A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations

Liangyuan Hu, Jiayi Ji, Hao Liu, Ronald Ennis

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodology that generates causal effects, assesses the heterogeneity of the effects and adjusts for the clustered nature of the data. This study uses a state-of-the-art machine learning survival model, riAFT-BART, to draw causal inferences about individual survival treatment effects, while accounting for the variability in institutional effects; further, it proposes a data-driven approach to agnostically (as opposed to a priori hypotheses) ascertain which subgroups exhibit an enhanced treatment effect from which intervention, relative to global evidence—average treatment effects measured at the population level. Comprehensive simulations show the advantages of the proposed method in terms of bias, efficiency and precision in estimating heterogeneous causal effects. The empirically validated method was then used to analyze the National Cancer Database.

Original languageEnglish
Article number14903
JournalInternational Journal of Environmental Research and Public Health
Volume19
Issue number22
DOIs
StatePublished - Nov 2022
Externally publishedYes

Keywords

  • causal inference
  • clustering
  • machine learning
  • survival data analysis
  • treatment effect heterogeneity

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