Inverse probability weighting to control confounding in an illness‐death model for interval‐censored data. (4th December 2017)

• Record Type: Journal Article
• Authors: Gillaizeau, Florence; Sénage, Thomas; Le Borgne, Florent; Le Tourneau, Thierry; Roussel, Jean‐Christian; Leffondrè, Karen; Porcher, Raphaël; Giraudeau, Bruno; Dantan, Etienne; Foucher, Yohann
• Is Part Of: Statistics in medicine. Volume 37:Number 8(2018)
• Page Start: 1245
• Page End: 1258
• Publication Date: 2017-12-04
• Subjects: confounding factors -- inverse probability weighting -- multistate -- propensity score -- semi‐Markov; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.7550
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Multistate models with interval‐censored data, such as the illness‐death model, are still not used to any considerable extent in medical research regardless of the significant literature demonstrating their advantages compared to usual survival models. Possible explanations are their uncommon availability in classical statistical software or, when they are available, by the limitations related to multivariable modelling to take confounding into consideration. In this paper, we propose a strategy based on propensity scores that allows population causal effects to be estimated: the inverse probability weighting in the illness semi‐Markov model with interval‐censored data. Using simulated data, we validated the performances of the proposed approach. We also illustrated the usefulness of the method by an application aiming to evaluate the relationship between the inadequate size of an aortic bioprosthesis and its degeneration or/and patient death. We have updated the R package multistate to facilitate the future use of this method.