Bayesian graphical models, intention-to-treat, and the rubin causal Model
نویسنده
چکیده
In clinical trials with significant noncompliance the standard intention-to-treat analyses sometimes mislead. Rubin’s causal model provides an alternative method of analysis that can shed extra light on clinical trial data. Formulating the Rubin Causal Model as a graphical model facilitates model communication and computation.
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An extended general location model for causal inferences from data subject to noncompliance and missing values.
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