Prediction intervals for overdispersed Poisson data and their application in medical and pre-clinical quality control
arxiv(2024)
摘要
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, e.g. 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 prediction intervals for overdispersed count data to be used as HCL.
Variable baseline quantities are accounted for by offsets. Furthermore, we
provide a bootstrap calibration algorithm that accounts for the skewed
distribution and achieves equal tail probabilities.
Comprehensive Monte-Carlo simulations assessing the coverage probabilities of
eight different methods for HCL calculation reveal, that the bootstrap
calibrated prediction intervals control the type-1-error best. Heuristics
traditionally used in control charts (e.g. the limits in Sheward c- or u-charts
or the mean plus minus 2 SD) fail to control a pre-specified coverage
probability.
The application of HCL is demonstrated based on data from the Ames assay and
for numbers of relapses of multiple sclerosis patients. The proposed prediction
intervals and the algorithm for bootstrap calibration are publicly available
via the R package predint.
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