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The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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Propensity score matching and complex surveys.

Peter C Austin1,2,3, Nathaniel Jembere1, Maria Chiu1

  • 11 Institute for Clinical Evaluative Sciences, Ontario, Canada.

Statistical Methods in Medical Research
|July 28, 2016
PubMed
Summary
This summary is machine-generated.

Propensity score matching in complex surveys is complex. Retaining natural survey weights for matched controls improved covariate balance and reduced bias, unlike inherited weights. Bootstrap methods effectively estimated treatment effect variances for binary outcomes.

Keywords:
Monte Carlo simulationsPropensity scorepropensity score matchingsurvey

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survey Methodology

Background:

  • Complex sample surveys are vital for estimating treatment effects.
  • Propensity score methods are popular but under-researched for complex survey data.
  • Confounding is a key challenge in observational studies.

Purpose of the Study:

  • To investigate propensity score matching implementation in complex surveys.
  • To examine propensity score model formulation and weight handling for matched controls.
  • To assess methods for estimating treatment effect variances.

Main Methods:

  • Monte Carlo simulations were used to evaluate different propensity score model formulations.
  • Two key issues were examined: propensity score model specification and weight assignment for matched controls.
  • Bootstrap methods were assessed for variance estimation with binary outcomes.

Main Results:

  • Propensity score model formulation results were inconclusive.
  • Retaining natural survey weights for matched controls generally led to better covariate balance and reduced bias compared to inherited weights.
  • Bootstrap methods demonstrated good performance for estimating variances of binary treatment effects.

Conclusions:

  • The choice of weight handling in propensity score matching impacts covariate balance and bias.
  • Bootstrap-based variance estimation is a reliable approach for binary outcomes in complex surveys.
  • Methods were illustrated using Canadian Community Health Survey data on education and mood/anxiety disorders.