The Bias of Parameters in Inverse-Intensity Weighted GEEs when Excluding Subjects with no Follow-up Visits
Bibliographic record
Abstract
Longitudinal data can be used to study disease progression and often features irregular visit times. Traditional methods such as generalized estimating equations (GEEs) and mixed effect models lead to biased estimates when visit and outcome processes are related. Inverse-intensity weighed GEEs (IIW-GEEs) account for dependency between the visit and outcome processes. A common issue is that subjects with no visits are excluded from dataset in practice. We aim to examine the bias of regression parameters in IIW-GEEs when excluding subjects without a visit. We show analytically that there is bias when subjects with no visits are excluded and verify this in simulation study. Moreover, we show that decreasing visit frequency, decreasing maximum follow-up time, increasing proportion of subjects with no visits lead to increase in bias on omitting subjects with no visits. We recommend everyone should be included in the dataset when analyzing, regardless of whether there is follow-up visit.
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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 | 0.062 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".