Background: Competing risks endpoints are used when patients can fail therapy from several causes. Analyzing these outcomes allows one to assess the direct benefit of treatment on a primary cause of failure in a clinical trial setting. Regression models can be used in clinical trials to adjust for residual imbalances in patient characteristics, improving the power to detect treatment differences. But, more efficient clinical trial designs, such as group sequential trials and adaptive designs, have not been extensively studied with competing risks endpoints, especially when covariate adjustment is used in the analysis. This proposal aims to expand the set of design and analysis options for clinical trials with competing risks outcomes, which is limited.
Specific Aims : This study will develop new competing risks methods for clinical trials including 1) a group sequential test for treatment effect based on the Fine-Gray regression model, 2) a group sequential test for treatment effect based on direct binomial regression of cumulative incidence at a fixed time point, and 3) a method permitting use of Gray's test in an adaptive trial that allows modifications based on all interim data. Research Plan and Methods: For each of the tests in specific aims 1 and 2, the goal is deriving the asymptotic distribution of the sequence of test statistics in a group sequential trial. Once this distribution is known, early stopping boundaries of the trial can be chosen to satisfy type I error rate and power requirements. Martingale and empirical process theory will be used to derive these results; the powerful tools provided by this theory are well-suited to time to event data and have been utilized to obtain methods for both fixed sample and group sequential trials. The plan for completing specific aim 3 involves application of the extended Conditional Rejection Principle approach (Irle & Schafer, 2012) to an adaptive trial using Gray's test to analyze a competing risks endpoint. This approach allows use of the full interim data for modifications while maintaining the type I error rate. Showing applicability of this method to Gray's test will involve use of martingale and empirical process theory. Simulation studies will be conducted to verify the asymptotic results and to examine the finite sample properties of the proposed methods. Relevance to the NHLBI's mission: This project aligns with the NHLBI's mission of promoting research and training to promote the prevention and treatment of heart, lung, and blood diseases. The proposed methods will find application in trials where onset of, or death from, a specific type of heart, lung, or blood diseases is the primary interest. The methodology will be illustrated on a clinical trial designed to determine whether a new agent reduces the risk of graft versus host disease after a blood or marrow transplant, where death is treated as a competing risk. This work will provide more efficient and flexible design options for clinical trials like this with competing risks, including an adaptive design with use of full interim information and group sequential testing for a treatment effect that adjusts for covariates.

Public Health Relevance

The purpose of this project is to improve clinical trial designs which utilize competing risks data, where patients can fail therapy from several causes. These include monitoring the trial results on an ongoing basis to consider stopping the study early, as well as adaptively modifying the trial design in response to the ongoing results. These improvements can require smaller sample sizes and shorten the trial duration; in turn, newly developed effective treatments will be made more readily available for the patients that can benefit from them.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Predoctoral Individual National Research Service Award (F31)
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Special Emphasis Panel (ZRG1-F16-E (09)F)
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Wolz, Michael
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Medical College of Wisconsin
Internal Medicine/Medicine
Schools of Medicine
United States
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Martens, Michael J; Logan, Brent R (2018) A group sequential test for treatment effect based on the Fine-Gray model. Biometrics 74:1006-1013