Applying Bayeisan Optimisation to FACTS trial designs and choosing stopping bound

Автор: Berry Consultants

Загружено: 6 нояб. 2024 г.

Просмотров: 67 просмотров

Описание:

A look at using machine learning to optimise trial sample sizes, firstly in a trivial 2 arm case, and then in a more complex 6 arm, phase 2 case, where the objective is to have at least 50% probability of selecting the correct MED. We then look at optimizing Bayesian “Goldilocks” two arm design, by processing simulation results in R.

Applying Bayeisan Optimisation to FACTS trial designs and choosing stopping bound

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