Addressing bias due to measurement error of an outcome with unknown sensitivity in database epidemiologic studies. A contribution from the ConcePTION project
Bibliographic record
Abstract
In epidemiologic database studies, the occurrence of an event is measured with error through an indicator whose specificity is often maximized, at the expense of sensitivity. However, if the indicator has low sensitivity, measures of occurrence are underestimated. In association studies, risk difference is biased, and risk ratio may be biased as well, in either direction, if the sensitivity is differential across exposure groups. In this work, we show that if an auxiliary screening indicator can be defined to complement the main indicator, estimates of the positive predictive value of both indicators provide tools to estimate the sensitivity of the primary indicator or a lower bound thereof. This mitigates bias in estimating the number of cases, prevalence, cumulative incidence, rate (particularly when the event is rare), and, in association studies, risk ratio and risk difference. They also allow testing for nondifferential sensitivity. Although direct estimation of sensitivity is often infeasible, this novel methodology improves evidence based on data obtained from reuse of existing databases, which may prove critical for regulatory and public health decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.373 | 0.727 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".