Romin Pajouheshnia

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OBJECTIVES To compare different methods to handle treatment when developing a prognostic model that aims to produce accurate probabilities of the outcome of individuals if left untreated. STUDY DESIGN AND SETTING Simulations were performed based on two normally distributed predictors, a binary outcome, and a binary treatment, mimicking a randomized trial(More)
BACKGROUND Prognostic models often show poor performance when applied to independent validation data sets. We illustrate how treatment use in a validation set can affect measures of model performance and present the uses and limitations of available analytical methods to account for this using simulated data. METHODS We outline how the use of(More)
BACKGROUND It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. METHODS A framework for(More)
Background: Ignoring treatments in prognostic model development or validation can affect the accuracy and transportability of models. We aim to quantify the extent to which the effects of treatment have been addressed in existing prognostic model research and provide recommendations for the handling and reporting of treatment use in future studies. Methods:(More)
Title: Treatment Use in Prognostic Model Research: a Systematic Review of Cardiovascular Prognostic Studies Authors: Romin Pajouheshnia ( Johanna Damen ( Rolf Groenwold ( Karel Moons ( Linda Peelen ( Version: 1 Date: 01 Sep(More)
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