Prior Effective Sample Size When Borrowing on the Treatment Effect Scale
arxiv(2024)
摘要
With the robust uptick in the applications of Bayesian external data
borrowing, eliciting a prior distribution with the proper amount of information
becomes increasingly critical. The prior effective sample size (ESS) is an
intuitive and efficient measure for this purpose. The majority of ESS
definitions have been proposed in the context of borrowing control information.
While many Bayesian models can be naturally extended to leveraging external
information on the treatment effect scale, very little attention has been
directed to computing the prior ESS in this setting. In this research, we
bridge this methodological gap by extending the popular ELIR ESS definition. We
lay out the general framework, and derive the prior ESS for various types of
endpoints and treatment effect measures. The posterior distribution and the
predictive consistency property of ESS are also examined. The methods are
implemented in R programs available on GitHub:
https://github.com/squallteo/TrtEffESS.
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