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

Group sequential design with maximin efficiency robust test for immunotherapy with generalized delayed treatment effect

The delayed treatment effect is a common feature of immunotherapy, characterized by a gradual onset of action ranging from no effect to full effect. In this study, we propose a generalized delayed treatment effect function to depict the delayed effective process precisely and flexibly. To reduce potential power loss caused by the delayed treatment effect in a group sequential trial, we employ the maximin efficiency robust test, which enhances power robustness across a range of possible delays. We present novel approaches based on the Markov chain method for determining group sequential boundaries, calculating the power function, and estimating the maximum sample size through iterative regressions between the square root of the maximum sample size and the normal quantile of power. Extensive simulation studies validate the effectiveness of our approaches, particularly in balanced trials, demonstrating the validity of group sequential boundaries and the accuracy of maximum sample size estimations. Additionally, we utilize a real trial as an example to compare our considered trial with group sequential trials using the log-rank and generalized piecewise weighted log-rank tests. The results show significantly reduced maximum sample sizes, highlighting the economic advantage of our approach.

On selecting the critical boundary functions in group-sequential trials with two time-to-event outcomes

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.

Interim Monitoring for Futility in Clinical Trials with Two Co-primary Endpoints Using Prediction

We discuss using prediction as a flexible and practical approach for monitoring futility in clinical trials with two co-primary endpoints. This approach is appealing in that it provides quantitative evaluation of potential effect sizes and associated precision, and can be combined with flexible error-spending strategies. We extend prediction of effect size estimates and the construction of predicted intervals to the two co-primary endpoints case, and illustrate interim futility monitoring of treatment effects using prediction with an example. We also discuss alternative approaches based on the conditional and predictive powers, compare these methods and provide some guidance on the use of prediction for better decision in clinical trials with co-primary endpoints.