Identification of causal effects on binary outcomes using structural mean models.
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.
Clarke
Paul S
PS
Centre for Market & Public Organisation, University of Bristol, 1TX, UK. paul.clarke@bristol.ac.uk
Windmeijer
Frank
F
D000465:Algorithms; D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000009:adverse effects; D056808:Biostatistics / Q000379:methods; D003198:Computer Simulation; D052246:Cyclooxygenase 2 Inhibitors / Q000009:adverse effects; D006471:Gastrointestinal Hemorrhage / Q000139:chemically induced / Q000517:prevention & control; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D010919:Placebos; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D018709:Statistics, Nonparametric; D016896:Treatment Outcome
D000894:Anti-Inflammatory Agents, Non-Steroidal; D052246:Cyclooxygenase 2 Inhibitors; D010919:Placebos