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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big SPECIAL DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Monday, March 14, 2005}
\centerline{Herrin T175}

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\centerline{\sl Donald Rubin}
\centerline{Department of Statistics}
\centerline{Harvard University}
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\centerline{\bf Principal Stratification for Causal Inference with Extended Partial Noncompliance}
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Abstract:

Many double-blind placebo-controlled randomized experiments with active
drugs suffer from complications involving 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 extended to allow different compliances to active
drug and placebo. Efron \& Feldman (1991, JASA) presented an analysis of
such a situation and discussed inference for dose-response from the
nonrandomized 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,
Biometrics), 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 the nonrandomized data, which seem debatable in this
setting. This is joint work with Hui Jin, Harvard University.

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