Autoregressive modelling for the analysis of longitudinal data with unequally spaced examinations

Abstract

Missing and/or unequally spaced examinations are often present in longitudinal studies. An autoregressive model is presented for the analysis of such data for continuous outcome variables. The fitting of the model can be accomplished by weighted non-linear regression methods available in standard statistical packages. Some features of the model include consideration of both time-dependent and fixed covariates, assessment of the relationships between changes in outcome and exposure over short periods of time, and use of all available person-time for an individual. An illustration looking at the role of personal cigarette smoking on changes in pulmonary function in children is included.