June 1, 1979Journal of the American Statistical Association715 citations
Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies
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Key Points
- The study aims to evaluate the effectiveness of multivariate matched sampling and regression adjustment in reducing bias related to matching variables in observational studies.
- Utilized Monte Carlo methods for simulation experiments.
- Applied nearest available Mahalanobis metric matching, followed by regression adjustment on matched pair differences.
- Focused on cases where dependent variables show moderate nonlinearity in matching variables.
- Multivariate matched sampling combined with regression adjustment significantly reduced bias due to matching variables.
- The approach demonstrated higher efficacy compared to traditional methods in controlling bias effects.
- Findings indicate strong performance across various conditions of nonlinearity in the dependent variables.
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
Cite This Study
Donald B. Rubin (1979) studied this question.