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Observational studies of drug effects conducted using health care mega-databases often involve large cohorts with multiple time-varying exposures and covariates. These present formidable technical challenges in data analysis, necessitating sampling approaches such as nested case–control designs. The nested case–control approach is, however, baffling to medical journal readers, particularly the comparisons involving “cases” versus “controls” and the convoluted way in which forward-looking relations from exposure to outcome are extracted from backward-looking data. I propose a “quasi-cohort” approach involving alternative ways of data presentation and analysis that are more in line with the underlying cohort design, including the computation of quasi-rates, rate ratios, and quasi-rate differences. I illustrate the quasi-cohort approach using data from a study of pneumonia risk associated with inhaled corticosteroid use in a cohort of 163,514 patients with chronic obstructive pulmonary disease, including 20,344 who had the outcome event of pneumonia hospitalization during more than 304 million person-days of follow-up.