Estimating effects of time-varying exposures on mortality risk.

Administrative databases have become an increasingly popular data source for population-based health research. We explore how mortality risk is associated with some health service utilization process via linked administrative data. A generalized Cox regression model is proposed using a time-dependent stratification variable to summarize lifetime service utilization. Recognizing the service utilization over time as an internal covariate in the survival analysis, conventional likelihood methods are inapplicable. We present an estimating function based procedure for estimating model parameters, and provide a testing procedure for updating the stratification levels. The proposed approach is examined both asymptotically and numerically via simulation. We motivate and illustrate the proposed approach using an on-going program pertaining to opioid agonist treatment (OAT) management for individuals identified with opioid use disorders. Our analysis of the OAT data indicates that the OAT effect on mortality risk decreases in successive OAT attempts, in which two risk classes based on an individual's treatment episode number are established: one with 1–3 OAT episodes, and the other with 4+ OAT episodes. [ABSTRACT FROM AUTHOR]