American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



SENSITIVITY TESTING FOR NONIDENTIFIABLE MODELS,
WITH APPLICATION TO LONGITUDINAL DATA
WITH NONINFORMATIVE DROPOUT

by

Jason Fine
Department of Biostatistics
University of North Carolina, Chapel Hill


Abstract

I consider the problem of evaluating a statistical hypothesis when some model characteristics are non-identifiable from observed data. Such a scenario is common in meta-analysis for assessing publication bias and in longitudinal studies for evaluating a covariate effect when dropouts are likely to be informative. One possible approach to this problem is to fix a minimal set of sensitivity parameters conditional upon which hypothesized parameters are identifiable. I discuss existing approaches to inference derived by assessing the sensitivity of parameter estimates to the sensitivity parameter. I propose to formally evaluate the hypothesis of interest using an infimum statistic over the whole support of the sensitivity parameter, along with the associated inferential challenges. I characterize the limiting distribution of the statistic as a process in the sensitivity parameter, which involves a careful theoretical analysis of its behavior under model misspecification. In practice, I suggest a nonparametric bootstrap procedure to implement this infimum test as well as to construct confidence bands for simultaneous pointwise tests across all values of the sensitivity parameter, adjusting for multiple testing. The methodology's practical utility is illustrated in an analysis of a longitudinal psychiatric study.


Date: Friday, March 12, 2010
Time: 3:30 - 4:30 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Conference Room
6th Floor - Room R627
New York, New York

RESERVATIONS ARE NOT REQUIRED


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