This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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216
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Type:
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Invited
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Date/Time:
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Monday, August 2, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Health Policy Statistics Section
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Abstract - #306043 |
Title:
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Conditional Independence Proofs for Generalizability
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Author(s):
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Sue Marcus*+
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Companies:
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Columbia University
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Address:
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722 West 168th Street, New York, NY, 10032,
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Keywords:
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causal effects ;
generalizability ;
conditional independence ;
randomized ;
nonrandomized ;
propensity scores
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Abstract:
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The idea of probabilistic independence as a basic intuitive concept with formal rules for inference was first proposed by Dawid (1979). Since then, there has been interest in the relationship between conditional independence and graphical models for causal inference (Pearl and Rax, 1987). Using the formal rules of conditional independence, we provide a formal proof for generalizing the impact of a treatment versus control from an RCT to a target population that may differ from the randomized trial population. We implement these results using full matching on the propensity score. The results are illustrated using several different hybrid randomized and nonrandomized designs.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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