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arXiv · 2004.06998

Prediction meets causal inference: the role of treatment in clinical prediction models

Nan van Geloven·Sonja Swanson·Chava Ramspek·Kim Luijken·Merel van Diepen·Tim Morris·Rolf Groenwold·Hans van Houwelingen·Hein Putter·Saskia le Cessie
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Abstract

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.

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Nan van Geloven, Sonja Swanson, Chava Ramspek, Kim Luijken, Merel van Diepen, Tim Morris, Rolf Groenwold, Hans van Houwelingen, Hein Putter, Saskia le Cessie. 2020-04-15. Prediction meets causal inference: the role of treatment in clinical prediction models. https://doi.org/10.1007/s10654-020-00636-1

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