Issues in COVID-19 research and statistical analyses (Part XVVVII)

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Issues in COVID-19 research and statistical analyses (Part XVVVII) March 30, 2022

Latest research published in the Lancet shows diabetes onset was higher for those with postacute sequelae or long-haul COVID-19 however these may be preliminary results. The results came from a cohort study which used databases from the United States Department of Veterans Affairs to extract a cohort of 181,280 participants who had a positive COVID-19 test between March 1, 2020, and Sept 30, 2021, and also had survived the first 30 days of COVID-19. This was the primary exposure group. They then created a contemporary control (n=4 18,441) which included enrolled participants between March 1, 2020, and Sept 30, 2021 as well as a historical control (n=4,286,911) that enrolled participants between March 1, 2018, and Sept 30, 2019. Both control groups had no evidence of SARS-CoV-2 infection. They made sure that participants in all three comparison groups were free of diabetes before entry into the cohort and were followed up for a median of 352 days (IQR 245–406). In terms of statistical techniques, they said they used logistic regression with “predefined and algorithmically selected high dimensional variables” to obtain predicted probabilities to creates inverse probability weights which they ended up using in a Cox proportional hazards regression, so that they could estimate post-acute COVID-19 risks of incident diabetes as well as antihyperglycaemic use, and finally, a composite of the two outcomes. They then quantified two measures of risk: hazard ratio (HR) and burden per 1000 people at 12 months. They did not specify if they violated the proportional hazards assumption.


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