Global perspectives on clinical cancer research: A comparison of randomized controlled trial (RCT) design and outcomes across high income and low-middle income countries.
Bibliographic record
Abstract
7021 Background: Cancer clinical trials have become increasingly international in scope. There are limited data regarding trial variation based on the economic status of the country in which they are conducted. Here we describe trial characteristics, design, and results of all RCTs published globally during 2014-2017. Methods: A structured literature search was designed using PUBMED to identify all RCTs evaluating anti-cancer therapies published during 2014-2017. Data captured included authorship, participants, study characteristics, design, and results. RCTs were classified based on the World Bank country-level economic classification of the first author [low-middle/upper-middle income countries (LMIC) and high-income countries (HIC)]. Among superiority RCTs that met the primary endpoint (i.e. statistically “positive”), we calculated the ESMO-MCBS to identify trials with substantial clinical benefit (MCBS scores 4/5 or A/B). Outcomes were compared with Chi Square or Fisher’s Exact tests. Results: The study cohort included 694 RCTs; 636 (92%) were led by HIC and 58 (8%) were led by LMIC. Compared to LMIC, RCTs in HICs were more likely to be funded by industry [73% vs 41%, p<0.001] and more likely to test novel systemic therapies [87% vs 78%, p=0.027]. LMIC studies were typically smaller (median N=220 vs N=474 participants, p<0.001) and more likely to meet their primary endpoints [66% vs 44%, p=0.002]. In “positive” superiority trials, the effect size was larger in LMICs compared to HICs (median HR 0.62 vs HR 0.84, p<0.001). The proportion of trials identifying treatments with substantial clinical benefit (ESMO MCBS 4/5/A/B) was 45% (LMIC) and 31% (HIC, p=0.291). Studies from LMIC were published in journals with lower impact factors (IF) (median IF 7 vs 21, p<0.001); a publication bias persisted when adjusted for whether a trial was positive or negative: median IF LMIC negative trial=5 vs HIC negative trial=18 (p<0.001); median IF LMIC positive trial=9 vs HIC positive trial= 26 (p<0.001). Conclusions: Only a small minority of oncology RCTs are led by investigators in LMIC; these trials are less likely to be funded by industry and more likely to meet their primary endpoint. “Positive” RCTs from LMIC identify therapies with a substantially larger effect size than HIC. These data identify a substantial publication bias against RCTs conducted in LMIC.
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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 | 0.501 | 0.642 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".