Multiple Endpoints In Clinical Trials Research Articles

Multivariate Rank-Based Analysis of Multiple Endpoints in Clinical Trials: A Global Test Approach

Clinical trials demand a comprehensive evaluation of multiple endpoints to provide a thorough understanding of intervention efficacy and safety. In response to a discerned gap in existing literature, this study introduces an innovative global nonparametric testing procedure, grounded in multivariate ranks, for the holistic analysis of these diverse endpoints. Unlike conventional approaches that heavily rely on pairwise comparisons for individual endpoints, our method takes a novel approach by directly incorporating multivariate ranks. This methodology builds on the strengths of previous models while addressing their limitations, ensuring a more nuanced and robust assessment. By considering the joint ranking of all endpoints, our approach exhibits heightened resilience against diverse data distributions and common censoring mechanisms commonly encountered in clinical trials. The proposed method emerges from a thoughtful integration of existing models, contributing to the methodological evolution in the field. To illustrate its superiority, extensive simulations have been conducted. The results unequivocally demonstrate the superior performance of our multivariate rank-based approach, showcasing its ability to effectively control type I error and achieve higher power compared to conventional rank-based methods. This empirical validation not only underscores the method's efficacy but also highlights its versatility and robustness across a spectrum of clinical trial settings.

Meaningful Endpoints for Idiopathic Pulmonary Fibrosis (IPF) Clinical Trials: Emphasis on 'Feels, Functions, Survives'. Report of a Collaborative Discussion in a Symposium with Direct Engagement from Representatives of Patients, Investigators, the National Institutes of Health, a Patient Advocacy Organization, and a Regulatory Agency.

Background: Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods: A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA's 'feels, functions, survives' criteria. Results: Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include 'feels, functions, survives' measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: https://www.fda.gov/media/162416/download). Conclusions: This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences-namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients' quality of life and survival.

Incorporating Data from Multiple Endpoints in the Analysis of Clinical Trials: Example from RSV Vaccines.

To meet regulatory approval, interventions must demonstrate efficacy against a primary outcome in randomized clinical trials. However, when there are multiple clinically relevant outcomes, selecting a single primary outcome is challenging. Incorporating data from multiple outcomes may increase statistical power in clinical trials. We examined methods for analyzing data on multiple endpoints, inspired by real-world trials of interventions against respiratory syncytial virus (RSV). We developed a novel permutation test representing a weighted average of individual outcome test statistics ( wavP ) to evaluate intervention efficacy in a multiple endpoint analysis. We compared the power and type I error rate of this approach to the Bonferroni correction ( bonfT ) and the minP permutation test. We evaluated the different approaches using simulated data from three hypothetical trials varying the intervention efficacy, correlation, and incidence of the outcomes, and data from a real-world RSV clinical trial. When the vaccine efficacy against different outcomes was similar, wavP yielded higher power than bonfT and minP ; in some scenarios the improvement in power was substantial. In settings where vaccine efficacy was notably larger against one endpoint compared with the others, all three methods had similar power. We developed an R package, PERmutation basEd ANalysis of mulTiple Endpoints (PERMEATE), to guide the selection of the most appropriate method for analyzing multiple endpoints in clinical trials. Analyzing multiple endpoints using a weighted permutation method can increase power, whereas controlling the type I error rate compared with established methods under conditions mirroring real-world RSV clinical trials.

Outcome measures of phase III anticancer drug trials in China.

Outcome measures of phase III anticancer drug trials in China.

A Test of the Dependence Assumptions for the Simes-Test-based Multiple Test Procedures

The Hochberg procedure is known to be more powerful than the Bonferroni method but the test statistics need to satisfy certain dependence conditions. Due to the lack of validation of the positive dependence assumption in the Hochberg procedure, the U.S. Food and Drug Administration guidance on multiple endpoints in clinical trials provides conservative recommendations on the Hochberg procedure, and limits its application on a small set of standard test statistics. Based on the demand of using the Hochberg procedure in a more flexible way, we develop a test to validate the dependence assumption in the Hochberg procedure and Benjamini-Hochberg procedure. A simulation study is conducted for power analysis, and a case study in metastatic breast cancer is included to illustrate how the proposed test can be applied to validating the dependence type between the progression-free survival and overall survival.

