ABSTRACT
While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive and unprecedented initiation of clinical research, it has also led to considerable disruption of clinical trials in other disease areas, with around 80% of non-COVID-19 trials stopped or interrupted during the pandemic. In many cases the disrupted trials will not have the planned statistical power necessary to yield interpretable results. This paper describes methods to compensate for the information loss arising from trial disruptions by incorporating additional information available from auxiliary data sources. The methods described include the use of auxiliary data on baseline and early outcome data available from the trial itself and frequentist and Bayesian approaches for the incorporation of information from external data sources. The methods are illustrated by application to the analysis of artificial data based on the Primary care pediatrics Learning Activity Nutrition (PLAN) study, a clinical trial assessing a diet and exercise intervention for overweight children, that was affected by the COVID-19 pandemic. We show how all of the methods proposed lead to an increase in precision relative to use of complete case data only. [ FROM AUTHOR] Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
BACKGROUND: Age has been implicated as the main risk factor for COVID-19-related mortality. Our objective was to utilize administrative data to build an explanatory model accounting for geriatrics-focused indicators to predict mortality in hospitalized older adults with COVID-19. METHODS: Retrospective cohort study of adults age 65 and older (N = 4783) hospitalized with COVID-19 in the greater New York metropolitan area between 3/1/20-4/20/20. Data included patient demographics and clinical presentation. Stepwise logistic regression with Akaike Information Criterion minimization was used. RESULTS: The average age was 77.4 (SD = 8.4), 55.9% were male, 20.3% were African American, and 15.0% were Hispanic. In multivariable analysis, male sex (adjusted odds ration (adjOR) = 1.06, 95% CI:1.03-1.09); Asian race (adjOR = 1.08, CI:1.03-1.13); history of chronic kidney disease (adjOR = 1.05, CI:1.01-1.09) and interstitial lung disease (adjOR = 1.35, CI:1.28-1.42); low or normal body mass index (adjOR:1.03, CI:1.00-1.07); higher comorbidity index (adjOR = 1.01, CI:1.01-1.02); admission from a facility (adjOR = 1.14, CI:1.09-1.20); and mechanical ventilation (adjOR = 1.52, CI:1.43-1.62) were associated with mortality. While age was not an independent predictor of mortality, increasing age (centered at 65) interacted with hypertension (adjOR = 1.02, CI:0.98-1.07, reducing by a factor of 0.96 every 10 years); early Do-Not-Resuscitate (DNR, life-sustaining treatment preferences) (adjOR = 1.38, CI:1.22-1.57, reducing by a factor of 0.92 every 10 years); and severe illness on admission (at 65, adjOR = 1.47, CI:1.40-1.54, reducing by a factor of 0.96 every 10 years). CONCLUSION: Our findings highlight that residence prior to admission, early DNR, and acute illness severity are important predictors of mortality in hospitalized older adults with COVID-19. Readily available administrative geriatrics-focused indicators that go beyond age can be utilized when considering prognosis.