2LMBA - Laboratoire de Mathématiques de Bretagne Atlantique (Université de Brest, 6 avenue Le Gorgeu, CS 93837, 29238 Brest cedex 3 / Université de Bretagne-Sud, Centre Yves Coppens, Bât. B, Campus de Tohannic, BP 573, 56017 Vannes - France)
Abstract : In the context of chronic diseases, patient's health evolution is often evaluated through the study of longitudinal markers and major clinical events such as relapses or death. Dynamic predictions of such types of events may be useful to improve patients management all along their follow-up. Dynamic predictions consist of predictions that are based on information repeatedly collected over time, such as measurements of a biomarker, and that can be updated as soon as new information becomes available. Several techniques to derive dynamic predictions have already been suggested, and computation of dynamic predictions is becoming increasingly popular. In this work, we focus on assessing predictive accuracy of dynamic predictions and suggest that using an R(2) -curve may help. It facilitates the evaluation of the predictive accuracy gain obtained when accumulating information on a patient's health profile over time. A nonparametric inverse probability of censoring weighted estimator is suggested to deal with censoring. Large sample results are provided, and methods to compute confidence intervals and bands are derived. A simulation study assesses the finite sample size behavior of the inference procedures and illustrates the shape of some R(2) -curves which can be expected in common settings. A detailed application to kidney transplant data is also presented.
https://www.hal.inserm.fr/inserm-01885569 Contributor : Nicolas DegauqueConnect in order to contact the contributor Submitted on : Tuesday, September 21, 2021 - 7:47:46 PM Last modification on : Wednesday, April 27, 2022 - 4:02:17 AM Long-term archiving on: : Wednesday, December 22, 2021 - 7:29:33 PM
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Marie-Cecile Fournier, Etienne Dantan, Paul Blanche. An R(2) -curve for evaluating the accuracy of dynamic predictions.. Statistics in Medicine, Wiley-Blackwell, 2018, 37 (7), pp.1125-1133. ⟨10.1002/sim.7571⟩. ⟨inserm-01885569⟩