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Generalizing Evidence from Randomized Trials using Inverse Probability of Sampling Weights.

Ashley L Buchanan1, Michael G Hudgens2, Stephen R Cole3

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|December 18, 2018
PubMed
Summary

Generalizing randomized trial results to broader populations is challenging. This study introduces an inverse probability of sampling weighted (IPSW) estimator to improve the generalizability of clinical trial findings, demonstrated with HIV treatment data.

Keywords:
Causal inferenceExternal validity/GeneralizabilityHIV/AIDSInverse probability weightsRandomized controlled trialTarget population

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Randomized trials (RCTs) are crucial for evidence-based medicine.
  • Generalizability of RCT findings to diverse target populations is often limited.
  • The sampling mechanism in RCTs is typically unknown, hindering external validity.

Purpose of the Study:

  • To develop and validate a method for generalizing clinical trial results to target populations.
  • To address the challenge of unknown sampling mechanisms in RCTs.
  • To provide a statistically sound approach for enhancing external validity.

Main Methods:

  • Introduction of an inverse probability of sampling weighted (IPSW) estimator.
  • Derivation of a consistent sandwich-type variance estimator.
  • Comparison of IPSW estimator with a previously proposed stratified estimator via simulations.
  • Application to generalize findings from HIV treatment RCTs.

Main Results:

  • The IPSW estimator is demonstrated to be consistent and asymptotically normal.
  • Simulation results show the performance of the IPSW estimator.
  • The methods successfully generalized HIV treatment trial results to the US population with HIV.

Conclusions:

  • The IPSW estimator offers a robust method for improving the generalizability of randomized trial findings.
  • This approach enhances the external validity of clinical research.
  • The methodology is applicable to real-world public health challenges, such as generalizing HIV treatment outcomes.