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Where are our false positives?
(OSF Preprints, 2022-05-03) Perezgonzalez J
In our current regime of COVID-19 testing, a question seems not to be asked: Are we inferring the best we can from our results? Or, put differently, are we testing with severity? This study thus explore the proportion of expected positives and negative cases, with an especial focus on estimating false positives in isolation and estimating false (or unknown) negatives in the remaining population. Both seems to have been chiefly ignored by Government health policy.
Rapid Antigen Tests (RATs) and COVID-19 prevalence
(OSF Preprins, 2022-05-04) Perezgonzalez J
The article contains a Bayesian analysis to model expected rate of positive and negative COVID-19 cases, based on Rapid Antigen Test performance and COVID-19 prevalence in New Zealand. The results suggest that the majority of approved tests were excellent in identifying negative cases but might turn out too many false positives. Recommendations for a protocol for RAT-based testing concludes the article.
Frequentist-Bayesian analyses in parallel using JASP - A tutorial
(PsyArXiv, 2022-07-21) Perezgonzalez J
A tutorial to demonstrate the use of parallel Frequentist-Bayesian analyses using JASP, and the plausible inferences one may be able to make from such combined analysis.