Abstract P3-17-01: Geographic variation in adverse event reporting patterns in breast cancer clinical trials
Bibliographic record
Abstract
Abstract Introduction: Adverse event (AE) reporting in clinical trials (CT) informs the safety of investigational products. Once approved, safety information in the monograph and prescribing information mainly derive from CT data. Some studies have shown geographic variations in the AE reporting patterns in multinational CT; none of them assessed this variation in cancer CT. We conducted a study to analyze the geographic AE reporting patterns in two breast cancer (BC) CT conducted by Translational Research in Oncology (TRIO). Objective: To perform a quantitative and qualitative comparison of non-serious AE (NSAE) and serious AE (SAE) reporting patterns between several geographic regions, in breast cancer CT conducted by TRIO. Methodology: We retrospectively analyzed aggregated NSAE/SAE data (as reported by investigators) from all patients randomized in two completed phase 3, multinational CT of anticancer therapies in advanced BC. Participating countries were grouped in 7 regions according to their geographic location (East Asia, Eastern Europe, Latin America and Caribbean, Middle East and Africa, Non-Eastern Europe, North America, Oceania). Regions were kept masked and numbered from 1 thru 7. AE data were extracted from the clinical data bases. For each region we calculated the mean number of NSAE and SAE per patient (pt), the mean number of NSAE and SAE per cycle/per pt, and the percentage (%) of pt experiencing selected AE (fatigue, febrile neutropenia and emesis). Comparisons between regions were done using unequal variance t-test and Fisher´s exact test. Results: 1,863 patients from 35 countries and 310 sites were included. Mean number of pt per region was 331. We found significant variation in the number of NSAE/SAE reported across several regions. Two regions (1 and 6) reported the highest mean number of AE while region 4 the lowest rates. The mean number of NSAE reported in region 4 is approximately 3-fold lower than regions 1 and 6 (mean NSAE 22.8 [region 1] vs. 7.9 [region 4]; p <.0001; mean NSAE cycle/pt 9.7 [region 1] vs. 3.2 [region 4]; p <.0001). Region 4 reported 8-fold lower rates of SAE than region 1 (mean number SAE 0.1 vs. 0.8, p<.0001) (Table 1). % of pt experiencing AE fatigue, febrile neutropenia and nausea/vomiting also varied significantly across regions, especially between regions 1 and 4 (Table 2). NSAE and SAE reporting in selected regions (regions 1 and 4) Region 1 (mean)Region 4 (mean)p-valueNSAE per pt22.87.9<.0001SAE per pt0.80.1<.0001NSAE per cycle/per pt9.73.2<.0001SAE per cycle/per pt0.10.03<.0001 % of pt experiencing selected AE in selected regions (regions 1 and 4) Region 1Region 4p-valueFatigue84.1 %8.5 %<.0001Febrile Neutropenia5.5 %2.1 %0.02Nausea and Vomiting61.0 %18.8 %<.0001 Conclusion: NSAE and SAE reporting patterns vary markedly by geographic region and one region appears to systematically under report both NSAE and SAE. These data warrant confirmation, and if confirmed, may provide an important caveat on the interpretation of reported study safety data. Citation Format: González V, Machado A, Fung H, Spera G, Meyer C, Millán P, Mackey JR, Fresco R. Geographic variation in adverse event reporting patterns in breast cancer clinical trials [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-17-01.
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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 | 0.073 | 0.217 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".