Predicting time-to-event outcomes under different intervention strategies: methods and applications

Abstract

This dissertation focuses on developing and applying estimation methods to make “what if” predictions in clinical research. The primary aim of these methods is to estimate risks for time-to-event outcomes using observational data in a manner that supports valid prediction under different intervention strategies, potentially conditional on patient and treatment characteristics. To this end, we formulate the identifiability assumptions required to ensure the

validity of these estimates.

Causal inferencePredictions under interventionsInverse probability weightingSurvival analysisObservational dataMulti-state models