Sample-constrained partial identification with application to selection bias.

ABSTRACT: Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort studies. We show that existing sensitivity analyses, which are often conservative and difficult to implement, can be formulated in our framework and made significantly more informative via auxiliary information on the population. We conduct a simulation study to eval

SUBMITTER: Tudball MJ 

PROVIDER: S-EPMC10183833 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature