DAE

Abstract

Inverse probability weighting is commonly used in two situations. First, it is used in a propensity score approach to deal with confounding in non-randomised studies of effect of treatment on outcome. Here weights are inverse probabilities of assignment to active treatment. Second, it is used to correct bias arising when an analysis model is tted to incomplete data by restricting to complete cases. Here weights are inverse probabilities of being a complete case.

Usually weights are estimated by regressing an indicator of whether the individual receives active treatment (in the first situation) or is a complete case (in the second) on a set of predictors.

Problems arise when these predictors can be missing. In this presentation, I shall discuss a method that involves multiply imputing these missing predictors.