Today we release the first full draft of the Doublethink methods paper. This is an evolution of what was originally conceived as the supplement to the Doublethink COVID-19 paper. The wider significance of the results persuaded us to separate the two, which now focus on:
- Doublethink methods paper: Broad connections between Bayesian and classical hypothesis testing that we hope bring the best of both world by enabling scientists to simultaneously control the Bayesian false discovery rate and the classical familywise error rate, in big data settings.
- Doublethink COVID-19 paper: Identifying direct risk factors for COVID-19 hospitalization among 2000 candidate variables in 200,000 UK Biobank participants. Compares results to the literature and considers the limitations imposed by mediation and complex 'exposome-wide' association studies.
After soliciting colleagues for comments and another round of editing, we will move toward submission in later this year.
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