Propensity score-based diagnostics for categorical response regression models.

ABSTRACT: For binary or categorical response models, most goodness-of-fit statistics are based on the notion of partitioning the subjects into groups or regions and comparing the observed and predicted responses in these regions by a suitable chi-squared distribution. Existing strategies create this partition based on the predicted response probabilities, or propensity scores, from the fitted model. In this paper, we follow a retrospective approach, borrowing the notion of balancing scores used in causal inference to inspect the conditional distribution of the predictors, given the propensity scores, in each category of the response to assess model adequacy. We can use this diagnostic under both prospective and retrospective sampling designs, and it may ascertain general forms of misspecification. W

SUBMITTER: Boonstra PS 

PROVIDER: S-EPMC3911784 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature