A flexible approach for assessing heterogeneity of causal treatment effects on patient survival using large datasets with clustered observations

HIGHLIGHTS

• who: Liangyuan Hu and colleagues from the Department of Biostatistics and Epidemiology, Rutgers University, New Brunswick, NJ, USA have published the research: A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations, in the Journal: (JOURNAL)
• what: The authors demonstrate a methodology that generates causal effects assesses the heterogeneity of the effects and adjusts for the nature of the data.
• how: The authors developed a random-intercept accelerated failure time model leveraging a probabilistic machine_learning technique Bayesian additive regression trees (BART) for causal inferences . . .
  

   

  

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