This module calculates the Positive Predictive Value (PPV) and False Discovery Rate (FDR) for research findings based on the framework described by Ioannidis (2005). It helps researchers understand the probability that their claimed findings are actually true given various study characteristics.
Details
The calculation is based on Bayes' theorem and considers:
Prior probability of true relationships (percentage of a priori true hypotheses)
Type I error rate (alpha level)
Statistical power (1 - beta)
Proportion of p-hacked or biased studies
PPV = (Power × R + u × β × R) / (R + α - β × R + u - u × α + u × β × R) where R is the pre-study odds of true relationships (percTrue/(100-percTrue)) and u is the bias factor (percHack/100)
References
Ioannidis, J. P. (2005). Why most published research findings are false. PLoS medicine, 2(8), e124.
Adapted from https://github.com/raviselker/ppv
Super classes
jmvcore::Analysis -> ClinicoPath::ppvBase -> ppvClass
Methods
Inherited methods
jmvcore::Analysis$.createImage()jmvcore::Analysis$.createImages()jmvcore::Analysis$.createPlotObject()jmvcore::Analysis$.load()jmvcore::Analysis$.render()jmvcore::Analysis$.save()jmvcore::Analysis$.savePart()jmvcore::Analysis$.setCheckpoint()jmvcore::Analysis$.setParent()jmvcore::Analysis$.setReadDatasetHeaderSource()jmvcore::Analysis$.setReadDatasetSource()jmvcore::Analysis$.setResourcesPathSource()jmvcore::Analysis$.setStatePathSource()jmvcore::Analysis$addAddon()jmvcore::Analysis$asProtoBuf()jmvcore::Analysis$check()jmvcore::Analysis$init()jmvcore::Analysis$optionsChangedHandler()jmvcore::Analysis$postInit()jmvcore::Analysis$print()jmvcore::Analysis$run()jmvcore::Analysis$serialize()jmvcore::Analysis$setError()jmvcore::Analysis$setStatus()jmvcore::Analysis$translate()ClinicoPath::ppvBase$initialize()