Composite Endpoints In Clinical Trials Research Articles

Restricted time win ratio: from estimands to estimation

The win ratio is a novel approach to analyzing prioritized composite endpoints in clinical trials. The win ratio has grown increasingly popular and has been applied in many ongoing cardiovascular clinical trials. Despite the intuitive interpretation of win ratio statistics, the traditionally used win ratio estimands are heavily influenced by the pattern of censoring. Treating missing values or censored data as ties is generally not a reliable way of handling missing values even in the estimation procedure. To avoid this complication, we propose the restricted time win ratio, which is the win ratio at a pre-specified time point τ and can be written in terms of the expectations of potential outcomes. To estimate the proposed estimand, we develop an approximately fully conditional specification algorithm which imputes the missing data of longitudinal and clinical outcomes jointly up to τ . The imputation algorithm can be easily revised to adjust a different hierarchical structure of the sequential comparisons specified by the users. Simulation studies in realistic settings show that the proposed imputation algorithm provides an unbiased and robust estimation of the restricted time win ratio. Finally, real data from a cardiovascular clinical trial are used to illustrate the proposed method.

Use of win time for ordered composite endpoints in clinical trials.

Consider the choice of outcome for overall treatment benefit in a clinical trial which measures the first time to each of several clinical events. We describe several new variants of the win ratio that incorporate the time spent in each clinical state over the common follow-up, where clinical state means the worst clinical event that has occurred by that time. One version allows restriction so that death during follow-up is most important, while time spent in other clinical states is still accounted for. Three other variants are described; one is based on the average pairwise win time, one creates a continuous outcome for each participant based on expected win times against a reference distribution and another that uses the estimated distributions of clinical state to compare the treatment arms. Finally, a combination testing approach is described to give robust power for detecting treatment benefit across a broad range of alternatives. These new methods are designed to be closer to the overall treatment benefit/harm from a patient's perspective, compared to the ordinary win ratio. The new methods are compared to the composite event approach and the ordinary win ratio. Simulations show that when overall treatment benefit on death is substantial, the variants based on either the participants' expected win times (EWTs) against a reference distribution or estimated clinical state distributions have substantially higher power than either the pairwise comparison or composite event methods. The methods are illustrated by re-analysis of the trial heart failure: a controlled trial investigating outcomes of exercise training.

Trends in use of composite endpoints in clinical trials: A comparison between acute heart failure trials and COVID-19 trials.

Composite endpoints can encode multiple pieces of information and are increasingly adopted in clinical trials. Advocacy for using composite endpoints began decades ago in cardiovascular trials, leading to incorporation of patient-oriented outcomes and consideration of a hierarchical ranking system. The use of composite endpoints in coronavirus disease (COVID-19) trials has evolved similarly. We conducted a literature review to investigate the use of composite endpoints in acute heart failure and COVID-19 clinical trials. The results showed more frequent use of patient-oriented outcomes and ordinal composite endpoints in COVID-19 trials, which might be driven by global consensus on a set of common outcome measures.

SA26 Win Ratio Analyses of Multiple Endpoints in Rare Disease Trials: A Case-Study Based on a Trial of Avaglucosidase Alfa in Late-Onset Pompe Disease (LOPD)

The win ratio approach aims to improve analyses of composite endpoints in clinical trials by accounting for the relative importance of components of the composite endpoint and enhancing interpretability of the treatment effect measure. We propose a new application as an alternative to hierarchical testing of endpoints to improve statistical power in small trials (e.g., in rare diseases). This is illustrated with a reanalysis of the COMET trial (NCT02782741) comparing avalglucosidase alfa (AVA) and alglucosidase alfa (ALGLU) in patients with LOPD, in which statistical noninferiority was achieved, but superiority was not met.

A class of proportional win-fractions regression models for composite outcomes.

