Bayesians in clinical trials: asleep at the switch.
The refreshing Bayes perspective has much to offer biostatistics. Yet, from its 225-year-old roots sprung difficulties that blocked its growth at the beginning of the 20th century. Computational obstacles in concert with an inability to identify the best indifferent prior revealed a weakness on which frequentists capitalized. It took Bayesians 40 years to recover, allowing the infant field of biostatistics to fall firmly in the hands of the frequentists. Recent disillusionment with the frequentist perspective, and its hegemony of p-values, has produced a second opportunity for the Bayesian philosophy to make solid contributions to clinical trials.However, difficulty with the applicability of the likelihood principle, problems with prevalent prior 'disinformation' in clinical medicine, in concert with the complexity of truly representative loss functions threaten again to thwart the Bayesian march into biostatistics. Seven suggestions are offered to the Bayesians to help them adapt to the rigors of clinical research.
Moyé
Lemuel A
LA
School of Public Health, University of Texas, 1200 Herman Pressler - E815 Houston, TX 77025, USA. Lemuel.A.Moye@uth.tmc.edu
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D015203:Reproducibility of Results