title: Causal inference in survival analysis using pseudo-observations.
abstract: Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: Section of Biostatistics, University of Copenhagen, Ø. Farimagsgade 5, Copenhagen, PB 2099, DK-1014, Denmark.
authors.forename: Per K
authors.identifier: http://orcid.org/0000-0003-0490-0885
authors.initials: PK
authors.lastname: Andersen
authors.affiliation: Section of Biostatistics, University of Copenhagen, Ø. Farimagsgade 5, Copenhagen, PB 2099, DK-1014, Denmark.|Department of Health Sciences, College of Medicine Biological Sciences and Psychology, University of Leicester, University Road, Leicester, LE1 7RH, U.K.
authors.forename: Elisavet
authors.initials: E
authors.lastname: Syriopoulou
authors.affiliation: Department of Biostatistics, University of Aarhus, Bartholins Allé 2, Aarhus C, DK-8000, Denmark.
authors.forename: Erik T
authors.initials: ET
authors.lastname: Parner
mesh_terms: D015982:Bias; D015984:Causality; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D005260:Female; D006086:Graft vs Host Disease; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000401:mortality / Q000628:therapy; D008297:Male; D009010:Monte Carlo Method; D019653:Myeloablative Agonists; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016019:Survival Analysis; D016896:Treatment Outcome
chemical_list: D019653:Myeloablative Agonists
keywords: G-formula; causal inference; propensity score; pseudo-observations; right-censoring; survival data