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Prediction Intervals for Overdispersed Binomial Endpoints and Their Application to Toxicological Historical Control
Max Menssen1, Jonathan Rathjens2
1Department of Biostatistics, Leibniz University Hannover, Hannover, Germany.
Pharmaceutical Statistics
|September 14, 2025
Summary
New prediction intervals improve toxicology study validation using historical control data. Frequentist methods best control type-1 errors, offering a reliable alternative to traditional heuristics for analyzing dichotomous outcomes.
Area of Science:
- Toxicology
- Biostatistics
- Statistical modeling
Background:
- Validation of concurrent control groups using historical control data (HCD) is essential in toxicology studies.
- Historical control limits (HCL) are commonly used for validation, but often fail to account for overdispersion and skewness in dichotomous HCD.
- Existing heuristic methods for HCL may not adequately control type-1 errors in practical applications.
Purpose of the Study:
- To propose and evaluate novel prediction intervals for validating concurrent control groups with dichotomous historical control data.
- To compare the performance of proposed frequentist and Bayesian prediction intervals against traditional heuristic HCL methods.
- To assess the type-1 error control and coverage probabilities of different statistical approaches.
Main Methods:
- Development of four prediction intervals: two frequentist and two Bayesian.
- Comprehensive Monte Carlo simulations to compare coverage probabilities and type-1 error rates.
- Application of proposed methods to real-world historical control data from carcinogenicity studies.
Main Results:
- Frequentist bootstrap-calibrated prediction intervals demonstrated superior control of the type-1 error.
- Bayesian prediction intervals derived from generalized linear mixed models were found to be practically applicable.
- Traditional heuristic methods (historical range, np-chart limits, mean ± 2 SD) consistently failed to control the type-1 error.
Conclusions:
- Frequentist prediction intervals offer a robust and reliable approach for validating concurrent controls in toxicology studies with dichotomous HCD.
- Bayesian methods provide a viable alternative, particularly when dealing with complex data structures.
- The study highlights the inadequacy of current heuristic HCL methods and recommends the adoption of validated prediction intervals for improved study reliability.
Keywords:
Bayesian hierarchical modelingOECD test guidelineShewhart control chartbootstrap‐calibrationlong‐term carcinogenicity studiesmicro‐nucleus‐testMore Related Videos
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