Identifying direct risk factors in UK Biobank with simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink

Big data approaches to discovering non-genetic risk factors have lagged behind genome-wide association studies that routinely uncover novel genetic risk factors for diverse diseases. Instead, epidemiology typically focuses on candidate risk factors. Since modern biobanks contain thousands of potential risk factors, candidate approaches may introduce bias, inadequately control for multiple testing, and miss important signals. Bayesian model averaging offers a solution, but classical statistics predominates, perhaps because of concern that the prior unduly influences results. Here we show that simultaneous Bayesian and frequentist discovery of direct risk factors is possible via a model-averaged hypothesis testing approach for large samples called ‘Doublethink’. Doublethink produces interchangeable posterior odds and p -values that control the false discovery rate (FDR) and familywise error rate (FWER). We implement the Doublethink approach in R and apply it to discover direct risk factors for COVID-19 hospitalization in 2020 among 1,912 variables in UK Biobank. We find nine exposome-wide significant variables at 9% FDR and 0.05% FWER. These include several commonly reported risk factors (e.g. age, sex, obesity) but exclude others (e.g. diabetes, cardiovascular disease, hypertension) which might be mediated through variables measuring general comorbidity (e.g. numbers of medications). We identify significant direct effects among infrequently reported risk factors (psychiatric disorders, infection, dementia and aging), and show how testing groups of correlated variables is a useful alternative to pre-analysis variable selection. We discuss the potential for impact and limitations of joint Bayesian-frequentist inference, and the mutual insights afforded into the long-standing differences on statistical approaches to scientific discovery. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by the Robertson Foundation, the Wellcome Trust and the Royal Society (grant no. 101237/Z/13/B). This study was supported by the National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (NIHR200915), a partnership between the UK Health Security Agency (UKHSA) and the University of Oxford. The views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA or the Department of Health and Social Care. The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This research has been conducted using the UK Biobank Resource under Application Number 53100. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes To access UK Biobank data, researchers must register and submit a research application (https://www.ukbiobank.ac.uk/register-apply). Registration is open to all bona fide researchers for all types of health-related research that is in the public interest. The registration and application process ensures researchers and projects meet UK Biobank's obligations to its participants and funders.