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Area of Science:

  • Causal inference
  • Biostatistics
  • Epidemiology

Background:

  • Generalizing causal effects from randomized controlled trials (RCTs) to target populations is crucial for real-world application.
  • Missing data in both RCTs and observational datasets pose significant challenges to existing generalization methods like inverse propensity sampling weighting.

Purpose of the Study:

  • To develop and evaluate novel statistical methods for generalizing causal effects from RCTs to target populations, specifically addressing the issue of missing covariate data.
  • To compare the performance of multiple imputation strategies and a direct estimation approach in handling missing data for treatment effect generalization.

Main Methods:

  • Proposed three multiple imputation strategies (separate, joint with fixed effect, joint ignoring source) to handle missing values in multisource data.
  • Introduced a direct estimation approach treating incomplete covariates as semidiscrete variables.
  • Conducted an extensive simulation study to assess the performance and assumptions of the proposed methods.

Main Results:

  • The choice of missing data handling strategy significantly impacts the generalized causal effect estimates.
  • The proposed methods offer viable alternatives for generalizing treatment effects in the presence of missing covariate data.
  • The study highlights the importance of carefully considering missing data mechanisms and data structures when generalizing findings.

Conclusions:

  • Novel multiple imputation and direct estimation methods effectively address missing data challenges in causal effect generalization.
  • The findings emphasize the critical role of appropriate missing data handling in ensuring reliable generalization of treatment effects from RCTs to target populations.
  • The developed methods are applicable to real-world scenarios, as demonstrated by the analysis of trauma patient data and a tranexamic acid RCT.