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A comparison of reweighting estimators of average treatment effects in real world populations

Chen-Yen Lin1, Eloise Kaizar2, Douglas Faries1

  • 1Eli Lilly and Company, Indianapolis, Indiana, USA.

Pharmaceutical Statistics
|March 6, 2021
PubMed

Insights

Statistical methods can generalize randomized controlled trial (RCT) findings to real-world populations by weighting trial participants. The best approach depends on population characteristics and selection bias.

Area of Science:

  • Biostatistics
  • Health Services Research
  • Clinical Trial Methodology

Background:

  • Regulatory agencies approve interventions based on randomized controlled trials (RCTs).
  • Healthcare stakeholders need real-world effectiveness data beyond idealized RCT settings.
  • Real-world effectiveness evidence is typically unavailable at the time of initial approval.

Purpose of the Study:

  • To evaluate statistical methods for generalizing RCT treatment effects to target populations.
  • To identify optimal estimation and weight construction procedures for real-world effectiveness.

Main Methods:

  • Utilized weighting procedures to adjust RCT participant data.
  • Compared various estimation and weight construction methods.
  • Employed simulations and a real-world Alzheimer's disease clinical trial dataset.

Main Results:

  • The optimal statistical approach for generalizing RCT results is contingent upon population characteristics.
  • Factors influencing the best method include selection bias and treatment effect heterogeneity.
  • Demonstrated the application using a real-world Alzheimer's disease intervention study.

Conclusions:

  • Statistical generalization methods can bridge the gap between RCT efficacy and real-world effectiveness.
  • Careful consideration of source and target population attributes is crucial for accurate estimation.
  • Provides guidance for stakeholders seeking to understand intervention performance in diverse populations.
Keywords:
entropyexternal validitypropensityweight estimationweight trimming

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