Model choice can obscure results in longitudinal studies.

摘要

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.