Bayesian Machine Learning Counterfactual Treatment Selection for Survival Outcome

Thomas M. Menino Convention & Exhibition Center 

Background: Atrial fibrillation (AF) carries high morbidity, and response to catheter ablation varies widely. Existing personalized treatment approaches focus on simple outcomes, while survival and time‑dependent treatment effects are rarely considered.
Method: We propose a Bayesian causal model to identify patient profiles showing differential treatment benefit for time‑to‑event outcomes. Using DECAAF II data, we modeled time to AF recurrence with a survival random forest using baseline covariates and treatment. Synthetic patient profiles were generated by bootstrap resampling, and counterfactual survival predictions were obtained for both treatments. Benefit at 270 days was defined as the difference in predicted survival risk. These differences were used to build an interpretable treatment‑effect tree. Real trial participants were then projected onto subgroups and annotated with observed 270‑day cumulative incidence and censoring rates.
Evaluation: Evaluation used a model‑based mortality index, defined as the difference in cumulative hazard at 270 days. Real patients showed subgroup‑specific patterns consistent with predictions, identifying groups favoring each treatment.

Bayesian prediction

Individualized treatment rules (ITRs)

Subgroup Identification

Random forests

Treatment effect heterogeneity

Clinical Trial 

Biopharmaceutical Section