Individual dynamic predictions using landmarking and joint modelling: Validation of estimators and robustness assessment. (December 2019)

• Record Type: Journal Article
• Authors: Ferrer, Loïc; Putter, Hein; Proust-Lima, Cécile
• Is Part Of: Statistical methods in medical research. Volume 28:Number 12(2019)
• Page Start: 3649
• Page End: 3666
• Publication Date: 2019-12
• Subjects: Competing risks -- dynamic prediction -- landmarking -- joint modelling -- prediction accuracy -- robustness; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280218811837
• Languages: English
• ISSNs: 0962-2802

Abstract

After the diagnosis of a disease, one major objective is to predict cumulative probabilities of events such as clinical relapse or death from the individual information collected up to a prediction time, usually including biomarker repeated measurements. Several competing estimators have been proposed, mainly from two approaches: joint modelling and landmarking. These approaches differ by the information used, the model assumptions and the complexity of the computational procedures. This paper aims to review the two approaches, precisely define the derived estimators of dynamic predictions and compare their performances notably in case of misspecification. The ultimate goal is to provide key elements for the use of individual dynamic predictions in clinical practice. Prediction of two competing causes of prostate cancer progression from the history of prostate-specific antigen is used as a motivated example. We formally define the quantity to estimate and its estimators, propose techniques to assess the uncertainty around predictions and validate them. We then conduct an in-depth simulation study compare the estimators in terms of prediction error, discriminatory power, efficiency and robustness to model assumptions. We show that prediction tools should be handled with care, in particular by properly specifying models and estimators.