Designs incorporating more than one endpoint have become popular in drug development. One of such designs allows for incorporation of short‐term information in an interim analysis if the long‐term primary endpoint has not been yet observed for some of the patients. At first we consider a two‐stage design with binary endpoints allowing for futility stopping only based on conditional power under both fixed and observed effects. Design characteristics of three estimators: using primary long‐term endpoint only, short‐term endpoint only, and combining data from both are compared. For each approach, equivalent cut‐off point values for fixed and observed effect conditional power calculations can be derived resulting in the same overall power. While in trials stopping for futility the type I error rate cannot get inflated (it usually decreases), there is loss of power. In this study, we consider different scenarios, including different thresholds for conditional power, different amount of information available at the interim, different correlations and probabilities of success. We further extend the methods to adaptive designs with unblinded sample size reassessments based on conditional power with inverse normal method as the combination function. Two different futility stopping rules are considered: one based on the conditional power, and one from P‐values based on Z‐statistics of the estimators. Average sample size, probability to stop for futility and overall power of the trial are compared and the influence of the choice of weights is investigated.

Background Peritoneal dialysis (PD) is complicated by a high rate of adverse events that might be attributed to cytotoxicity of currently used PD fluids. However, clinical development of novel PD fluids is virtually non-existent, in part due to difficulties in recruiting sufficiently large populations for adequately powered trials. The aim of this study is to understand the potential impact of introducing composite outcomes on clinical trial feasibility in PD. Methods A composite outcome “major adverse peritoneal events (MAPE)” was designed to combine clinically relevant complications of PD, such as ( 1 ) technical failure (cause-specific for peritonitis and/or insufficient dialysis), ( 2 ) peritonitis, and ( 3 ) peritoneal membrane deterioration. Incidence rates of individual endpoints were obtained from the literature and expert panel estimations, and population sizes were computed based on Chi-square test for adequately powered confirmatory randomized controlled clinical trials with 2 parallel arms. Results Incidence rates for technical failure, peritonitis, and peritoneal membrane deterioration were estimated at 15%, 50%, and 23%, respectively, at 2 years follow-up, with adequate agreement between the literature and expert opinion. Assuming that a given intervention reduces adverse outcomes by 30%, an adequately powered clinical trial needs to recruit up to 1,720 patients when studying individual outcomes. Combining endpoints increases power in simulated trials despite considerable overlap, and the composite outcome MAPE reduces the required population to 202 patients aiming for 80% power. Conclusion Introduction of the composite outcome MAPE, covering relevant major adverse peritoneal events, may improve the feasibility of clinical trials to adequately test novel PD fluids.

In confirmatory cancer clinical trials, overall survival (OS) is normally a primary endpoint in the intention-to-treat (ITT) analysis under regulatory standards. After the tumor progresses, it is common that patients allocated to the control group switch to the experimental treatment, or another drug in the same class. Such treatment switching may dilute the relative efficacy of the new drug compared to the control group, leading to lower statistical power. It would be possible to decrease the estimation bias by shortening the follow-up period but this may lead to a loss of information and power. Instead we propose a modified weighted log-rank test (mWLR) that aims at balancing these factors by down-weighting events occurring when many patients have switched treatment. As the weighting should be pre-specified and the impact of treatment switching is unknown, we predict the hazard ratio function and use it to compute the weights of the mWLR. The method may incorporate information from previous trials regarding the potential hazard ratio function over time. We are motivated by the RECORD-1 trial of everolimus against placebo in patients with metastatic renal-cell carcinoma where almost 80% of the patients in the placebo group received everolimus after disease progression. Extensive simulations show that the new test gives considerably higher efficiency than the standard log-rank test in realistic scenarios.

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