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Updated: Jun 25, 2026
06:52
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
Model choice can obscure results in longitudinal studies.
Christopher H Morrell1, Larry J Brant, Luigi Ferrucci
1Gerontology Research Center, National Institute on Aging, 5600 Nathan Shock Drive, Baltimore, MD 21224, USA.
Summary
Accurately analyzing longitudinal data requires distinguishing between age at study entry (first age) and follow-up duration (time). Models incorporating both first age and time terms generally yield better results for observational studies.
Area of Science:
- Biostatistics
- Epidemiology
- Longitudinal Data Analysis
Background:
- Modeling observational longitudinal data presents challenges in parameterizing age and time.
- Accurate analysis requires careful consideration of how age and time influence results.
Purpose of the Study:
- To investigate the impact of different age and time parameterizations on modeling longitudinal data.
- To determine the necessity of distinguishing between age at entry and follow-up time.
Main Methods:
- Fitted various statistical models to longitudinal study data.
- Compared models that treated age as a single variable versus models that decomposed age into 'first age' and 'time' components.
Main Results:
- Decomposing age into 'first age' and 'time' components yielded different conclusions compared to using age alone.
- Models incorporating both 'first age' and 'time' demonstrated superior performance.
Conclusions:
- Models utilizing both 'first age' and 'time' terms are generally superior for longitudinal data analysis.
- These distinct terms are typically essential for the correct analysis of longitudinal observational studies.
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