Abstract P4-15-01: Fragility index of trials supporting approval of breast cancer drugs
Bibliographic record
Abstract
Abstract Background: Decisions on regulatory approval and reimbursement of drugs are based typically on the observation of statistically significant results showing superiority over an established standard. The Fragility Index (FI) quantifies the reliability of statistically significant results by estimating the number of events which would change statistically significant results to non-significant results. Here, we calculate the FI of trials supporting approval of breast cancer drugs. Methods: We searched Drugs@FDA to identify randomized controlled trials (RCT) supporting breast cancer drug approvals by the US Food and Drug Administration (FDA) between January 2010 and December 2018. We adapted the FI framework (Walsh et al. J Clin Epidemiol 2014) to allow input comprising of time to event data. First, we reconstructed survival tables from reported data using the Parmar Toolkit (Parmar et al. Stat Med 1998). Then, the FI was calculated as the number of events for each arm which would result in a non-significant effect for the primary endpoint of each trial. The FI was then compared quantitatively to the number of patients in each respective trial who withdrew consent or were lost to follow-up. Results: We identified 15 RCT with a median of 724 patients (range, 302-4084) and 318 events (range, 210-635). The median FI was 18 (range, 4 to 33 - see Table). The FI was 10 or fewer patients in 3 trials (20%) and 20 or fewer in 11 trials (73%). Among the 13 RCTs (87%) reporting data, the median number of patients who withdrew consent and were lost to follow up was 16 (range, 2-103). The number of patients who withdrew consent or were lost to follow-up was greater than the FI in 7 trials (54%). There was no association between trial sample size or reported P-value and the FI. Conclusion: Statistical significance of trials supporting breast cancer drug approval rely often on a small number of events. In over one half of trials the FI was lower than the number of patients withdrawing consent or being lost to follow-up. Post-approval randomized trials or real-world data analyses should be performed to ensure that effects observed in registration trials are robust. Main resultsFDA-Approved DrugYear of approvalPhase 3 TrialPrimary EndpointWithdrew consent and lost to follow-upFragility IndexAbemaciclib2018MONARCH 3PFS415Abemaciclib2017MONARCH 2PFS1020Eribulin2010EMBRACEPFS238Eribulin2010EMBRACEOS2316Everolimus2012BOLERO-2PFS3818Neratinib2017ExteNETIDFS827Olaparib2018OlympiADPFS1610Palbociclib2017PALOMA-2PFS1918Palbociclib2016PALOMA-3PFS718Pertuzumab2017APHINITYIDFS1034Pertuzumab2012CLEOPATRAPFS4125Ribociclib2018MONALEESA-7PFS227Ribociclib2018MONALEESA-3PFSn/a19Ribociclib2017MONALEESA-2PFSn/a22T-DM12013EMILIAOS333T-DM12013EMILIAPFS326Talazoparib2018EMBRACAPFS7013 Citation Format: Alexandra Desnoyers, Michelle B. Nadler, Ramy Saleh, Eitan Amir. Fragility index of trials supporting approval of breast cancer drugs [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-15-01.
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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 | 0.161 | 0.543 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".