Prediction Intervals for Overdispersed Poisson Data and Their Application in Medical and Pre-Clinical Quality Control.

ABSTRACT: In pre-clinical and medical quality control, it is of interest to assess the stability of the process under monitoring or to validate a current observation using historical control data. Classically, this is done by the application of historical control limits (HCL) graphically displayed in control charts. In many applications, HCL are applied to count data, for example, the number of revertant colonies (Ames assay) or the number of relapses per multiple sclerosis patient. Count data may be overdispersed, can be heavily right-skewed and clusters may differ in cluster size or other baseline quantities (e.g., number of petri dishes per control group or different length of monitoring times per patient). Based on the quasi-Poisson assumption or the negative-binomial distribution, we propose pr

SUBMITTER: Menssen M 

PROVIDER: S-EPMC11889705 | biostudies-literature | 2025 Mar-Apr

REPOSITORIES: biostudies-literature