The win ratio is gaining traction as a simple and intuitive approach to analysis of prioritized composite endpoints in clinical trials. To extend it from two-sample comparison to regression, we propose a novel class of semiparametric models that includes as special cases both the two-sample win ratio and the traditional Cox proportional hazards model on time to the first event. Under the assumption that the covariate-specific win and loss fractions are proportional over time, the regression coefficient is unrelated to the censoring distribution and can be interpreted as the log win ratio resulting from one-unit increase in the covariate. U-statistic estimating functions, in the form of an arbitrary covariate-specific weight process integrated by a pairwise residual process, are constructed to obtain consistent estimators for the regression parameter. The asymptotic properties of the estimators are derived using uniform weak convergence theory for U-processes. Visual inspection of a "score" process provides useful clues as to the plausibility of the proportionality assumption. Extensive numerical studies using both simulated and real data from a major cardiovascular trial show that the regression methods provide valid inference on covariate effects and outperform the two-sample win ratio in both efficiency and robustness. The proposed methodology is implemented in the R-package WR, publicly available from the Comprehensive R Archive Network (CRAN).

Late Graft Loss After Kidney Transplantation: Is "Death With Function" Really Death With a Functioning Allograft?

About half of late kidney allograft losses are attributed to death with function (DWF), a poorly characterized outcome. An ongoing question is whether DWF is a consequence of chronic allograft dysfunction. Using the prospective Long-term Deterioration of Kidney Allograft Function study database, we sought to better define the impact, phenotype, and clinical course of DWF in the current era. Three thousand five hundred eighty-seven kidney recipients with functional grafts at 90 days post-transplant were followed prospectively for a median of 5.2 years. Characteristics at transplantation in those with DWF (N = 350, 9.8%) differed from those who otherwise lost their grafts (death-censored graft failure [DC-GF], N = 295, 8.2%) or maintained function (N = 2942, 82.0%); DWF patients were older, sicker, and had been on dialysis longer, with more preexisting cardiovascular disease, whereas DC-GF patients experienced more early rejection, more acute rejection after 90 days, and a clinically significant decrease in kidney function before graft failure. In contrast, the clinical course after transplantation in DWF patients did not differ before death from those who maintained function throughout. DWF and DC-GF in kidney transplant recipients represent differing clinical phenotypes occurring in distinct patient populations. Reducing the impact of DWF requires better definition of causes and clinical course and then trials of therapies to improve outcomes. Composite endpoints in clinical trials that group DWF and DC-GF together may obscure important clinical findings.

Are component endpoints equal? A preference study into the practice of composite endpoints in clinical trials.

ObjectivesTo examine patients’ perspectives regarding composite endpoints and the utility patients put on possible adverse outcomes of revascularization procedures.DesignIn the PRECORE study, a stated preference elicitation method Best‐Worst Scaling (BWS) was used to determine patient preference for 8 component endpoints (CEs): need for redo percutaneous coronary intervention (PCI) within 1 year, minor stroke with symptoms <24 hours, minor myocardial infarction (MI) with symptoms <3 months, recurrent angina pectoris, need for redo coronary artery bypass grafting (CABG) within 1 year, major MI causing permanent disability, major stroke causing permanent disability and death within 24 hours.SettingA tertiary PCI/CABG centre.ParticipantsOne hundred and sixty patients with coronary artery disease who underwent PCI or CABG.Main outcome measuresImportance weights (IWs).ResultsPatients considered need for redo PCI within 1 year (IW: 0.008), minor stroke with symptoms <24 hours (IW: 0.017), minor MI with symptoms <3 months (IW: 0.027), need for redo CABG within 1 year (IW: 0.119), recurrent angina pectoris (IW: 0.300) and major MI causing permanent disability (IW: 0.726) less severe than death within 24 hours (IW: 1.000). Major stroke causing permanent disability was considered worse than death within 24 hours (IW: 1.209). Ranking of CEs and the relative values attributed to the CEs differed among subgroups based on gender, age and educational level.ConclusionPatients attribute different weight to individual CEs. This has significant implications for the interpretation of clinical trial data.

Understanding the Use of Composite Endpoints in Clinical Trials.

