Bayesian pooling versus sequential integration of small preclinical trials: a comparison within linear and nonlinear modeling frameworks
| Autor: | Helena Geys, Tom Jacobs, Fabiola La Gamba, Christel Faes, Jan Serroyen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pharmacology
Statistics and Probability Process (engineering) business.industry Computer science Computation Bayesian probability Pooling Linear model Bayes Theorem Small sample Machine learning computer.software_genre Nonlinear system Nonlinear Dynamics Sample Size Linear Models Humans Computer Simulation Pharmacology (medical) Artificial intelligence business computer |
| Zdroj: | Journal of Biopharmaceutical Statistics. 31:25-36 |
| ISSN: | 1520-5711 1054-3406 |
| DOI: | 10.1080/10543406.2020.1776312 |
| Popis: | Bayesian sequential integration is an appealing approach in drug development, as it allows to recursively update posterior distributions as soon as new data become available, thus considerably reducing the computation time. However, preclinical trials are often characterized by small sample sizes, which may affect the estimation process during the first integration steps, particularly when complex PK-PD models are used. In this case, sequential integration would not be practicable, and trials should be pooled together. This work is aimed at comparing simple Bayesian pooling with sequential integration through a simulation study. The two techniques are compared under several scenarios using linear as well as nonlinear models. The results of our simulation study encourage the use of Bayesian sequential integration with linear models. However, in the case of nonlinear models several caveats arise. This paper outlines some important recommendations and precautions in that respect. |
| Databáze: | OpenAIRE |
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