English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Testing the equality of two proportions is a common procedure in science,especially in medicine and public health. In these domains it is crucial to beable to quantify evidence for the absence of a treatment effect. Bayesianhypothesis testing by means of the Bayes factor provides one avenue to do so,requiring the specification of prior distributions for parameters. The mostpopular analysis approach views the comparison of proportions from acontingency table perspective, assigning prior distributions directly to thetwo proportions. Another, less popular approach views the problem from alogistic regression perspective, assigning prior distributions tologit-transformed parameters. Reanalyzing 39 null results from the New EnglandJournal of Medicine with both approaches, we find that they can lead tomarkedly different conclusions, especially when the observed proportions are atthe extremes (i.e., very low or very high). We explain these stark differencesand provide recommendations for researchers interested in testing the equalityof two proportions and users of Bayes factors more generally. The test thatassigns prior distributions to logit-transformed parameters creates priordependence between the two proportions and yields weaker evidence when theobservations are at the extremes. When comparing two proportions, we argue thatthis test should become the new default.

A Puzzle of Proportions: Two Popular Bayesian Tests Can Yield Dramatically Different Conclusions