From: Treatment use in prognostic model research: a systematic review of cardiovascular prognostic studies
Design | |
Collect information on treatments used at the study baseline (see Fig. 1) | |
Collect information on treatment drop-in or discontinuation during follow-up (see Fig. 1). | |
If using readily available data (e.g. from an existing cohort or register), consider whether sufficient information on treatment use has been recorded. | |
Analysis | |
Model development | |
Guided treatments: Consider explicitly including treatment use in the prognostic model. If a treatment was randomly allocated (e.g. data from an RCT), consider using only the subset of untreated individuals [8]. | |
Model validation | |
Guided treatments: If treatments were randomly allocated, exclude treated individuals from the analysis. If treatment use is non-random (e.g. data from an observational study or register), consider first using inverse treatment probability weighting before validating the model in the untreated subset [11]. | |
Background treatments: Consider differences in treatment use between the development and validation cohorts when exploring the impact of case-mix on model performance [24,25,26]. | |
Reporting | |
Report information on treatment use at baseline. List any treatments that may have affected the prognosis of individuals in the study and the absolute number (%) treated. | |
Report information on effective treatments used during follow-up and, where relevant, the duration of treatment use. | |
Discuss the potential impact of treatment use on the validity and transportability of the developed prognostic model or estimates of model performance. |