Combining Longitudinal Data From Different Cohorts to Examine the Life-Course Trajectory. Issue 12 (2nd July 2021)

• Record Type: Journal Article
• Authors: Hughes, Rachael A; Tilling, Kate; Lawlor, Deborah A
• Is Part Of: American journal of epidemiology. Volume 190:Issue 12(2021)
• Page Start: 2680
• Page End: 2689
• Publication Date: 2021-07-02
• Subjects: Avon Longitudinal Study of Parents and Children -- Barry Caerphilly Growth Study -- Born in Bradford Study -- Christ's Hospital School Study -- life course -- mixed-effects models -- Promotion of Breastfeeding Intervention Trial -- repeated measures; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwab190
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Longitudinal data are necessary to reveal changes within an individual as he or she ages. However, rarely will a single cohort study capture data throughout a person's entire life span. Here we describe in detail the steps needed to develop life-course trajectories from cohort studies that cover different and overlapping periods of life. Such independent studies are probably from heterogenous populations, which raises several challenges, including: 1) data harmonization (deriving new harmonized variables from differently measured variables by identifying common elements across all studies); 2) systematically missing data (variables not measured are missing for all participants in a cohort); and 3) model selection with differing age ranges and measurement schedules. We illustrate how to overcome these challenges using an example which examines the associations of parental education, sex, and race/ethnicity with children's weight trajectories. Data were obtained from 5 prospective cohort studies (carried out in Belarus and 4 regions of the United Kingdom) spanning data collected from birth to early adulthood during differing calendar periods (1936–1964, 1972–1979, 1990–2012, 1996–2016, and 2007–2015). Key strengths of our approach include modeling of 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.