Clinicians, institutions, healthcare networks, and policymakers use outcomes reported in clinical trials as the basis for medical decision-making when managing individual patients or populations. Therefore, the choice of a valid primary endpoint is crucial for randomized controlled trials (RCT) to demonstrate efficacy of new therapies. Recent improvements in treatment, however, have led to a decline in the morbidity and mortality of several common diseases, resulting in a reduction in relevant outcomes that can be used as clinical trial endpoints. Composite endpoints have been used as a solution to maintain the feasibility of RCTs, particularly when facing low event rates, high cost, and long follow-up. However, the benefits of using composite endpoints must be weighed against the risks of misinterpretation by clinicians and policymakers, as incorrect interpretation may have a detrimental effect on patients and populations. This paper defines a composite endpoint, discusses the rationale for its use, and provides a practical approach to interpreting results to aid in medical decision-making.

Traditional and new composite endpoints inheart failure clinical trials: facilitating comprehensive efficacy assessments and improving trial efficiency.

Composite endpoints are commonly used as the primary measure of efficacy in heart failure clinical trials to assess the overall treatment effect and to increase the efficiency of trials. Clinical trials still must enrol large numbers of patients to accrue a sufficient number of outcome events and have adequate power to draw conclusions about the efficacy and safety of new treatments for heart failure. Additionally, the societal and health system perspectives on heart failure have raised interest in ascertaining the effects of therapy on outcomes such as repeat hospitalization and the patient's burden of disease. Thus, novel methods for using composite endpoints in clinical trials (e.g. clinical status composite endpoints, recurrent event analyses) are being applied in current and planned trials. Endpoints that measure functional status or reflect the patient experience are important but used cautiously because heart failure treatments may improve function yet have adverse effects on mortality. This paper discusses the use of traditional and new composite endpoints, identifies qualities of robust composites, and outlines opportunities for future research.

The win ratio approach to analyzing composite outcomes: An application to the EVOLVE trial

BackgroundUnlike conventional time-to-event analysis of composite endpoints in clinical trials, the “win ratio” method allows for flexibility in prioritizing their components. Here, we compare the EVOLVE trial findings using the win ratio with those from time-to-event analysis. MethodsExposure: Randomization to cinacalcet or placebo.Outcome: The primary composite endpoint combining all-cause mortality and non-fatal myocardial infarction, hospitalization for unstable angina, heart failure, and peripheral vascular events.Analysis: In an unadjusted analysis, we paired each participant from the cinacalcet arm with every participant from the placebo arm within randomization strata. Pairs were classified as “winners” or “losers,” according to which participant died first during the shared follow-up time, or experienced the next ranked event first. We ranked non-fatal events in two ways: 1) all ranked evenly; and 2) prioritized by their effect on health-related quality of life. The win ratio equaled the total winners divided by total losers. Further analyses were conducted where the win ratio was stratified by, or adjusted for, age. ResultsThe unadjusted win ratio for the primary composite endpoint was 1.09 (95% CI 0.97 to 1.21), a statistically non-significant result which supports the primary trial result — unadjusted hazard ratio 0.93 (95% CI 0.85 to 1.02). Age-stratified analyses showed a nominally significant benefit of cinacalcet (win ratio 1.14, 95% CI 1.04 to 1.26). Ranking of non-fatal outcomes by their relative effects on quality of life did not materially alter the results. ConclusionThe win ratio method corroborated the findings of EVOLVE based on conventional time-to-event analysis.EVOLVE ClinicalTrials.gov number: NCT00345839.

Novel approaches to composite endpoints in clinical trials.

Novel approaches to composite endpoints in clinical trials.

