface when a special endpoint, such as mortality, may need to be tested with multiple clinical trials combined, especially under group sequential designs. Another interesting question is how to study mortality or morbidity endpoints together with symptomatic endpoints in an efficient way, where the former type of endpoints are often studied in only one single trial but the latter type of endpoints are usually studied in at least two independent trials. This article is devoted to discussion of insufficiency of such a widely used paradigm applying only per-trial based multiple comparison procedures and to expand the utility of the procedures to such complex trial designs. A number of viable expanded strategies are stipulated.

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