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March 1, 1976Biometrics

Multivariate Matching Methods That are Equal Percent Bias Reducing, II: Maximums on Bias Reduction for Fixed Sample Sizes

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Authors

DRDonald B. Rubin

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Overview

Multivariate matching methods reduce bias in data collection, suggesting optimal sample size ratios.

Key Points

  • This research aims to explore multivariate matching methods that minimize bias from multiple variables.
  • Derivation of a formula for maximum percent reduction in bias under fixed distributions.
  • Evaluation of various multivariate matching methods in controlling bias.
  • Estimation of minimum sample size ratios for achieving well-matched samples.
  • Maximum achievable bias reduction is quantified for fixed distributions and sample sizes.
  • A procedure for determining necessary sample sizes for effective matching is established.

Cite This Study

Donald B. Rubin (1976) studied this question.

synapsesocial.com/papers/6a0b3d57db419d24cd5d0019https://doi.org/10.2307/2529343
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies1979 · 711 citations
  2. 2Affinely Invariant Matching Methods with Ellipsoidal Distributions1992 · 127 citations
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  5. 5Matching with Multiple Criteria and Its Application to Health Disparities Research2026