title: Estimating restricted mean treatment effects with stacked survival models.
abstract: The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such imbalances is estimating a covariate-adjusted restricted mean difference by modeling the covariate-adjusted survival distribution and then marginalizing over the covariate distribution. Because the estimator for the restricted mean difference is defined by the estimator for the covariate-adjusted survival distribution, it is natural to expect that a better estimator of the covariate-adjusted survival distribution is associated with a better estimator of the restricted mean difference. We therefore propose estimating restricted mean differences with stacked survival models. Stacked survival models estimate a weighted average of several survival models by minimizing predicted error. By including a range of parametric, semi-parametric, and non-parametric models, stacked survival models can robustly estimate a covariate-adjusted survival distribution and, therefore, the restricted mean treatment effect in a wide range of scenarios. We demonstrate through a simulation study that better performance of the covariate-adjusted survival distribution often leads to better mean squared error of the restricted mean difference although there are notable exceptions. In addition, we demonstrate that the proposed estimator can perform nearly as well as Cox regression when the proportional hazards assumption is satisfied and significantly better when proportional hazards is violated. Finally, the proposed estimator is illustrated with data from the United Network for Organ Sharing to evaluate post-lung transplant survival between large-volume and small-volume centers. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Minneapolis Medical Research Foundation, Minneapolis, MN, U.S.A.|Biostatistics and Data Management Core, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii.
authors.forename: Andrew
authors.initials: A
authors.lastname: Wey
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: David M
authors.initials: DM
authors.lastname: Vock
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: John
authors.initials: J
authors.lastname: Connett
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: Kyle
authors.initials: K
authors.lastname: Rudser
mesh_terms: D003198:Computer Simulation; D016016:Proportional Hazards Models; D015996:Survival Rate
keywords: bias-variance tradeoff; proportional hazards assumption; restricted mean difference; stacked survival models; survival analysis