Counterfactual Phenotyping with Censored Time-to-Events
This work addresses the challenge of counterfactual reasoning in clinical interventions for researchers and practitioners, though it appears incremental as it builds on existing latent variable and treatment effect estimation methods.
The paper tackled the problem of estimating treatment efficacy for time-to-event outcomes with censoring by proposing a latent variable model to decouple confounding physiological characteristics from intervention effects, demonstrating its ability to discover actionable phenotypes in cardiovascular risk clinical trials.
Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring. Counterfactual reasoning in such scenarios requires decoupling the effects of confounding physiological characteristics that affect baseline survival rates from the effects of the interventions being assessed. In this paper, we present a latent variable approach to model heterogeneous treatment effects by proposing that an individual can belong to one of latent clusters with distinct response characteristics. We show that this latent structure can mediate the base survival rates and helps determine the effects of an intervention. We demonstrate the ability of our approach to discover actionable phenotypes of individuals based on their treatment response on multiple large randomized clinical trials originally conducted to assess appropriate treatments to reduce cardiovascular risk.