Adjusted restricted mean survival times in observational studies. (22nd May 2019)

• Record Type: Journal Article
• Authors: Conner, Sarah C.; Sullivan, Lisa M.; Benjamin, Emelia J.; LaValley, Michael P.; Galea, Sandro; Trinquart, Ludovic
• Is Part Of: Statistics in medicine. Volume 38:Number 20(2019)
• Page Start: 3832
• Page End: 3860
• Publication Date: 2019-05-22
• Subjects: inverse probability weighting -- observational studies -- propensity score -- restricted mean survival time -- survival analysis -- time‐to‐event data; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8206
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : In observational studies with censored data, exposure‐outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre‐specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression‐based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model‐free method of taking the area under the survival function. We derive the adjusted RMST by integrating an adjusted Kaplan‐Meier estimator with inverse probability weighting (IPW). The adjusted difference in RMSTs is the area between the two IPW‐adjusted survival functions. In a Monte Carlo‐type simulation study, we demonstrate that the proposed estimator performs as well as two regression‐based approaches: the ANCOVA‐type method of Tian et al and the pseudo‐observation method of Andersen et al. We illustrate the methods by reexamining the association between total cholesterol and the 10‐year risk of coronary heart disease in the Framingham Heart Study.