Sample size for a non-inferiority clinical trial with time-to-event data in the presence of competing risks

Authors: Dong Han, Zheng Chen, Yawen Hou

Journal of Biopharmaceutical Statistics, 2018, 28:797-807
24 pages, 1 figure

Abstract: The analysis and planning methods for competing risks model have been described in the literatures in recent decades, and non-inferiority clinical trials are helpful in current pharmaceutical practice. Analytical methods for non-inferiority clinical trials in the presence of competing risks were investigated by Parpia et al., who indicated that the proportional sub-distribution hazard model is appropriate in the context of biological studies. However, the analytical methods of competing risks model differ from those appropriate for analyzing non-inferiority clinical trials with a single outcome; thus, a corresponding method for planning such trials is necessary. A sample size formula for non-inferiority clinical trials in the presence of competing risks based on the proportional sub-distribution hazard model is presented in this paper. The primary endpoint relies on the sub-distribution hazard ratio. A total of 120 simulations and an example based on a randomized controlled trial verified the empirical performance of the presented formula. The results demonstrate that the empirical power of sample size formulas based on the Weibull distribution for non-inferiority clinical trials with competing risks can reach the targeted power.

Submitted to arXiv on 28 Feb. 2018

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.