Prediction intervals for overdispersed multinomial data with application to historical controls

TL;DR

This paper develops and compares statistical methods for constructing prediction intervals for overdispersed multinomial data, facilitating the validation of historical controls in toxicology and pharmaceutical research.

Contribution

It introduces and evaluates new frequentist and Bayesian approaches for overdispersed multinomial prediction intervals, addressing a gap in existing methodologies.

Findings

Bootstrap methods outperform asymptotic approaches in error control.

Rank-based methods provide reliable simultaneous coverage.

Standard methods tend to produce liberal intervals for small samples.

Abstract

In pharmaceutical and toxicological research, historical control data are increasingly used to validate concurrent control groups, typically via the construction of historical control limits. While methods have been described for continuous and dichotomous endpoints, approaches for overdispersed multinomial data, common in developmental and reproductive toxicology or histopathology, are currently lacking. This article introduces and compares methods for constructing simultaneous prediction intervals for future multinomial observations subject to overdispersion. We investigate a range of frequentist approaches, including asymptotic approximations and bootstrap techniques (incorporating symmetric, asymmetric, and marginal calibration, as well as rank-based methods), alongside Bayesian hierarchical models. Extensive simulation studies assessing simultaneous coverage probability and the balance of lower and upper tail error probabilities show that standard asymptotic methods and simple Bonferroni adjustments yield liberal intervals, especially for small sample sizes or rare event categories. In contrast, bootstrap methods, specifically the Marginal Calibration and Rank-Based Simultaneous Confidence Sets, provide reliable error control and equal tail probabilities across diverse scenarios involving varying cluster sizes and degrees of overdispersion. These methods fill an important gap for multinomial endpoints and support the validation of concurrent controls using historical control data, in line with the recent European Food Safety Authority scientific opinion on the use and reporting of historical control data.