Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies
- 1 June 1979
- journal article
- research article
- Published by Taylor & Francis in Journal of the American Statistical Association
- Vol. 74 (366a) , 318-328
- https://doi.org/10.1080/01621459.1979.10482513
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.Keywords
This publication has 19 references indexed in Scilit:
- Bayesian Inference for Causal Effects: The Role of RandomizationThe Annals of Statistics, 1978
- Assignment to Treatment Group on the Basis of a CovariateJournal of Educational Statistics, 1977
- Multivariate Matching Methods That are Equal Percent Bias Reducing, I: Some ExamplesBiometrics, 1976
- Multivariate Matching Methods That are Equal Percent Bias Reducing, II: Maximums on Bias Reduction for Fixed Sample SizesBiometrics, 1976
- The Effect of Bias on Estimators of Relative Risk for Pair-Matched and Stratified SamplesJournal of the American Statistical Association, 1975
- The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational StudiesPublished by JSTOR ,1973
- Matching to Remove Bias in Observational StudiesPublished by JSTOR ,1973
- The Computerized Construction of a Matched SampleAmerican Journal of Sociology, 1970
- Large-Sample Covariance Analysis when the Control Variable is FallibleJournal of the American Statistical Association, 1960
- Matching in Analytical StudiesAmerican Journal of Public Health and the Nations Health, 1953