The Stata Journal Promoting communications on statistics and Stata · 2014 · 82 citations · 12 references
Bayesian StatisticBayesian Decision TheoryTreatment EffectBayesian InferenceCausal InferencePharmacodynamic ModelingGeneralized Propensity ScoreBiostatisticsBayesian MethodsRadiation Therapy PlanningPublic HealthStatisticsBayesian Hierarchical ModelingTherapeutic Drug MonitoringPharmacokinetic ModelingEstimation StatisticDose–response FunctionMarginal Structural ModelsDosimetryBayesian StatisticsRadiation DoseTime-varying ConfoundingDose-response ProgramStatistical InferenceMedicine
In this article, we revise the estimation of the dose–response function described in Hirano and Imbens (2004, Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 73–84) by proposing a flexible way to estimate the generalized propensity score when the treatment variable is not necessarily normally distributed. We also provide a set of programs that accomplish this task. To do this, in the existing Dose-response program (Bia and Mattei, 2008, Stata Journal 8: 354–373), we substitute the maximum likelihood estimator in the first step of the computation with the more flexible generalized linear model.
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