Estimating Subject-Specific Dependent Competing Risk Profile with Censored Event Time Observations
Article first published online: 9 JUL 2010
© 2010, The International Biometric Society
Volume 67, Issue 2, pages 427–435, June 2011
How to Cite
Li, Y., Tian, L. and Wei, L.-J. (2011), Estimating Subject-Specific Dependent Competing Risk Profile with Censored Event Time Observations. Biometrics, 67: 427–435. doi: 10.1111/j.1541-0420.2010.01456.x
- Issue published online: 20 JUN 2011
- Article first published online: 9 JUL 2010
- Received July 2009. Revised February 2010. Accepted March 2010.
- Local likelihood function;
- Nonparametric function estimation;
- Perturbation-resampling method;
- Risk index score
Summary In a longitudinal study, suppose that the primary endpoint is the time to a specific event. This response variable, however, may be censored by an independent censoring variable or by the occurrence of one of several dependent competing events. For each study subject, a set of baseline covariates is collected. The question is how to construct a reliable prediction rule for the future subject's profile of all competing risks of interest at a specific time point for risk-benefit decision making. In this article, we propose a two-stage procedure to make inferences about such subject-specific profiles. For the first step, we use a parametric model to obtain a univariate risk index score system. We then estimate consistently the average competing risks for subjects who have the same parametric index score via a nonparametric function estimation procedure. We illustrate this new proposal with the data from a randomized clinical trial for evaluating the efficacy of a treatment for prostate cancer. The primary endpoint for this study was the time to prostate cancer death, but had two types of dependent competing events, one from cardiovascular death and the other from death of other causes.