Quantitative Bias Analysis for Single-Arm Trials With External Control Arms
Bibliographic record
Abstract
Importance: Unmeasured confounding is a key concern for decision-makers when observational datasets are used to assemble external control arms (ECAs) for single-arm trials. Objective: To investigate the utility of quantitative bias analysis (QBA) for exploring the sensitivity to unmeasured confounding of nonrandomized analyses using ECAs. Design, Setting, and Participants: This study emulated 15 treatment comparisons using experimental arms from existing randomized trials in advanced non-small cell lung cancer (aNSCLC) conducted after 2011 and ECAs derived from observational data. Participants were eligible individuals diagnosed with aNSCLC between January 1, 2011, and March 1, 2020. After adjustment for measured baseline confounders, a prespecified QBA was conducted to address potential bias by known unmeasured and mismeasured confounders. The QBA relied on a synthesis of external evidence from a targeted literature search, randomized trial data, and clinician input. Hazard ratios from the original randomized trials were compared with those from their emulation based on ECA analyses. Analyses were completed from February 2022 to October 2023. Exposure: Initiation of systemic therapies for aNSCLC. Main outcomes and measures: Hazard ratios for all-cause death. Results: Sample sizes varied from 52 to 830 depending on the treatment group. The mean difference in the log hazard ratio estimates when using the original control arm vs the ECA for each trial was 0.247 in unadjusted analyses (ratio of hazard ratios, 1.36), 0.139 when adjusted for measured confounders (ratio of hazard ratios, 1.22), and 0.098 when adding external adjustment for unmeasured and mismeasured confounders (ratio of hazard ratios, 1.17). Conclusions and Relevance: QBA was feasible and informative in ECA analyses in which residual confounding was expected to be the most important source of bias. These findings encourage further exploration of how QBA can help quantify the impact of bias in other settings and when using other data sources.
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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 | 0.769 | 0.892 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".