Conference Agenda

A key aim of clinical prediction models (or prognostic models) is to provide individualized risk estimates that support patients and doctors in making treatment decisions. However, as most prediction models are derived from observational data where some individuals already received the treatment the model aims to inform, standard prediction methods often fail to provide the necessary information for this.

Causal predictions (also called counterfactual predictions or predictions under intervention) have recently gain traction as an alternative way to support treatment decisions. These are estimates of risks under specified treatment strategies, for example a patient’s baseline risk `if they do not initiate the treatment’ or the risk ‘if they do initiate the treatment’. A major challenge in estimating and evaluating causal predictions in observational data is confounding adjustment.

In this talk, I will outline how causal inference methods such as marginal structural models and the clone-censor-reweight approach can be adapted for the purpose of developing and evaluating causal predictions of time-to-event outcomes.