The Quasi-cohort approach in pharmacoepidemiology: upgrading the nested case-control

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.