Applied Statistics - Donald Rubin 

 This week, the Applied Statistics Workshop will present a talk by Donald Rubin, the John Loeb Professor of Statistics at Harvard.  Professor Rubin has published widely on numerous topics in statistics, and is perhaps best known for his work on missing data and causal inference.  His articles have appeared in over thirty journals, and he is the author or co-author of several books on missing data, causal inference, and Bayesian data analysis, many of which are the standards in their fields.  In 1995, Professor Rubin received the Samuel S. Wilks Memorial Award from the American Statistical Association. 

 Professor Rubin will present a talk entitled "Principal Stratification for Causal Inference with Extended Partial Compliance," which is based on joint work with Hui Jin.  Their paper is available from the workshop website. The presentation will be at noon on Wednesday, February 28 in Room N354, CGIS North, 1737 Cambridge St. As always, lunch will be provided. An abstract of the paper follows on the jump: 


 
Principal Stratification for Causal Inference with Extended Partial Compliance

 Hui Jin and Donald B. Rubin 

 Abstract 

 Many double-blind placebo-controlled randomized experiments with active drugs suffer from complications beyond simple noncompliance. First, the compliance with assigned dose is often partial, with patients taking only part of the assigned dose, whether active or placebo. Second, the blinding may be imperfect in the sense that there may be detectable positive or negative side-effects of the active drug, and consequently, simple compliance has to be extended to allow different compliances to active drug and placebo. Efron and Feldman (1991) presented an analysis of such a situation and discussed inference for dose-response from the non-randomized data in the active treatment arm, which stimulated active discussion, including concerning the role of the intention-to-treat principle in such studies. Here, we formulate the problem within the principal stratification framework of Frangakis and Rubin (2002), which adheres to the intention-to-treat principle, and we present a new analysis of the Efron-Feldman data within this framework. Moreover, we describe precise assumptions under which dose-response can be inferred from such non-randomized data, which seem debatable in the setting of this example. Although this article only deals in detail with the specific Efron-Feldman data, the same framework can be applied to various circumstances in both natural science and social science.