Impact of unequal testing on vaccine effectiveness estimates across two study designs: a simulation study
Bibliographic record
Abstract
Observational studies are essential for measuring vaccine effectiveness. Recent research has raised concerns about how a relationship between testing and vaccination may affect estimates of vaccine effectiveness against symptomatic infection (symptomatic VE). Using an agent-based network model and SARS-CoV-2 as an example, we investigated how differences in the likelihood of testing by vaccination could influence estimates of symptomatic VE across two common study designs: retrospective cohort and test-negative designs. First, we measured the influence of unequal testing on symptomatic VE estimates across study designs and sampling periods. Next, we investigated whether the magnitude of bias in VE estimates from unequal testing was shaped by immune escape (vaccine efficacy against susceptibility and against infectiousness) and underlying epidemic potential (probability of transmission). We found that unequal testing led to larger bias in the cohort design than the test-negative design and that bias was largest with lower efficacy against susceptibility. We also found the magnitude of bias was moderated by the study’s selected sampling period, efficacy against infectiousness, and probability of transmission, with these moderating effects more pronounced in the test-negative design. Our study illustrates that VE estimates across study designs require careful interpretation, especially in the presence of epidemic and immunological heterogeneity. Estimates of effectiveness of SARS-CoV-2 vaccines against symptomatic infection from observational studies may be biased if testing rates differ according to vaccination status. Here, the authors use simulations to quantify the impact of this testing bias in retrospective cohort and test-negative study designs.
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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 | 0.200 | 0.348 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".