Title | A global sensitivity test for evaluating statistical hypotheses with nonidentifiable models. |
Publication Type | Journal Article |
Year of Publication | 2010 |
Authors | Todem, D, J Fine, and L Peng |
Journal | Biometrics |
Volume | 66 |
Issue | 2 |
Pagination | 558-66 |
Date Published | 2010 Jun |
ISSN | 1541-0420 |
Keywords | Humans, Longitudinal Studies, Methods, Models, Psychological, Models, Statistical, Psychiatry, Publication Bias, Sensitivity and Specificity |
Abstract | We consider the problem of evaluating a statistical hypothesis when some model characteristics are nonidentifiable 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 nonignorable. One possible approach to this problem is to fix a minimal set of sensitivity parameters conditional upon which hypothesized parameters are identifiable. Here, we extend this idea and show how to evaluate the hypothesis of interest using an infimum statistic over the whole support of the sensitivity parameter. We 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, we 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. |
DOI | 10.1111/j.1541-0420.2009.01290.x |
Alternate Journal | Biometrics |
Original Publication | A global sensitivity test for evaluating statistical hypotheses with nonidentifiable models. |
PubMed ID | 19645705 |
PubMed Central ID | PMC3076640 |
Grant List | K01 CA131259 / CA / NCI NIH HHS / United States P01 CA142538-01 / CA / NCI NIH HHS / United States R01 CA094893 / CA / NCI NIH HHS / United States R01 CA094893-07 / CA / NCI NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States 1K01 CA131259 / CA / NCI NIH HHS / United States K01 CA131259-04 / CA / NCI NIH HHS / United States |
A global sensitivity test for evaluating statistical hypotheses with nonidentifiable models.
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