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Control for confounders in observational studies was generally handledthrough stratification and standardization until the 1960s. Standardizationtypically reweights the stratum-specific rates so that exposure categoriesbecome comparable. With the development first of loglinear models, soon also ofnonlinear regression techniques (logistic regression, failure time regression)that the emerging computers could handle, regression modelling became thepreferred approach, just as was already the case with multiple regressionanalysis for continuous outcomes. Since the mid 1990s it has becomeincreasingly obvious that weighting methods are still often useful, sometimeseven necessary. On this background we aim at describing the emergence of themodelling approach and the refinement of the weighting approach for confoundercontrol.

Standardization and Control for Confounding in Observational Studies: A Historical Perspective