The test-negative design: validity, accuracy and precision of vaccine efficacy estimates compared to the gold standard of randomised placebo-controlled clinical trials
Bibliographic record
Abstract
The test-negative design (TND) is an efficient form of case-control study commonly applied to influenza vaccine effectiveness (VE) estimation. TND validity is predicated on the core assumption that the intervention (vaccine) has no effect on other non-targeted aetiologies resulting in similar illness/disease. Here we verify this core assumption and compare efficacy estimates derived by the TND versus classical per-protocol analysis of four datasets obtained from randomised placebo-controlled clinical trials (RCT) of the live attenuated influenza vaccine (LAIV) in children ≤7 years-old and the elderly ≥60 years-old. We further assess generalisability of the TND approach in two other RCT datasets to evaluate monoclonal antibody in the prevention of respiratory syncytial virus (RSV) hospitalisation. Efficacy estimates and their confidence intervals were virtually identical for per-protocol RCT versus TND analyses of LAIV and also for RSV monoclonal antibody. Neither LAIV nor monoclonal antibodies affected the risk of disease aetiologies that were not specifically targeted by the respective interventions (e.g. other respiratory viruses). This study validates the core assumption of the TND approach for influenza vaccine efficacy estimation and confirms the accuracy and precision of its estimates compared to the gold standard of classic per-protocol RCT analysis of the same data sets. The TND approach is generalisable for other conditions such as RSV for which the core assumption is also met. However, when used in observational studies, the TND, like all designs, still requires assessment for bias and confounding that may exist in the absence of randomised participation and blinded follow-up.
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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 | 0.628 | 0.814 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| 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; 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".