Optimal exact tests for multiple binary endpoints

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Robin Ristl, Dong Xi, Ekkehard Glimm, Martin Posch
Journal/Conference Name Computational Statistics & Data Analysis
Paper Category
Paper Abstract In confirmatory clinical trials with small sample sizes, hypothesis tests based on asymptotic distributions are often not valid and exact non-parametric procedures are applied instead. However, the latter are based on discrete test statistics and can become very conservative, even more so, if adjustments for multiple testing as the Bonferroni correction are applied. Improved exact multiple testing procedures are proposed for the setting where two parallel groups are compared in multiple binary endpoints. Based on the joint conditional distribution of test statistics of Fisher’s exact tests, optimal rejection regions for intersection hypothesis tests are constructed utilizing different objective functions. Depending on the optimization objective, the optimal test yields maximal power under a specific alternative, maximal exhaustion of the nominal type I error rate, or the largest possible rejection region controlling the type I error rate. To efficiently search the large space of possible rejection regions, an optimization algorithm based on constrained optimization and integer linear programming is proposed. Applying the closed testing principle, optimized multiple testing procedures with strong familywise error rate control are constructed. Furthermore, a computationally efficient greedy algorithm for nearly optimal tests is proposed. The unconditional power of the optimized procedures is numerically compared to the power of alternative approaches and the optimal tests are illustrated with a clinical trial example in a rare disease. The described methods are implemented in the R package multfisher.
Date of publication 2018
Code Programming Language R
Comment

Copyright Researcher 2021