Robust joint modeling of longitudinal measurements and competing risks failure time data

Biom J. 2009 Feb;51(1):19-30. doi: 10.1002/bimj.200810491.


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Biometry / methods*
  • Computer Simulation
  • Confidence Intervals
  • Data Interpretation, Statistical*
  • Longitudinal Studies*
  • Models, Statistical*
  • Risk Assessment / methods*
  • Risk Factors
  • Statistical Distributions