Abstract

DINIZ, CARLOS; PIRES, RUBIANE; PARAIBA, CAROLINA  and  FERREIRA, PAULO. Influence Diagnostics for Correlated Binomial Regression Models: An Application to a Data Set on High-Cost Health Services Occurrence. Rev.Colomb.Estad. [online]. 2021, vol.44, n.2, pp.253-278.  Epub Aug 27, 2021. ISSN 0120-1751.  https://doi.org/10.15446/rce.v44n2.85606.

This paper considers a frequentist perspective to deal with the class of correlated binomial regression models (Pires & Diniz, 2012), thus providing a new approach to analyze correlated binary response variables. Model parameters are estimated by direct maximization of the log-likelihood function. We also consider a diagnostic analysis under the correlated binomial regression model setup, which is performed considering residuals based on predictive values and deviance residuals (Cook & Weisberg, 1982) to check for model assumptions, and global influence measure based on case-deletion (Cook, 1977) to detect influential observations. Moreover, a sensitivity analysis is carried out to detect possible influential observations that could affect the inferential results. This is done using local influence metrics (Cook, 1986) with case-weight, response, and covariate perturbation schemes. A simulation study is conducted to assess the frequentist properties of model parameter estimates and check the performance of the considered diagnostic metrics under the correlated binomial regression model. A data set on high-cost claims made to a private health care provider in Brazil is analyzed to illustrate the proposed methodology.

Keywords : Generalized binomial distribution; Health care provider; Influence; Overdispersion; Regression; Residuals.

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