Robust Joint Modeling of Longitudinal Measurements and Competing Risks Failure Time Data |
| |
Authors: | Ning Li Robert M Elashoff Gang Li |
| |
Institution: | 1. Department of Biomathematics, University of California at Los Angeles, 10833 Leconte Ave, Box 951766, Los Angeles, CA 90095‐1766, USA;2. Department of Biostatistics, School of Public Health, University of California at Los Angeles, Los Angeles, CA 90095, USA |
| |
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 (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim) |
| |
Keywords: | Cause‐specific hazard EM algorithm Joint modeling Longitudinal data Non‐ignorable missing data Robust inference |
|
|