title: Robust joint modeling of longitudinal measurements and competing risks failure time data.
abstract: Existing methods for joint modeling of longitudinal measurements and survival data can be highly influenced by outliers in the longitudinal outcome. We propose a joint model for analysis of longitudinal measurements and competing risks failure time data which is robust in the presence of outlying longitudinal observations during follow-up. Our model consists of a linear mixed effects sub-model for the longitudinal outcome and a proportional cause-specific hazards frailty sub-model for the competing risks data, linked together by latent random effects. Instead of the usual normality assumption for measurement errors in the linear mixed effects sub-model, we adopt a t -distribution which has a longer tail and thus is more robust to outliers. We derive an EM algorithm for the maximum likelihood estimates of the parameters and estimate their standard errors using a profile likelihood method. The proposed method is evaluated by simulation studies and is applied to a scleroderma lung study.
authors.affiliation: Department of Biomathematics, University of California at Los Angeles, 90095-1766, USA. ningli@ucla.edu
authors.forename: Ning
authors.initials: N
authors.lastname: Li
authors.forename: Robert M
authors.initials: RM
authors.lastname: Elashoff
authors.forename: Gang
authors.initials: G
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D008137:Longitudinal Studies; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D016008:Statistical Distributions