Composite Partial Likelihood Estimation Under Length-Biased Sampling, with Application to a Prevalent Cohort Study of Dementia

• Activity Number: 596
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Chiung-Yu Huang*+ and Jing Qin
• Companies: National Institute of Allergy and Infectious Diseases and National Institutes of Health, BRB
• Keywords: Backward and forward recurrence time ; Cross-sectional sampling ; Left truncation ; Renewal processes

Abstract

The Canadian Study of Health and Aging employed a prevalent cohort design to study survival after onset of dementia, where patients with dementia were sampled and the onset time of dementia was determined retrospectively. This sampling scheme favors individuals who survive longer. In recent years, there has been a rising interest in developing estimation procedures for prevalent cohort survival data that not only account for length bias but also actually exploit the incidence distribution of the disease to improve efficiency. We consider semiparametric estimation of the Cox model for the time from dementia onset to death under a stationarity assumption with respect to the disease incidence. Under the stationarity condition, the semiparametric maximum likelihood estimation is expected to be fully efficient yet difficult to perform for statistical practitioners, as the likelihood depends on the baseline hazard function in a complicated way. Motivated by the composite likelihood method (Besag, 1974), we develop a composite partial likelihood method that retains the simplicity of the popular partial likelihood estimator and can be easily performed using standard statistical software.