BITES: balanced individual treatment effect for survival data.

ABSTRACT:

Motivation

Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes). Several methods were suggested for this scenario based on observational data, i.e. data where the intervention was not applied randomly, for both continuous and binary outcome variables. However, patient outcome is often recorded in terms of time-to-event data, comprising right-censored event times if an event does not occur within the observation period. Albeit their enormous importance, time-to-event data are rarely used for treatment optimization. We sugges

SUBMITTER: Schrod S 

PROVIDER: S-EPMC9235492 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature