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To compare different methods to handle treatment when developing a prognostic model that aims to produce accurate probabilities of the outcome of individuals if left untreated.Simulations were performed based on two normally distributed predictors, a binary outcome, and a binary treatment, mimicking a randomised trial or an observational study. Comparison was made between simply ignoring treatment (SIT), restricting the analytical dataset to untreated individuals (AUT), inverse probability weighting (IPW), and explicit modelling of treatment (MT). Methods were compared in terms of predictive performance of the model and the proportion of incorrect treatment decisions.Omitting a genuine predictor of the outcome from the prognostic model decreased model performance, in both an observational study and a randomised trial. In randomised trials, the proportion of incorrect treatment decisions was smaller when applying AUT or MT, compared to SIT and IPW. In observational studies, MT was superior to all other methods regarding the proportion of incorrect treatment decisions.If a prognostic model aims to produce correct probabilities of the outcome in the absence of treatment, ignoring treatments that affect that outcome can lead to suboptimal model performance and incorrect treatment decisions. Explicitly modeling treatment is recommended.

Type

Journal article

Journal

Journal of clinical epidemiology

Publication Date

04/2016

Addresses

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands. Electronic address: r.h.h.groenwold@umcutrecht.nl.