Statistical analysis

  • Date: 11/13/2008

Jack Kalbfleisch (Department of Biostatistics, University of Michigan)


University of British Columbia


Statistical analysis of Illness Death and
Risk Data


Place: WMAX 110, West Mall, UBC


Semi-competing risks data frequently arise in clinical and
observational studies. In these cases, the subject can experience
both non-terminal and terminal events where the terminal event (e.g.,
death) censors the non-terminal event (e.g., relapse) but not
vice-versa. Typically, the two events are correlated. An approach
based on latent failure times has been advocated for the analysis of
such data, where the joint survival function of two event times is
assumed to follow a copula function over the positive quadrant with
observation restricted to the upper wedge. We argue, that similar to
models for competing risks, latent failure times should generally be
avoided in modeling such data. We consider an illness-death process
which circumvents any need for latent times and provides for easy
incorporation of covariates. Nonparametric maximum likelihood
estimation is used for inference, a simple iterative procedure is
developed and needed asymptotic results are obtained. Simulation
studies are conducted to assess the finite sample performance of the
proposed estimators and the methods are illustrated in an analysis of
data on nasopharyngeal cancer from a randomized clinical trial in Singapore.


Jiahua Chen (