SY16-5 Problems in clinical trials to evaluate de-escalation

Recently, de-escalation of treatment has been evaluated in some clinical trials for breast cancer. If intensive treatments do not improve survival more than less intensive treatments, less intensive treatments may be better in terms of quality of life, toxicity, cost, and some burdens. To design a clinical trial of de-escalation of treatment, many factors should be considered such as definition of treatment (regimen), population selection (evaluation of biomarker), selection of endpoints (use of multiple endpoints), randomization, and analysis methods. For example, when de-escalation is evaluated in the non-inferiority setting, it may be difficult to conduct a randomized controlled trial because of a large sample size, and how to use multiple endpoints, such as overall survival, toxicity, quality of life, and cost, may also be difficult. In this talk, I discuss the use of historical data and multiple endpoints in clinical trials of de-escalation of treatment.

Challenges on Multiple Endpoints in Clinical Trials: An Industry Survey in Japan

Two issues on clinical trials with multiple endpoints were surveyed: (1) the terminology of multiple endpoints, relationship between rare events and endpoints, and differences in multiplicity adjustment between regions; and (2) the current practice on multiplicity adjustment and sample size calculation. This article provides a summary of the results of a survey on the first issue. The survey was conducted among 63 members of the Japan Pharmaceutical Manufacturers Association from October to November 2017. Thirty-five companies based in Japan and 12 companies based in other countries, 47 companies in total, responded to the survey. The terms co-primary endpoints, secondary endpoint, and composite endpoint were used in a variety of ways. An endpoint for a clinically most important event that is expected to occur rarely differed between regions. Although the Pharmaceuticals and Medical Devices Agency did not demand multiplicity adjustment, it was considered in clinical trials with multiple endpoints for approval in Japan. The use of terminology differed from the definition in the Food and Drug Administration guidance and the European Medicines Agency guideline. There remain challenges on a clinically most important event that is expected to occur rarely and multiplicity adjustment in clinical trials with multiple endpoints.

Challenges on Multiple Endpoints in Clinical Trials: An Industry Survey in Japan.

Two issues on clinical trials with multiple endpoints were surveyed: (1) the terminology of multiple endpoints, relationship between rare events and endpoints, and differences in multiplicity adjustment between regions; and (2) the current practice on multiplicity adjustment and sample size calculation. This article provides a summary of the results of a survey on the first issue. The survey was conducted among 63 members of the Japan Pharmaceutical Manufacturers Association from October to November 2017. Thirty-five companies based in Japan and 12 companies based in other countries, 47 companies in total, responded to the survey. The terms co-primary endpoints, secondary endpoint, and composite endpoint were used in a variety of ways. An endpoint for a clinically most important event that is expected to occur rarely differed between regions. Although the Pharmaceuticals and Medical Devices Agency did not demand multiplicity adjustment, it was considered in clinical trials with multiple endpoints for approval in Japan. The use of terminology differed from the definition in the Food and Drug Administration guidance and the European Medicines Agency guideline. There remain challenges on a clinically most important event that is expected to occur rarely and multiplicity adjustment in clinical trials with multiple endpoints.

Weighted Statistics for Testing Multiple Endpoints in Clinical Trials

Weighted Statistics for Testing Multiple Endpoints in Clinical Trials

Multiple comparisons in non-inferiority trials: Reaction to recent regulatory guidance on multiple endpoints in clinical trials

ABSTRACTWe consider analysis of active control, non-inferiority clinical trials with multiple primary endpoints for assessing efficacy of an investigational treatment. Many of the issues with multiple endpoints for non-inferiority trials are similar to issues for superiority trials, but there are important differences. Because non-inferiority trials typically make decisions with confidence interval bounds instead of p-values, care must be taken in adjusting for multiple hypotheses. Composite endpoints are more difficult to interpret in non-inferiority trials due to difficulties in indirectly comparing the investigational treatment to placebo on each component. Otherwise many of the methods used in superiority trials (including sequential testing, graphical procedures and gatekeeping procedures) can be applied to non-inferiority trials with a little additional care. We focus on the differences between non-inferiority and superiority trials to provide guidance on application of recent regulatory guidance to non-inferiority trials.

Changes are still needed on multiple co-primary endpoints.

