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When treatment use is non-random, IPW followed by the exclusion of treated individuals is recommended, however, this method is sensitive to violations of its assumptions.
Prognostic models have a range of applications, from risk stratification, to use in making individualized predictions to help counsel patients or guide healthcare providers when deciding whether or not to recommend a certain treatment or intervention .
A summary of the methods, including additional technical details can be found in Table - Provides correct estimates of performance in (untreated) target population if treatment use is or is not associated with other prognostic factors, provided key assumptions of IPW are met.†- Does not provide correct estimates in the presence of non-positivity, or when there are unobserved predictors that are strongly associated with both the outcome and use of treatment [- Does not affect discrimination.- Not sufficient to correct calibration if relative treatment effects are heterogeneous or use is associated with an individual’s risk.- Adjusts for other differences in case-mix leading to misleading estimates of the calibration of the original model.
A common and straightforward approach to remove the effects of treatment is to exclude from the analysis individuals in the validation data set who received treatment.
However, it is not clear to what extent treatment use in a validation set might influence the observed performance of a prognostic model that was developed in a treatment-naïve population, or how one can account for additional treatment use in a validation set in order to correctly estimate how a prognostic model would perform in its target (untreated) population using a treated validation set.