Work

Change scores and baseline adjustment: splitting the difference (in differences)

Year: 2025

Type: article

Abstract: A lively debate exists on how best to incorporate repeated measurements of an outcome in a statistical analysis. Two popular approaches are change-score analyses (the difference in baseline and follow-up outcome measurements are regressed on the exposure) and baseline adjustment (in which baseline outcomes are included as a covariate in a regression model). In this article, we compare both through the lens of the causal inference literature. We draw in particular on the connections between change-score analyses and 'difference-in-differences'-a popular approach for inferring causal effects in economics and social sciences, which has received less attention in epidemiology. We make practical recommendations for which approach to use, based on one's knowledge or belief about the confounding mechanism.

Source: International Journal of Epidemiology

DOI: 10.1093/ije/dyaf042

Authors: Oliver Dukes, Zach Shahn, Audrey Renson

Institutions: Ghent University, City University of New York, New York University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: Fonds Wetenschappelijk Onderzoek