Combining longitudinal data from different cohorts to examine the life-course trajectory
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".