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08:04
Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
Published on: December 4, 2013
Evaluating the effect of change on change: a different viewpoint
1Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA. shahar@email.arizona.edu
Journal of Evaluation in Clinical Practice
|February 26, 2009
Summary
Change models in cohort studies do not estimate a unique "longitudinal effect." Their primary benefit is controlling for time-stable confounders, an advantage often lost in random effects models.
Area of Science:
- Epidemiology
- Biostatistics
- Observational Studies
Background:
- Researchers often prefer change models over cross-sectional analyses for two-time point cohort data.
- This preference stems from the belief that change models offer a stronger epistemological basis by estimating 'change on change'.
Purpose of the Study:
- To analyze two common regression models of change.
- To compare their cross-sectional origins and implications for confounders and causal inference.
- To present statistical viewpoints on parameterization and interpretation.
Main Methods:
- Tracing regression models of change to their cross-sectional roots.
- Examining models through the lens of time-stable confounders, effect modification, and causal diagrams.
- Reviewing statistical literature viewpoints.
Main Results:
- Change models do not estimate conceptually different effects compared to cross-sectional models.
- The superiority of change models lies in their self-matched design, enabling control of time-stable confounders.
- Statistical interpretations and parameterizations vary significantly.
Conclusions:
- Models of change between two time points do not estimate a distinct 'longitudinal effect'.
- The key advantage of 'change on change' regression is the control of time-stable confounders in observational studies.
- Fitting random effects models often negates this crucial advantage.
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