Model choice can obscure results in longitudinal studies
- PMID: 19196902
- PMCID: PMC2655028
- DOI: 10.1093/gerona/gln024
Model choice can obscure results in longitudinal studies
Abstract
Background: This article examines how different parameterizations of age and time in modeling observational longitudinal data can affect results.
Methods: When individuals of different ages at study entry are considered, it becomes necessary to distinguish between longitudinal and cross-sectional differences to overcome possible selection biases.
Results: Various models were fitted using data from longitudinal studies with participants with different ages and different follow-up lengths. Decomposing age into two components-age at entry into the study (first age) and the longitudinal follow-up (time) compared with considering age alone-leads to different conclusions.
Conclusions: In general, models using both first age and time terms performed better, and these terms are usually necessary to correctly analyze longitudinal data.
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