Randomised controlled trials evaluating anticancer therapies in haematological cancers: an overview of global research activity
Bibliographic record
Abstract
Background: Design, results, and interpretation of oncology randomised controlled trials (RCTs) have changed substantially over the past decade.In this study, we describe all RCTs evaluating anticancer therapies in haematological cancers published globally during 2014-2017 with comparisons with solid tumours RCTs. Methods:A PubMed literature search identified all phase 3 RCTs of anticancer therapy for haematological cancers and solid tumours published globally during 2014-2017.Descriptive statistics, chi-square tests and the Kruskal-Wallis test were used to compare RCT design results, and output between haematological cancers and solid tumours as well as for different haematological cancer subtypes.Results: 694 RCTs were identified; 124 in haematological cancers and 570 in solid tumours.Overall survival (OS) was the primary endpoint in only 12% (15/124) of haematological cancer trials compared to 35% (200/570) in solid tumours (p < 0.001).Haematological cancer RCTs evaluated the systemic novel therapy more often than the solid tumour RCT (98% versus 84%, p = 0.002).Use of surrogate endpoints like progression-free survival (PFS) and time to treatment failure (TTF) were more common in haematological cancers than solid tumours (47% versus 31%, p < 0.001).Within haematological cancers, the use of PFS and TTF was more prevalent in chronic lymphocytic leukaemia and multiple myeloma as compared to others (80%-81% versus 0%-41%, p < 0.001).Seventy-eight percent of haematologic trials were funded by industry as compared to 70% of solid tumour trials.Only 4% (5/124) of haematologicalcancer trials were led by investigators in upper-middle and lower-middle-income countries as compared to the 9% of solid tumour trials. Conclusion:The fact that only 12% of haematological cancer RCTs are designed to show improvements in OS is of grave concern for the field and the care of future patients.This is further compounded by the highly prevalent use of alternative primary endpoints that are rarely valid surrogates for OS in haematological cancers.
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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 | 0.190 | 0.336 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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; 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".