title: Survival analysis without survival data: connecting length-biased and case-control data.
abstract: We show that relative mean survival parameters of a semiparametric log-linear model can be estimated using covariate data from an incident sample and a prevalent sample, even when there is no prospective follow-up to collect any survival data. Estimation is based on an induced semiparametric density ratio model for covariates from the two samples, and it shares the same structure as for a logistic regression model for case-control data. Likelihood inference coincides with well-established methods for case-control data. We show two further related results. First, estimation of interaction parameters in a survival model can be performed using covariate information only from a prevalent sample, analogous to a case-only analysis. Furthermore, propensity score and conditional exposure effect parameters on survival can be estimated using only covariate data collected from incident and prevalent samples.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington 98195, U.S.A. kcgchan@u.washington.edu.
authors.forename: Kwun Chuen Gary
authors.initials: KC
authors.lastname: Chan
keywords: Accelerated failure time model; Biased sampling; Empirical likelihood; Prevalent cohort; Propensity score; Proportional mean residual life model