Comparing diagnostic tests: trials in people with discordant test results
Bibliographic record
Abstract
Diagnostic tests are traditionally compared for accuracy against a gold standard but can also be compared prospectively in a trial. A conventional trial comparing two tests would randomize each participant to a testing strategy, but a more efficient alternative is to give both tests to all participants and follow up those with discordant results. Participants could be randomized before or after testing. The statistical analysis of such a trial has not previously been described. We investigated two estimates of the risk difference for a binary outcome: one based on analysing outcomes as if from a conventional trial and one combining estimates of different parameters in the manner of a decision analysis. We show that the trial estimate and decision analysis estimate are both unbiased and derive approximate formulae for their standard errors. By using the decision analysis estimate (but not the trial estimate), the same precision can be achieved by randomizing before testing as by randomizing after. To avoid destroying equipoise, and to allow consenting and randomizing to be carried out at the same visit, we recommend randomizing before testing. Giving both tests to all participants means fewer need to be recruited: in one example from the literature, the proposed design was nearly four times more efficient in this sense than a conventional trial design.
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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 | 0.313 | 0.690 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".