The Food and Drug Administration in the United States issued a much-awaited draft guidance on 'Multiple Endpoints in Clinical Trials' in January 2017. The draft guidance is well written and contains consistent message on the technical implementation of the principles laid out in the guidance. In this commentary, we raise a question on applying the principles to studies designed from a safety perspective. We then direct our attention to issues related to multiple co-primary endpoints. In a paper published in the Drug Information Journal in 2007, Offen et al. give examples of disorders where multiple co-primary endpoints are required by regulators. The standard test for multiple co-primary endpoints is the min test which tests each endpoint individually, at the one-sided 2.5% level, for a confirmatory trial. This approach leads to a substantial loss of power when the number of co-primary endpoints exceeds 2, a fact acknowledged in the draft guidance. We review approaches that have been proposed to tackle the problem of power loss and propose a new one. Using recommendations by Chen et al. for the assessment of drugs for vulvar and vaginal atrophy published in the Drug Information Journal in 2010, we argue the need for more changes and urge a path forward that uses different levels of claims to reflect the effectiveness of a product on multiple endpoints that are equally important and mostly unrelated. Copyright © 2017 John Wiley & Sons, Ltd.

Some remaining challenges regarding multiple endpoints in clinical trials.

Despite recent advance in methods for handling multiple endpoints in clinical trials, some challenges remain. This paper discusses some of these challenges, including confusion surrounding the terminology used to describe the multiple endpoints, the justification for simultaneously testing for non-inferiority and superiority in a non-inferiority trial, lack of agreement on the situations under which multiple objectives do or do not lead to the need for a multiplicity correction, and choice of the most appropriate multiple comparisons procedure. In addition, this paper will discuss the position of the recent FDA draft guidance, Multiple Endpoints in Clinical Trials, on these issues. Copyright © 2017 John Wiley & Sons, Ltd.

Adaptive designs for clinical trials with multiple endpoints

Rationale for adaptive designs with multiple endpoints In clinical trials, the efficacy of a new treatment is usually assessed by a single primary endpoint [1,2]. Other important aspects concerning the new treatment, such as adverse events or quality of life, are taken into account by the definition of secondary endpoints. Usually, only the primary endpoint is analyzed confirmatorily with a statistical test whereas the secondary endpoints are evaluated with descriptive statistical methods. A descriptive analysis, however, does not result in a definite proof and therefore provides only limited additional information. The current research on multiple testing strategies offers many opportunities to incorporate secondary endpoints in the confirmatory analysis of a trial, for example, by a predefined hierarchical testing procedure or enhanced gatekeeping strategies [3–6]. While the nature of secondary endpoints is that they are only of subordinate clinical importance, there also exist many clinical trial applications where the efficacy of a new treatment cannot be adequately described by only one primary endpoint. The EMEA Guideline Points to Consider on Multiplicity Issues states that ‘If, however, a single variable is not sufficient to capture the range of clinically relevant treatment benefits, the use of more than one primary variable may become necessary’ [2]. In some situations, the efficacy claim is therefore based on the significance of several primary endpoints, also referred to as coprimary endpoints. However, even if several primary endpoints are considered, it is not always necessary to demonstrate a significant and relevant effect in all primary endpoints under investigation. Another option is to base the efficacy claim of the new intervention on the significance of at least one endpoint out of a predefined set of primary endpoints considered as clinically relevant. Whenever several endpoints are included in the confirmatory analysis of a clinical trial, a multiple testing problem arises. In order to control the probability to falsely reject at least one of the null hypotheses under investigation at a predefined global significance level α, the local significance levels corresponding to the individual test hypotheses have to be adjusted by an adequate multiple testing procedure. There exist a variety of multiple testing procedures in the statistical literature for many different possible applications [3–8]. When several endpoints are assessed within a multiple test problem, the power of the trial depends on the expected effects of all endpoints and on the correlations between them. The calculation of an adequate sample size therefore is a particular challenge, as the number of required parameter assumptions to be estimated in the planning stage is much larger as compared with a single endpoint test problem. For this reason, adaptive study designs which allow reacting flexibly to these uncertainties in the planning stage play a major role for clinical trials with multiple endpoints.

Relations among three parametric multiple testing methods for correlated tests

Multiple endpoints in clinical trials are usually correlated. To control the family-wise type I error rate, both Huque and Alosh's flexible fixed-sequence (FFS) testing method and Li and Mehrotra's adaptive α allocation approach (4A) have taken into account correlations among endpoints. I suggested a weighted multiple testing correction (WMTC) for correlated tests and compared it with FFS. However, the relationship between the 4A method and the FFS method or the relationship between the 4A method and the WMTC method has not been studied. In this paper, simulations are conducted to investigate these relationships. Tentative guidelines to help choosing an appropriate method are provided.

