  Control for confounders in observational studies was generally handled
through stratification and standardization until the 1960s. Standardization
typically reweights the stratum-specific rates so that exposure categories
become comparable. With the development first of loglinear models, soon also of
nonlinear regression techniques (logistic regression, failure time regression)
that the emerging computers could handle, regression modelling became the
preferred approach, just as was already the case with multiple regression
analysis for continuous outcomes. Since the mid 1990s it has become
increasingly obvious that weighting methods are still often useful, sometimes
even necessary. On this background we aim at describing the emergence of the
modelling approach and the refinement of the weighting approach for confounder
control.
