This prompts me to re-introduce some of these, to highlight their considerable flexibility and ease of use, and to update them. Surprisingly-or possibly not-they overlooked classical estimators developed in earlier decades. An epidemiologist-statistician pair recently proposed a new estimator that is easily applied to data from individually-matched series with a 2:1 ratio (and no other confounding variables) using just a hand calculator or spreadsheet. To many, however, conditional logistic regression, commonly used to estimate the incidence density ratio parameter, is somewhat of a black box whose output is not easily checked. Now, with Big Data, individual matching becomes an economical option. This design was costly, and the data sometimes mis-analyzed. With greater access to regression-based methods for confounder control, the etiologic study with individual matching, analyzed by classical (calculator) methods, lost favor in recent decades.
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