|
Abstract:
|
In this talk we focus on linear regression with missing covariates and a right censored outcome. We consider a general two-phase outcome sampling design, where full covariate information is only ascertained for subjects in phase two and sampling occurs under an independent Bernoulli sampling scheme with known subject-specific sampling probabilities that depend on phase one information (e.g., survival time, failure status, and covariates). We introduce a class of augmented estimators that is shown to improve asymptotic efficiency over simple but inefficient inverse probability of sampling weighted estimators. We provide asymptotic results for the augmented estimators, and evaluate finite sample performance using simulations and data from the National Wilm's Tumor Study.
|
