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Explicit inclusion of treatment in prognostic modelling was recommended in observational and randomised settings

Explicit inclusion of treatment in prognostic modelling was recommended in observational and randomised settings Thumbnail


Abstract

Objectives
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.

Study Design and Setting
Simulations were performed based on two normally distributed predictors, a binary outcome, and a binary treatment, mimicking a randomized trial or an observational study. Comparison was made between simply ignoring treatment (SIT), restricting the analytical data set to untreated individuals (AUT), inverse probability weighting (IPW), and explicit modeling of treatment (MT). Methods were compared in terms of predictive performance of the model and the proportion of incorrect treatment decisions.

Results
Omitting a genuine predictor of the outcome from the prognostic model decreased model performance, in both an observational study and a randomized trial. In randomized 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.

Conclusion
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.

Citation

(2016). Explicit inclusion of treatment in prognostic modelling was recommended in observational and randomised settings. Journal of Clinical Epidemiology, 90-100. https://doi.org/10.1016/j.jclinepi.2016.03.017

Acceptance Date Mar 23, 2016
Publication Date Apr 1, 2016
Journal Journal of Clinical Epidemiology
Print ISSN 0895-4356
Publisher Elsevier
Pages 90-100
DOI https://doi.org/10.1016/j.jclinepi.2016.03.017
Keywords Prognosis, Models, Statistical, Computer simulation, Decision support techniques, Calibration
Publisher URL http://dx.doi.org/10.1016/j.jclinepi.2016.03.017

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