Joint Inference for Competing Risks Survival Data

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2016-07-02

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Abstract

© 2016 American Statistical Association. This article develops joint inferential methods for the cause-specific hazard function and the cumulative incidence function of a specific type of failure to assess the effects of a variable on the time to the type of failure of interest in the presence of competing risks. Joint inference for the two functions are needed in practice because (i) they describe different characteristics of a given type of failure, (ii) they do not uniquely determine each other, and (iii) the effects of a variable on the two functions can be different and one often does not know which effects are to be expected. We study both the group comparison problem and the regression problem. We also discuss joint inference for other related functions. Our simulation shows that our joint tests can be considerably more powerful than the Bonferroni method, which has important practical implications to the analysis and design of clinical studies with competing risks data. We illustrate our method using a Hodgkin disease data and a lymphoma data. Supplementary materials for this article are available online.

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Published Version (Please cite this version)

10.1080/01621459.2015.1093942

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Li, Gang, and Qing Yang (2016). Joint Inference for Competing Risks Survival Data. Journal of the American Statistical Association, 111(515). 10.1080/01621459.2015.1093942 Retrieved from https://hdl.handle.net/10161/16700.

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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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