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Identification of causal effects on binary outcomes using structural mean models

Clarke, PS and Windmeijer, F (2010) 'Identification of causal effects on binary outcomes using structural mean models.' Biostatistics, 11 (4). pp. 756-770. ISSN 1465-4644

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Structural mean models (SMMs) were originally formulated to estimate causal effects among those selecting treatment in randomized controlled trials affected by nonignorable noncompliance. It has already been established that SMMs can identify these causal effects in randomized placebo-controlled trials under fairly weak assumptions. SMMs are now being used to analyze other types of study where identification depends on a no effect modification assumption. We highlight how this assumption depends crucially on the unknown causal model that generated the data, and so is difficult to justify. However, we also highlight that, if treatment selection is monotonic, additive and multiplicative SMMs do identify local (or complier) causal effects, but that the double-logistic SMM estimator does not without further assumptions. We clarify the proper interpretation of inferences from SMMs by means of an application and a simulation study. © 2010 The Author.

Item Type: Article
Uncontrolled Keywords: Humans; Gastrointestinal Hemorrhage; Anti-Inflammatory Agents, Non-Steroidal; Placebos; Treatment Outcome; Models, Statistical; Logistic Models; Monte Carlo Method; Statistics, Nonparametric; Algorithms; Computer Simulation; Cyclooxygenase 2 Inhibitors; Randomized Controlled Trials as Topic; Biostatistics
Subjects: H Social Sciences > HA Statistics
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Institute for Social and Economic Research
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 15 Aug 2013 19:24
Last Modified: 15 Jan 2022 01:02

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