title: A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all-or-nothing compliance.
abstract: Survival data from randomized trials are most often analyzed in a proportional hazards (PH) framework that follows the intention-to-treat (ITT) principle. When not all the patients on the experimental arm actually receive the assigned treatment, the ITT-estimator mixes its effect on treatment compliers with its absence of effect on noncompliers. The structural accelerated failure time (SAFT) models of Robins and Tsiatis are designed to consistently estimate causal effects on the treated, without direct assumptions about the compliance selection mechanism. The traditional PH-model, however, has not yet led to such causal interpretation. In this article, we examine a PH-model of treatment effect on the treated subgroup. While potential treatment compliance is unobserved in the control arm, we derive an estimating equation for the Compliers PROPortional Hazards Effect of Treatment (C-PROPHET). The jackknife is used for bias correction and variance estimation. The method is applied to data from a recently finished clinical trial in cancer patients with liver metastases.
authors.affiliation: Clinical Biostatistics, Merck Sharp & Dohme (Europe), Clos du Lynx 5, 1200 Woluwe, Belgium. tom_loeys@merck.com
authors.forename: T
authors.initials: T
authors.lastname: Loeys
authors.forename: E
authors.initials: E
authors.lastname: Goetghebeur
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D015982:Bias; D015984:Causality; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008113:Liver Neoplasms / Q000556:secondary / Q000628:therapy; D010349:Patient Compliance; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016896:Treatment Outcome