Group-sequential logrank methods for trial designs using bivariate non-competing event-time outcomes

We discuss the multivariate (2L-variate) correlation structure and the asymptotic distribution for the group-sequential weighted logrank statistics formulated when monitoring two correlated event-time outcomes in clinical trials. The asymptotic distribution and the variance–covariance for the 2L-variate weighted logrank statistic are derived as available in various group-sequential trial designs. These methods are used to determine a group-sequential testing procedure based on calendar times or information fractions. We apply the theoretical results to a group-sequential method for monitoring a clinical trial with early stopping for efficacy when the trial is designed to evaluate the joint effect on two correlated event-time outcomes. We illustrate the method with application to a clinical trial and describe how to calculate the required sample sizes and numbers of events.

     

Development of low-volume,high-intensity,aerobic-type interval training for elderly Japanese men: a feasibility study

Background

The purposes of this study were to identify 1) the feasibility of a novel exercise protocol (elderly Japanese male version of high-intensity interval aerobic training: EJ-HIAT) and 2) its preliminary data (%V?O_2peak, rating of perceived exertion) in comparison with traditional moderate-intensity continuous aerobic training (MICT).

Results

Twenty-one sedentary elderly men, aged 60–69 years, performed two exercise protocols: EJ-HIAT, consisting of 3 sets of 2?3-min cycling at 75?85%V?O_2peak with 1??2-min active rests at 50%V?O_2peak between sets, and MICT, consisting of 40-min cycling at 65%V?O_2peak. The completion rate, defined as the rate of participants who 1) did not demand withdrawal, 2) were not interrupted by the tester, and 3) did not change the workload during either exercise protocol, of EJ-HIAT was similar to that of MICT (EJ-HIAT: 100%, MICT: 95.2%). Maximal perceived exertion ratings assessed by Borg scale were also similar between EJ-HIAT and MICT. However, objectively measured maximal intensity assessed by %V?O_2peak was higher for EJ-HIAT than for MICT (EJ-HIAT: 86.0 ± 5.6%, MICT: 67.1 ± 6.4%).

Conclusion

These results suggested that EJ-HIAT has good feasibility and perceived exertion similar to MICT despite having higher objectively measured intensity than MICT. An intervention aimed as identifying the effects of EJ-HIAT on exercise tolerance should be performed in the future.

Trial registration

UMIN000021185 (February 26, 2016).

     

A convenient formula for sample size calculations in clinical trials with multiple co-primary continuous endpoints

The clinical efficacy of a new treatment may often be better evaluated by two or more co-primary endpoints. Recently, in pharmaceutical drug development, there has been increasing discussion regarding establishing statistically significant favorable results on more than one endpoint in comparisons between treatments, which is referred to as a problem of multiple co-primary endpoints. Several methods have been proposed for calculating the sample size required to design a trial with multiple co-primary correlated endpoints. However, because these methods require users to have considerable mathematical sophistication and knowledge of programming techniques, their application and spread may be restricted in practice. To improve the convenience of these methods, in this paper, we provide a useful formula with accompanying numerical tables for sample size calculations to design clinical trials with two treatments, where the efficacy of a new treatment is demonstrated on continuous co-primary endpoints. In addition, we provide some examples to illustrate the sample size calculations made using the formula. Using the formula and the tables, which can be read according to the patterns of correlations and effect size ratios expected in multiple co-primary endpoints, makes it convenient to evaluate the required sample size promptly.     

Sample size determination for clinical trials with co‐primary outcomes: exponential event times

Clinical trials with event‐time outcomes as co‐primary contrasts are common in many areas such as infectious disease, oncology, and cardiovascular disease. We discuss methods for calculating the sample size for randomized superiority clinical trials with two correlated time‐to‐event outcomes as co‐primary contrasts when the time‐to‐event outcomes are exponentially distributed. The approach is simple and easily applied in practice. Copyright © 2012 John Wiley & Sons, Ltd.