Estimating disease prevalence from two-phase surveys with non-response at the second phase

In this paper we compare several methods for estimating population disease prevalence from data collected by two-phase sampling when there is non-resp onse at the second phase. The traditional weighting type estimator requires the missing completely at random assumption and may yield biased estimates if the assumption does not hold. We review two approaches and propose one new approach to adjust for non-response assuming that the non-response depe nds on a set of covariates collected at the first phase: an adjusted weight ing type estimator using estimated response probability from a response mod el; a modelling type estimator using predicted disease probability from a d isease model; and a regression type estimator combining the adjusted weight ing type estimator and the modelling type estimator. These estimators are i llustrated using data from an Alzheimer's disease study in two populations. Simulation results are presented to investigate the performances of the pr oposed estimators under various situations. Copyright (C) 2000 John Wiley & Sons, Ltd.