Use of composite endpoints in clinical trials

The success of a confirmatory clinical trial designed to demonstrate the efficacy of a new treatment is highly dependent on the choice of valid primary efficacy endpoint(s). The optimal clinical and statistical situation for the design of such a trial is one that starts with the selection of a single primary efficacy endpoint that completely characterizes the disease under study, admits the most efficient clinical and statistical evaluation of treatment effect, and provides clear and broad interpretation of drug effect. For diseases with multidimensional presentations, however, the selection of such an endpoint may not be possible, and so drug effectiveness is often characterized by the use of composite or multiple efficacy endpoint(s). The use of a composite endpoint with components that are only slightly correlated but not quite dissimilar in their recognized clinical relevance could lead to a more sensitive statistical test and thus, adequately powered trials with smaller sample size. This note discusses the utility of composite endpoints in clinical trials and some of the common approaches for dealing with multiplicity arising from their use.

Multiplicity Issues in Clinical Trials With Multiple Objectives

Modern clinical trials for evaluating efficacy and safety of new treatments frequently include multiple objectives with questions of varying clinical importance. Answering them generally requires performing a number of statistical tests and analyses which raise multiplicity of tests issues. These issues can be complex and multidimensional in nature. For example, one dimension may relate to the assessment of the effects of the treatment on multiple endpoints, the other to the effects of multiple doses of the treatment, and yet another to the type of the tests (e.g., superiority or noninferiority-type tests). Also, the trial may seek claims for treatment benefits either for the total patient population or for targeted subgroups. In addition, there may be interest in finding whether or not a certain consistency of results persists across certain multiple endpoints; in some situations this may be measuring different but critical features of a disorder, or in other situations may be measuring the same underlying pathophysiology of the disorder. Addressing such problems in clinical trials for the purpose of controlling the Type I error rate requires the use of advanced statistical test strategies and methods, some of which have only recently appeared. Actually, the last decade has witnessed a number of novel methods as well as innovative extensions of old methods for addressing complex multiplicity problems in clinical trials. The main purpose of this article is to present—at the conceptual level—how multiplicity issues of confirmatory clinical trials that include multiple objectives can be addressed by using some of these new statistical methods that use α-propagation and gatekeeping concepts. One additional goal of this contribution is to address some issues that often arise in the use of coprimary and composite endpoints in clinical trials.

Bayesian design and analysis of composite endpoints in clinical trials with multiple dependent binary outcomes

The author considers studies with multiple dependent primary endpoints. Testing hypotheses with multiple primary endpoints may require unmanageably large populations. Composite endpoints consisting of several binary events may be used to reduce a trial to a manageable size. The primary difficulties with composite endpoints are that different endpoints may have different clinical importance and that higher-frequency variables may overwhelm effects of smaller, but equally important, primary outcomes. To compensate for these inconsistencies, we weight each type of event, and the total number of weighted events is counted. To reflect the mutual dependency of primary endpoints and to make the weighting method effective in small clinical trials, we use the Bayesian approach. We assume a multinomial distribution of multiple endpoints with Dirichlet priors and apply the Bayesian test of noninferiority to the calculation of weighting parameters. We use composite endpoints to test hypotheses of superiority in single-arm and two-arm clinical trials. The composite endpoints have a beta distribution. We illustrate this technique with an example. The results provide a statistical procedure for creating composite endpoints.

Weighting Composite Endpoints in Clinical Trials: Essential Evidence for the Heart Team

Coronary revascularization trials often use a composite endpoint of major adverse cardiac and cerebrovascular events (MACCE). The usual practice in analyzing data with a composite endpoint is to assign equal weights to each of the individual MACCE elements. Noninferiority margins are used to offset effects of presumably less important components, but their magnitudes are subject to bias. This study describes the relative importance of MACCE elements from a patient perspective. A discrete choice experiment was conducted. Survey respondents were presented with a scenario that would make them eligible for the Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery (SYNTAX) trial three-vessel disease cohort. Respondents chose among pairs of procedures that differed on the 3-year probability of MACCE, potential for increased longevity, and procedure/recovery time. Conjoint analysis derived relative weights for these attributes. In all, 224 respondents completed the survey. The attributes did not have equal weight. Risk of death was most important (relative weight 0.23), followed by stroke (0.18), potential increased longevity and recovery time (each 0.17), myocardial infarction (0.14), and risk of repeat revascularization (0.11). Applying these weights to the SYNTAX 3-year endpoints resulted in a persistent, but decreased margin of difference in MACCE favoring coronary artery bypass graft surgery compared to percutaneous coronary intervention. When labeled only as "procedure A" and "procedure B," 87% of respondents chose coronary artery bypass graft surgery over percutaneous coronary intervention. When procedures were labeled as "coronary stent" and "coronary bypass surgery," only 73% chose coronary artery bypass graft surgery. Procedural preference varied with demographics, sex, and familiarity with the procedures. The MACCE elements do not carry equal weight in a composite endpoint, from a patient perspective. Using a weighted composite endpoint increases the validity of statistical analyses and trial conclusions. Patients are subject to bias by labels when considering coronary revascularization.

