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Activity Number:
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224
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #303468 |
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Title:
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Longitudinal Structural Mixed Models and Causal Inference in Surgical Trials with Noncompliance
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Author(s):
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Colleen Sitlani*+ and Patrick Heagerty
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Companies:
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University of Washington and University of Washington
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Address:
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Dept of Biostatistics, F-600 Health Sciences Building, Seattle, WA, 98195,
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Keywords:
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longitudinal data ; noncompliance ; causal inference ; structural nested mean models ; endogeneity
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Abstract:
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Randomized surgical trials with the goal of evaluating the long-term benefit of surgical intervention as compared to a non-surgical treatment are often faced with serious patient noncompliance. There are several statistical challenges associated with longitudinal 'as-treated' analyses that seek to estimate average causal effects attributable to surgery. We adopt an underlying longitudinal structural mixed model that is a natural example of a structural nested mean model, and then compare the performance of analysis methods when endogenous processes lead to patient crossover. Standard linear mixed models may not be valid yet can perform surprisingly well. In contrast, causal estimation methods such as marginal structural models, g-estimation and instrumental variable approaches can be valid and their implementation in this setting will be reviewed.
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