Semiparametric analysis of mixture regression models with competing risks data

Lifetime Data Analysis
Wenbin Lu, Limin Peng

Abstract

In the analysis of competing risks data, cumulative incidence function is a useful summary of the overall crude risk for a failure type of interest. Mixture regression modeling has served as a natural approach to performing covariate analysis based on this quantity. However, existing mixture regression methods with competing risks data either impose parametric assumptions on the conditional risks or require stringent censoring assumptions. In this article, we propose a new semiparametric regression approach for competing risks data under the usual conditional independent censoring mechanism. We establish the consistency and asymptotic normality of the resulting estimators. A simple resampling method is proposed to approximate the distribution of the estimated parameters and that of the predicted cumulative incidence functions. Simulation studies and an analysis of a breast cancer dataset demonstrate that our method performs well with realistic sample sizes and is appropriate for practical use.

References

Jan 1, 1975·Proceedings of the National Academy of Sciences of the United States of America·A Tsiatis
Jan 1, 1993·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·F J CummingsJ M Bennett

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Citations

Jan 26, 2010·Statistics in Medicine·M A NicolaieH Putter
Oct 29, 2008·Journal of Traumatic Stress·David R GagnonCasey T Taft
Jun 22, 2019·Biometrical Journal. Biometrische Zeitschrift·Mengjiao Peng, Liming Xiang
Nov 20, 2013·Mathematical Biosciences and Engineering : MBE·Antonio Di CrescenzoBarbara Martinucci
Nov 20, 2013·Mathematical Biosciences and Engineering : MBE·Miguel Lara-AparicioBeatriz Fuentes-Pardo
Jan 13, 2015·Journal of the Royal Statistical Society. Series B, Statistical Methodology·Ruosha Li, Limin Peng
Feb 28, 2017·Journal of the Royal Statistical Society. Series B, Statistical Methodology·Lu Mao, D Y Lin

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