Open Access Journal of Biostatistics and Biometrics
An Alternative Perspective on Consensus Priors with Applications to Phase I Clinical Trials
*Steven B. Kim Department Of Biostatistics, University Of California, Monterey Bay, United States
*Corresponding Author: Steven B. Kim
Department Of Biostatistics, University Of California, Monterey Bay, United States Email:email@example.com
Published on: 2018-09-03
We occasionally need to make a decision or a series of decisions based on a small sample. In some cases, an investigator is knowledgeable about a parameter of interest in some degrees or is accessible to various sources of prior information. Yet, two or more experts cannot have an identical prior distribution for the parameter. In this manuscript, we discuss the use of a consensus prior and compare two classes of Bayes estimators. In the first class of Bayes estimators, the contribution of each prior opinion is determined by observing data. In the second class, the contribution of each prior opinion is determined after observing data. Bayesian designs for Phase I clinical trials allocate trial participants at new experimental doses based on accumulated information, while the typical sample sizes are fairly small. Using simulations, we illustrate the usefulness of a combined estimate in the early phase clinical trials.
In small-sample studies, statisticians often rely on parametric assumptions to gain efficiency for estimation and testing. In a Bayesian framework, prior assumptions are also incorporated into the model. Statistical inference regarding parameters of scientific interest is based on the resulting posterior distribution which is obtained by updating the prior assumption with observed data. While large sample sizes are preferred for statistical precision, there are many situations when we need to make a decision or a series of decisions based on a small amount of empirical evidence together with a prior. In this case, an investigator’s prior opinion can be highly influential, yet it is still essential.