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Adaptive pair-matching in randomized trials with unbiased and efficient effect estimation.

Laura B Balzer1, Maya L Petersen, Mark J van der Laan

  • 1Division of Biostatistics, University of California, Berkeley, CA, 94110-7358, U.S.A.

Statistics in Medicine
|November 26, 2014
PubMed
Summary

Adaptive pair-matching in randomized trials creates dependent units, impacting standard analysis. New methods offer more precise estimation of treatment effects, improving study power and validity.

Keywords:
adaptive designscausal inferenceefficiencypair-matchingrandomized trialstargeted minimum loss based estimation (TMLE)

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Pair-matching is a common strategy in randomized trials to enhance validity and power.
  • Standard analysis often assumes independent pairs, which may not hold with adaptive matching.
  • Adaptive pair-matching uses baseline covariates to form pairs, creating dependent units.

Purpose of the Study:

  • To explore the consequences of adaptive pair-matching in randomized trials.
  • To investigate methods for estimating the average treatment effect conditional on baseline covariates.
  • To compare precision of conditional effect estimators versus marginal effect estimators.

Main Methods:

  • Exploration of adaptive pair-matching design in randomized controlled trials.
  • Comparison of unadjusted estimators with targeted minimum loss-based estimation (TMLE).
  • Analysis of dependent units arising from adaptive pair-matching.

Main Results:

  • Adaptive pair-matching leads to dependent units, violating standard assumptions.
  • Estimators of the conditional average treatment effect can be more precise than marginal effect estimators.
  • Substantial efficiency gains observed from matching and further gains with adjustment.

Conclusions:

  • Adaptive pair-matching requires specialized statistical approaches beyond standard assumptions.
  • Targeted minimum loss-based estimation offers improved precision for treatment effect estimation in such designs.
  • Findings are motivated by the Sustainable East Africa Research in Community Health study evaluating antiretroviral therapy for HIV prevention.