Analysis of duration data from longitudinal surveys subject to loss to follow-up
Bibliographic record
Abstract
Data from longitudinal surveys give rise to many statistical challenges. They often come from a vast, heterogeneous population and from a complex sampling design. Further, they are usually collected retrospectively at intermittent interviews spaced over a long period of time, which gives rise to missing information and loss to follow-up. As a result, duration data from this kind of surveys are subject to dependent censoring, which needs to be taken into account to prevent biased analysis. Methods for point and variance estimation are developed using Inverse Probability of Censoring (IPC) weights. These methods account for the random nature of the IPC weights and can be applied in the analysis of duration data in survey and non-survey settings. The IPC estimation techniques are based on parametric estimating function theory and involve the estimation of dropout models. Survival distributions without covariates are estimated via a weighted Kaplan-Meier method and regression modeling through the Cox Proportional Hazards model and other models is based on weighted estimating functions. The observational frameworks from Statistics Canada's Survey of Labour and Income Dynamics (SLID) and the UK Millenium Cohort Study are used as motivation, and durations of jobless spells from SLID are analyzed as an illustration of the methodology. Issues regarding missing information from longitudinal surveys are also discussed.
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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 | 0.086 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".