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As of this month, Journal of Biology initiates a 're-review opt-out scheme' whereby once authors have revised their paper in response to peer review it is their choice whether the reviewers see it again. The experiment was inspired by the widespread frustration with current peer review practices and is strongly supported by a majority of the Editorial Board of the journal.
A statistical model is proposed for the analysis of peer-review ratings of R01 grant applications submitted to the National Institutes of Health. Innovations of this model include parameters that reflect differences in reviewer scoring patterns, a mechanism to account for the transfer of information from an application's preliminary ratings and group discussion to final ratings provided by all panel members and posterior estimates of the uncertainty associated with proposal ratings. Application of this model to recent R01 rating data suggests that statistical adjustments to panel rating data would lead to a 25% change in the pool of funded proposals. Viewed more broadly, the methodology proposed in this article provides a general framework for the analysis of data collected interactively from expert panels through the use of the Delphi method and related procedures.