Analysis of multiple endpoints in clinical trials: it's time for the designations of primary, secondary and tertiary to go

Analysis of multiple endpoints in clinical trials: it's time for the designations of primary, secondary and tertiary to go

Medical Product Development, Innovation, and Life-Cycle Regulation: The Challenges for Biostatistics

The article provides a perspective on the challenges for biostatistics as well as on contributions that biostatisticians are making and can make to medical product development and regulation and what the future might be in these areas. The current environment in the United States for pharmaceutical development and regulation is discussed along with the expectations that the public has for how medical products should contribute to public heath. The globalization of research and the use of study designs that incorporate multi-regional populations present new challenges for design and inference. The emerging interest in and development of the science of safety assessment and quantitative approaches to risk evaluation is considered. Guidance development, especially in the area of clinical trials design, continues to be one of the needs that FDA is asked to meet. Guidance development is proceeding for non-inferiority study designs, adaptive designs, multiple endpoints in clinical trials, and missing outcome data in clinical trials. Biostatisticians will be asked and challenged to take on leadership roles in new areas such as personalized medicine, biomarker and genomics, development of new tools for visual display of clinical data, quality assurance and monitoring in clinical trials.

Further Comments on the Alpha-Spending Function

The use of alpha-spending functions has become a powerful tool in designing group sequential trials since 1990. In this manuscript, we present practical problems that we encountered and suggest possible solutions. Since group sequential methods deal with only one endpoint, they have certain intrinsic limitations in medical research. As a result, we often modify the design in conducting clinical trials since the sequential statistical framework is not sophisticated enough to handle many practical problems related to multiple endpoints in clinical trials. Topics discussed include the choice of a primary endpoint; the use of symmetric and asymmetric boundaries in interim analysis; data-driven interim analyses; overruling of a group sequential boundary; comparison of two means and the use of logrank tests in survival trials.

A flexible fixed-sequence testing method for hierarchically ordered correlated multiple endpoints in clinical trials

Statistical approaches for addressing multiplicity in clinical trials range from the very conservative (the Bonferroni method) to the least conservative the fixed sequence approach. Recently, several authors proposed methods that combine merits of the two extreme approaches. Wiens [2003. A fixed sequence Bonferroni procedure for testing multiple endpoints. Pharmaceutical Statist. 2003, 2, 211–215], for example, considered an extension of the Bonferroni approach where the type I error rate ( α ) is allocated among the endpoints, however, testing proceeds in a pre-determined order allowing the type I error rate to be saved for later use as long as the null hypotheses are rejected. This leads to a higher power of the test in testing later null hypotheses. In this paper, we consider an extension of Wiens’ approach by taking into account correlations among endpoints for achieving higher flexibility in testing. We show strong control of the family-wise type I error rate for this extension and provide critical values and significance levels for testing up to three endpoints with equal correlations and show how to calculate them for other correlation structures. We also present results of a simulation experiment for comparing the power of the proposed method with those of Wiens’ and others. The results of this experiment show that the magnitude of the gain in power of the proposed method depends on the prospective ordering of testing of the endpoints, the magnitude of the treatment effects of the endpoints and the magnitude of correlation between endpoints. Finally, we consider applications of the proposed method for clinical trials with multiple time points and multiple doses, where correlations among endpoints frequently arise.

How to deal with multiple endpoints in clinical trials

Multiple endpoints are common in clinical trials. This article discusses statistical methods that can be applied to control the rate of false positive conclusions at an acceptable level. The considered methods include the Bonferroni adjustment and related methods, the intersection-union test, ordered hypotheses and gatekeeper procedures, composite endpoints and global assessment measures, closed testing procedures, and combinations of different approaches.

A combined superiority and non‐inferiority approach to multiple endpoints in clinical trials

Treatment comparisons in clinical trials often involve multiple endpoints. By making use of bootstrap tests, we develop a new non-parametric approach to multiple-endpoint testing that can be used to demonstrate non-inferiority of a new treatment for all endpoints and superiority for some endpoint when it is compared to an active control. It is shown that this approach does not incur a large multiplicity cost in sample size to achieve reasonable power and that it can incorporate complex dependencies in the multivariate distributions of all outcome variables for the two treatments via bootstrap resampling.