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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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The use of bootstrapping when using propensity-score matching without replacement: a simulation study.

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  • 1Institute for Clinical Evaluative Sciences, Toronto, Canada; Institute of Health Management, Policy and Evaluation, University of Toronto, Toronto, Canada; Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Canada.

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Summary

Estimating standard errors in propensity-score matching (PSM) is crucial for reliable results. This study proposes two bootstrap methods for PSM, finding that resampling matched pairs offers more accurate standard error estimates.

Keywords:
Monte Carlo simulationsbootstrapmatchingpropensity scorepropensity-score matchingvariance estimation

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Observational Studies

Background:

  • Propensity-score matching (PSM) is vital for causal inference from observational data.
  • Accurate standard error estimation in PSM is critical for valid statistical inference (confidence intervals, hypothesis tests).
  • Current literature lacks consensus on optimal standard error estimation methods for PSM.

Purpose of the Study:

  • To propose and evaluate novel bootstrap methods for standard error estimation in propensity-score matching without replacement.
  • To compare the performance of these bootstrap methods against empirical distributions.

Main Methods:

  • Two bootstrap methods were developed for PSM without replacement.
  • Method 1: Resampling from the matched pairs within the PSM sample.
  • Method 2: Resampling from the original data, re-estimating propensity scores, and re-matching within each bootstrap sample.
  • Monte Carlo simulations were used to assess performance.

Main Results:

  • Method 1 (resampling matched pairs) yielded standard error estimates closer to the empirical standard deviation.
  • This suggests Method 1 provides more accurate variance estimation for PSM.

Conclusions:

  • The proposed bootstrap method of resampling matched pairs is recommended for standard error estimation in propensity-score matching without replacement.
  • This approach enhances the reliability of statistical inference in observational studies using PSM.