Group-based trajectory modeling under non-random attrition: A sensitivity analysis and application to frailty trajectories
Bibliographic record
Abstract
Group-based trajectory modeling (GBTM) is used to identify trajectories but suffers from attrition bias when dropout is nonrandom. We evaluate an extended GBTM that models attrition as a latent-class process. Monte Carlo simulations compare the extended model with conventional GBTM across trajectory separation and missing-data mechanisms, and test a parsimonious version with a constant dropout rate within classes. conventional GBTM is adequate for separated trajectories but biased when they overlap and attrition is nonrandom. The extended GBTM remains unbiased, as shown in Manitoba frailty data. Implications: modeling attrition improves robustness; the parsimonious extension remains reliable under complex dropout. • Compared extended GBTM with conventional GBTM under simulated non-random attrition. • Conventional GBTM bias emerged when trajectory classes overlapped and attrition was non-random; robust only under well-separated trajectories. • Parsimonious extended GBTM (constant dropout rate within classes) consistently produced unbiased estimates of class proportions and trajectory shapes across all scenarios. • Empirical application to frailty data reclassified 11% of the sample into a high-risk worsening group, illustrating the practical impact of accounting for attrition.
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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 | 0.052 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".