Bibliographic record
Abstract
BACKGROUND: Regulatory agencies now recognize single-arm trials with external historical controls, particularly common in oncology, to assess promising treatments for rare or specific indications. When a new treatment indication depends on events over time, such as treatment failures, this design can introduce time-related biases in comparisons with external controls. METHODS: We describe two potential biases resulting from calendar time and choice of time zero. We illustrate these biases using simulated data, emulating those from a single-arm trial of the effectiveness of blinatumomab in treating relapsed or refractory acute lymphoblastic leukemia on the outcome of mortality. RESULTS: The trial compared 189 patients treated with blinatumomab with 1112 external historical control patients. First, calendar time was not concurrent, with the blinatumomab arm diagnosed during 2010-2014 and the control cohort during 1990-2013. The median survival under blinatumomab was 6.1 months compared with 3.3 months in the control arm, though for the latter it increased from 2.4 to 4.2 months over the 24-year period. Second, using the latest line of salvage treatment as cohort, entry for the control cohort introduces selection bias. The corresponding hazard ratio of death with blinatumomab compared with control was 0.56 (95% CI = 0.47, 0.67) but became 0.98 (95% CI = 0.83, 1.15) after redefining cohort entry by the matched line of salvage treatment rather than the latest line. CONCLUSION: While single-arm trials with external historical controls are gaining recognition, a proper understanding of time-related sources of bias is essential if such trials will be used to provide valid evidence for drug approval from regulatory agencies.
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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 | 0.167 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".