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Indexed by:期刊论文

Date of Publication:2017-04-01

Journal:LIFETIME DATA ANALYSIS

Included Journals:SCIE、PubMed

Volume:23

Issue:2

Page Number:183-206

ISSN No.:1380-7870

Key Words:Censoring; Gauss-Hermite numerical integration; Laplace approximation; Logit transformation; Logistic-normal distribution; Random effects; Simplex distribution

Abstract:Motivated by the joint analysis of longitudinal quality of life data and recurrence free survival times from a cancer clinical trial, we present in this paper two approaches to jointly model the longitudinal proportional measurements, which are confined in a finite interval, and survival data. Both approaches assume a proportional hazards model for the survival times. For the longitudinal component, the first approach applies the classical linear mixed model to logit transformed responses, while the second approach directly models the responses using a simplex distribution. A semiparametric method based on a penalized joint likelihood generated by the Laplace approximation is derived to fit the joint model defined by the second approach. The proposed procedures are evaluated in a simulation study and applied to the analysis of breast cancer data motivated this research.