title: Instrumental variable method for time-to-event data using a pseudo-observation approach.
abstract: Observational studies are often in peril of unmeasured confounding. Instrumental variable analysis is a method for controlling for unmeasured confounding. As yet, theory on instrumental variable analysis of censored time-to-event data is scarce. We propose a pseudo-observation approach to instrumental variable analysis of the survival function, the restricted mean, and the cumulative incidence function in competing risks with right-censored data using generalized method of moments estimation. For the purpose of illustrating our proposed method, we study antidepressant exposure in pregnancy and risk of autism spectrum disorder in offspring, and the performance of the method is assessed through simulation studies.
authors.affiliation: Section for Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark.
authors.forename: Maiken I S
authors.initials: MI
authors.lastname: Kjaersgaard
authors.affiliation: Section for Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark.
authors.forename: Erik T
authors.initials: ET
authors.lastname: Parner
mesh_terms: D000928:Antidepressive Agents / Q000494:pharmacology; D000067877:Autism Spectrum Disorder / Q000139:chemically induced; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D015994:Incidence; D008431:Maternal-Fetal Exchange; D015233:Models, Statistical; D064887:Observational Studies as Topic / Q000706:statistics & numerical data*; D011247:Pregnancy; D016019:Survival Analysis
chemical_list: D000928:Antidepressive Agents
keywords: Generalized method of moments (GMM); Instrumental variables; Pseudo-observations; Time-to-event