In observational epidemiology, mendelian randomisation (MR) studies provide an opportunity to study the causal association between an exposure and an outcome while reducing the risk of certain biases Little consensus exists around the reporting of MR studies, and the quality of reporting of these studies has been inconsistent; many MR study reports do not state or examine the various assumptions of MR and report insufficient details on the data sources STROBE-MR (strengthening the reporting of observational studies in epidemiology using mendelian randomisation), a checklist of 20 reporting items, has been developed for the communication of MR studies This article explains the rationale of these checklist items and provides examples of transparent reporting MR study authors, reviewers, and journal editors are encouraged to use STROBE-MR to improve the reporting of these studies on 3 November 2021 by guest. Protected by copyright.

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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.