Prediction meets causal inference: the role of treatment in clinical prediction models
In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a ‘predictimand’ framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference.
|Keywords||Censoring, Clinical prediction model, Estimands, Predictimands, Treatment|
|Persistent URL||dx.doi.org/10.1007/s10654-020-00636-1, hdl.handle.net/1765/127452|
|Journal||European Journal of Epidemiology|
van Geloven, N, Swanson, S.A, Ramspek, C.L. (Chava L.), Luijken, K. (Kim), van Diepen, M, Morris, T.P. (Tim P.), … le Cessie, S. (2020). Prediction meets causal inference: the role of treatment in clinical prediction models. European Journal of Epidemiology. doi:10.1007/s10654-020-00636-1