Combining longitudinal data from different cohorts to examine the life-course trajectory

Abstract

Longitudinal data are necessary to reveal changes within the same individual as they age. However, rarely will a single cohort capture data throughout the lifespan. We describe in detail the steps needed to develop life-course trajectories from cohorts that cover different and overlapping periods of life. Such independent studies are likely from heterogenous populations which raises several challenges including: data harmonisation (deriving new harmonised variables from differently measured variables by identifying common elements across all studies); systematically missing data (variables not measured are missing for all participants of a cohort); and model selection with differing age ranges and measurement schedules. We illustrate how to overcome these challenges using an example which examines the effects of parental education, sex, and ethnicity on weight trajectories. Data were from five prospective cohorts (Belarus and four UK regions), spanning from birth to early adulthood during differing calendar periods. Key strengths of our approach include modelling trajectories over wide age ranges, sharing of information across studies and direct comparison of the same parts of the life-course in different geographical regions and time periods. We also introduce a novel approach of imputing individual-level covariates of a multilevel model with a nonlinear growth trajectory and interactions.