Informing randomized clinical trials of respiratory syncytial virus vaccination during pregnancy to prevent recurrent childhood wheezing: A sample size analysis
Bibliographic record
Abstract
BACKGROUND: Early RSV illness is associated with wheeze-associated disorders in childhood. Candidate respiratory syncytial virus (RSV) vaccines may prevent acute RSV illness in infants. We investigated the feasibility of maternal RSV vaccine trials to demonstrate reductions in recurrent childhood wheezing in general paediatric populations. METHODS: We calculated vaccine trial effect sizes that depended on vaccine efficacy, allocation ratio, rate of early severe RSV illness, risk of recurrent wheezing at age 3, and increased risk of RSV infection on recurrent wheezing. Model inputs came from systematic reviews and meta-analyses. For each combination of inputs, we estimated the sample size required to detect the effect of vaccination on recurrent wheezing. RESULTS: There were 81 scenarios with 1:1 allocation ratio. Risk ratios between vaccination and recurrent wheezing ranged from 0.9 to 1.0 for 70% of the scenarios. Among the 57 more plausible scenarios, the lowest sample size required to detect significant reductions in recurrent wheezing was 6196 mother-infant pairs per trial arm; however, 75% and 47% of plausible scenarios required >31,060 and >100,000 mother-infant pairs per trial arm, respectively. Studies with asthma endpoints at age 5 will likely need to be larger. DISCUSSION: Clinical efficacy trials of candidate maternal RSV vaccines undertaken for licensure are unlikely to demonstrate an effect on recurrent wheezing illness due to the large sample sizes likely needed to demonstrate a significant effect. Further efforts are needed to plan for alternative study designs to estimate the impact of maternal RSV vaccine programs on recurrent childhood wheezing in general populations.
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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 | 0.332 | 0.520 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".