Assessing influence for pharmaceutical data in zero-inflated generalized Poisson mixed models.
For clustered count data with excess zeros where the observations are either over-dispersed or under-dispersed, the zero-inflated generalized Poisson mixed (ZIGPM) regression model may be appropriate, in which the baseline discrete distribution is a generalized Poisson distribution, which is a natural extension of standard Poisson distribution. Motivated by one data set drawn from a pharmaceutical study, influence diagnostics for ZIGPM models based on case-deletion and local influence analysis are developed in this work. The one-step approximations of the estimates under case-deletion model and some case-deletion measures are given. Meanwhile, local influence measures are obtained under various perturbations of the observed data or model assumptions. Results from a pharmaceutical study illustrate the usefulness of the diagnostic statistics.
Xie
Feng-Chang
FC
Department of Applied Mathematics, Nanjing Agricultural University, Nanjing 210095, China. fcxie@njau.edu.cn
Wei
Bo-Cheng
BC
Lin
Jin-Guan
JG
D003627:Data Interpretation, Statistical; D004358:Drug Therapy / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions; D016012:Poisson Distribution; D012044:Regression Analysis