Mixture Modeling of Time-to-Event Data in the Proportional Odds Model

Xifen Huang, Chaosong Xiong, Jinfeng Xu (), Jianhua Shi and Jinhong Huang
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Xifen Huang: School of Mathematics, Yunnan Normal University, Kunming 650092, China
Chaosong Xiong: School of Mathematics, Yunnan Normal University, Kunming 650092, China
Jinfeng Xu: School of Mathematics, Minnan Normal University, Zhangzhou 363000, China
Jianhua Shi: School of Mathematics, Minnan Normal University, Zhangzhou 363000, China
Jinhong Huang: School of Mathematics, Minnan Normal University, Zhangzhou 363000, China

Mathematics, 2022, vol. 10, issue 18, 1-11

Abstract: Subgroup analysis with survival data are most essential for detailed assessment of the risks of medical products in heterogeneous population subgroups. In this paper, we developed a semiparametric mixture modeling strategy in the proportional odds model for simultaneous subgroup identification and regression analysis of survival data that flexibly allows the covariate effects to differ among several subgroups. Neither the membership or the subgroup-specific covariate effects are known a priori. The nonparametric maximum likelihood method together with a pair of MM algorithms with monotone ascent property are proposed to carry out the estimation procedures. Then, we conducted two series of simulation studies to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of German breast cancer data is further provided for illustrating the proposed methodology.

Keywords: heterogeneous covariate effects; mixture of proportional odds model; MM algorithm; nonparametric maximum likelihood (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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