A global sensitivity test for evaluating statistical hypotheses with nonidentifiable models.

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 mod

SUBMITTER: Todem D 

PROVIDER: S-EPMC3076640 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature