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Related Experiment Video

Updated: May 14, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

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

Oliver Dukes¹, Zach Shahn², Audrey Renson³

¹Department of Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.

International Journal of Epidemiology

|May 13, 2025

Summary

This summary is machine-generated.

This study compares change-score and baseline adjustment analyses for repeated outcome measurements. Recommendations are provided based on understanding confounding mechanisms for robust causal inference.

Keywords:

baseline adjustment causal inference change scores difference-in-differences

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Area of Science:

Biostatistics
Epidemiology
Causal Inference

Background:

Repeated outcome measurements are common in statistical analyses.
Change-score and baseline adjustment are popular analytical approaches.
Debate exists on optimal methods for incorporating repeated measures.

Purpose of the Study:

To compare change-score and baseline adjustment analyses.
To frame this comparison using causal inference principles.
To provide practical guidance for statistical analysis of repeated measures.

Main Methods:

Comparison of change-score analysis and baseline adjustment.
Utilizing causal inference literature, including difference-in-differences.
Analysis based on assumptions about confounding mechanisms.

Main Results:

Change-score analysis is linked to difference-in-differences methods.
Baseline adjustment offers an alternative perspective.
The choice between methods depends on confounding assumptions.

Conclusions:

Understanding confounding mechanisms is crucial for selecting appropriate statistical analysis.
Causal inference provides a framework for evaluating methods for repeated measures.
Practical recommendations are offered for epidemiologists and social scientists.