Sample size determination for jointly testing a cause-specific hazard and the all-cause hazard in the presence of competing risks.

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2017-12-27

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Abstract

This article considers sample size determination for jointly testing a cause-specific hazard and the all-cause hazard for competing risks data. The cause-specific hazard and the all-cause hazard jointly characterize important study end points such as the disease-specific survival and overall survival, which are commonly used as coprimary end points in clinical trials. Specifically, we derive sample size calculation methods for 2-group comparisons based on an asymptotic chi-square joint test and a maximum joint test of the aforementioned quantities, taking into account censoring due to lost to follow-up as well as staggered entry and administrative censoring. We illustrate the application of the proposed methods using the Die Deutsche Diabetes Dialyse Studies clinical trial. An R package "powerCompRisk" has been developed and made available at the CRAN R library.

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10.1002/sim.7590

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Yang, Qing, Wing K Fung and Gang Li (2017). Sample size determination for jointly testing a cause-specific hazard and the all-cause hazard in the presence of competing risks. Stat Med. 10.1002/sim.7590 Retrieved from https://hdl.handle.net/10161/15987.

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Yang

Qing Yang

Associate Research Professor in the School of Nursing

Dr. Qing Yang is Associate Professor and Biostatistician at Duke School of Nursing. She received her PhD in Biostatistics from University of California, Los Angeles. Dr. Yang’s statistical expertise is longitudinal data analysis and time-to-event data analysis. As a biostatistician, she has extensive experience collaborating with researchers in different therapeutic areas, including diabetes, cancer, cardiovascular disease and mental health. Her current research interests are advanced latent variable models that are widely used in symptom cluster research and intensive longitudinal data analysis that arise from mobile health research.


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