The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities

The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities

The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities

The conventional reporting of composite endpoints in clinical trials has an inherent limitation in that it emphasizes each patient's first event, which is often the outcome of lesser clinical importance. To overcome this problem, we introduce the concept of the win ratio for reporting composite endpoints. Patients in the new treatment and control groups are formed into matched pairs based on their risk profiles. Consider a primary composite endpoint, e.g. cardiovascular (CV) death and heart failure hospitalization (HF hosp) in heart failure trials. For each matched pair, the new treatment patient is labelled a 'winner' or a 'loser' depending on who had a CV death first. If that is not known, only then they are labelled a 'winner' or 'loser' depending on who had a HF hosp first. Otherwise they are considered tied. The win ratio is the total number of winners divided by the total numbers of losers. A 95% confidence interval and P-value for the win ratio are readily obtained. If formation of matched pairs is impractical then an alternative win ratio can be obtained by comparing all possible unmatched pairs. This method is illustrated by re-analyses of the EMPHASIS-HF, PARTNER B, and CHARM trials. The win ratio is a new method for reporting composite endpoints, which is easy to use and gives appropriate priority to the more clinically important event, e.g. mortality. We encourage its use in future trial reports.

Methodological issues in the use of composite endpoints in clinical trials: examples from the HIV field

In many fields, the choice of a primary endpoint for a trial is not always the ultimate clinical endpoint of interest, but rather some surrogate endpoint believed to be relevant for predicting the effect of the intervention on the clinical endpoint. The classic example of such a field is clinical HIV treatment research, where a variety of primary endpoints are used to evaluate the efficacy of new antiretroviral drugs or new combinations of existing drugs. The choice of endpoint reflects either the goal of therapy as recommended by treatment guidelines (e.g. rapid virological suppression) or the licensing requirements of official drug approval organizations (e.g. time to loss of virological response [TLOVR]). To review the diversity of endpoints used in recent clinical trials in HIV infection and highlight the methodological issues. We identified articles relating to antiretroviral therapy by searching PubMed and through hand searches of relevant conference abstracts. We restricted the search to randomized controlled trials conducted in HIV-infected adults published/presented from January 2005 until March 2008. We identified 28 trials in antiretroviral-naive patients (i.e. patients who were starting antiretroviral therapy for the first time at the time of randomization) and 23 trials in antiretroviral-experienced patients. Most trials were performed for purposes of drug licensing, but others were focused on strategies of using approved drugs. Most trials (40 of 51) used a composite primary endpoint (TLOVR in 13). Of note, 22 of these 40 studies reported that they had used a purely virological efficacy endpoint, but the primary endpoint was actually a composite one due to the way in which missing data and treatment switches were considered as failures. Examples are restricted to HIV clinical trials. Whilst most current HIV clinical trials use composite primary endpoints, there are substantial differences in the components that make up these endpoints. In HIV and other fields where precise definitions are variable, guidelines for standardization of definition and reporting would greatly improve the ability to compare trial results.

“All for one, one for all”: Evaluating composite endpoints in clinical trials

“All for one, one for all”: Evaluating composite endpoints in clinical trials

Handling multiplicity due to multiple primary or composite endpoints in clinical trials

Handling multiplicity due to multiple primary or composite endpoints in clinical trials