Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies

Abstract

Abstract Monte Carlo methods are used to study the efficacy of multivariate matched sampling and regression adjustment for controlling bias due to specific matching variables X when dependent variables are moderately nonlinear in X. The general conclusion is that nearest available Mahalanobis metric matching in combination with regression adjustment on matched pair differences is a highly effective plan for controlling bias due to X. Key Words: Covariance adjustmentNonrandomized studiesQuasi-experiments