To investigate seasonality and association of increased enterovirus and influenza activity in the community with ventricular fibrillation (VF) risk during first ST-elevation myocardial infarction (STEMI). MethodsThis study comprised all consecutive patients with first STEMI (n = 4,659; aged 18-80 years) admitted to the invasive catheterization laboratory between 2010-2016, at Copenhagen University Hospital, Rigshospitalet, covering eastern Denmark (2.6 million inhabitants, 45% of the Danish population). Hospital admission, prescription, and vital status data were assessed using Danish nationwide registries. We utilized monthly/weekly surveillance data for enterovirus and influenza from the Danish National Microbiology Database (2010)(2011)(2012)(2013)(2014)(2015)(2016) that receives copies of laboratory tests from all Danish departments of clinical microbiology. ResultsOf the 4,659 consecutively enrolled STEMI patients, 581 (12%) had VF before primary percutaneous coronary intervention. In a subset (n = 807), we found that VF patients

We introduce causal inference reasoning to crossover trials, with a focus on thorough QT (TQT) studies. For such trials, we propose different sets of assumptions and consider their impact on the modeling strategy and estimation procedure. We show that unbiased estimates of a causal treatment effect are obtained by a g‐computation approach in combination with weighted least squares predictions from a working regression model. Only a few natural requirements on the working regression and weighting matrix are needed for the result to hold. It follows that a large class of Gaussian linear mixed working models lead to unbiased estimates of a causal treatment effect, even if they do not capture the true data‐generating mechanism. We compare a range of working regression models in a simulation study where data are simulated from a complex data‐generating mechanism with input parameters estimated on a real TQT data set. In this setting, we find that for all practical purposes working models adjusting for baseline QTc measurements have comparable performance. Specifically, this is observed for working models that are by default too simplistic to capture the true data‐generating mechanism. Crossover trials and particularly TQT studies can be analyzed efficiently using simple working regression models without biasing the estimates for the causal parameters of interest.