COVID-19 pandemic surges can induce bias in trials using response adaptive randomization: A simulation study
Bibliographic record
Abstract
Abstract Response-adaptive randomization is being used in COVID-19 trials, but it is unknown whether outcome rate changes during surges of COVID-19 will lead to bias in trial results. In response-adaptive randomization, allocation ratios are adjusted according to interim analyses to assign more patients to promising interventions. Although it is known that response-adaptive randomization may give biased estimates if outcome rates drift over time, observed mortality fluctuations in the COVID-19 pandemic are more extreme than any previously tested in simulation. We hypothesized that pandemic surges induce bias in trials using response-adaptive randomization, and that adjustment for time will alleviate that bias. Bayesian 4-arm superiority trials with a mortality outcome were simulated to investigate bias in treatment effect, comparing complete and response-adaptive randomization under different pandemic scenarios based on data from New York, Spain, and Italy. Relative bias in the odds ratio associated with treatment ranged from 0.3% to 11% and was largest in trials with a surge and an effective intervention that did not adjust for time. Bias was attenuated by adjustment for time without compromising the false-positive rate. Trials using response-adaptive randomization were more likely to identify effective interventions but were slower to drop ineffective interventions. Even with variation in outcome rates similar to observed pandemic surges, COVID-19 trials using response-adaptive randomization that adjust for time can provide accurate estimates of treatment effects.
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 | 0.189 | 0.355 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".