Development and validation of an index score to adjust for healthy user bias in observational studies
Bibliographic record
Abstract
BACKGROUND: Objectives: To develop a healthy user index to serve as a method of confounding adjustment in future observational studies of preventive therapies. METHODS: A large administrative database of patients with type 2 diabetes was split in half randomly, yielding derivation and validation cohorts. Influenza vaccination was used as a 'prototypical marker' of a healthy user. In our derivation cohort, we fitted a mixed effects logistic regression model, and a points-based system was used to construct the index. The healthy user index was then evaluated in the validation cohort. RESULTS: Overall, 13% had received the influenza vaccination. In the derivation cohort (n= 914 732), the healthy user index ranged from 0 to 91 with a mean of 41.6 (SD 12.9). When applied to the validation cohort (n= 913 231), the index ranged from 0 to 96 (mean 41.6, SD 12.9) and significantly predicted influenza vaccination with a c-statistic of 0.605, suggesting moderate discrimination ability. CONCLUSION: Our healthy user index combined age, sex and healthy behaviors to predict healthy users within administrative datasets. This index score may allow for better adjustment of healthy user bias in health services research; however, external validation is further required. Key Words: Administrative Data Uses, Bias, Biostatistical Methods, Observational Data.
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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 | 0.151 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".