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.