Methods to assess sufficient cause interactions are well developed for binary outcomes. We extend these methods to handle time-to-event outcomes, which occur frequently in medicine and epidemiology. Based on theory for marginal structural models in continuous time, we show how to assess sufficient cause interaction nonparametrically, allowing for censoring and competing risks. We apply the method to study interaction between intensive blood pressure therapy and statin treatment on all-cause mortality.
From the aDepartment of Biostatistics, Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo
bDepartment of Medicine, Diakonhjemmet hospital, Oslo.
Submitted May 29, 2018; accepted November 26, 2018.
The authors were all supported by the research grant NFR239956/F20—Analyzing clinical health registries: Improved software and mathematics of identifiability.
Disclosures: The authors report no conflicts of interest..
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
The SPRINT data can be obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center. Our simulation scenarios and all the R code are available in the online supplement material.
Correspondence: Mats Julius Stensrud, Domus Medica, Postboks 1122 Blindern, 0317 Oslo. E-mail: firstname.lastname@example.org