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

By N. Keiding, D. Clayton. 2015

58 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4d23fdc56627598f2038e279c0384805549044ea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. P. Klepac
2. T. Russell
3. S. Clifford
4. C. Jarvis
5. J. Munday
6. Z. Kevin
7. B. Quilty
8. A. Gimma

Citations in the corpus

• Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020 (2020, methodology): “Correction was performed using Formula (1) and the hospitalisation-to-death distribution in [9].”