Seminars

This talk  discusses cure detection  among the prostate cancer
patients in the NIH population-based Surveillance Epidemiology and End
Results (SEER) program, wherein the main endpoint (e.g.  deaths from
prostate cancer) and the censoring causes (e.g. deaths from heart
diseases) may be dependent. While a  number of authors  have
studied  the  mixture survival model to analyze survival data
with nonnegligible cure fractions, none has studied the mixture cure
model in the presence of dependent censoring. To account for such
dependence, we propose a  more general class of cure models that
allow for dependent censoring.  We derive the cure  models from
the perspective of competing risks and model the dependence between the
censoring time and the survival time using a class of  Archimedean
copula models. Within this framework, we consider the parameter
estimation,  the cure detection, and the  two- sample comparison of latency 
distributions in the presence of dependent censoring when a proportion of 
patients is deemed cured. Large sample results using the martingale theory 
are obtained. We examine the finite sample performance of the proposed 
methods via simulation and apply them to analyze the SEER prostate cancer data.