Estimation of Treatment Effects for Heterogeneous Matched Pairs Data with Probit Models

Abstract

Estimating the effect of medical treatments on subject responses is one of the crucial problems in medical research. Matched-pairs designs are commonly implemented in the field of medical research to eliminate confounding and improve efficiency. In this article, new estimators of treatment effects for heterogeneous matched pairs data are proposed. Asymptotic properties of the proposed estimators are derived. Simulation studies show that the proposed estimators have some advantages over the famous Heckman's estimator and inverse probability weighted (IPW) estimator. We apply the proposed methodologies to a blood lead level data set and an acute leukaemia data set.