Integrating Real-World Evidence (RWE) in Oncology Drug Regulation: A Comparative Analysis of CDSCO and Health Canada
Bibliographic record
Abstract
ABSTRACT Cancer remains a major global health challenge, necessitating continuous innovation in oncology therapeutics. While randomized controlled trials (RCTs) are the traditional gold standard for evidence generation, they often lack applicability to diverse real-world populations. This has fuelled growing interest in incorporating Real-World Evidence (RWE), derived from Real-World Data (RWD), into regulatory decision-making processes, especially in oncology. This study aims to compare how RWE is integrated into oncology drug regulation by two key regulatory bodies, Health Canada and India’s Central Drugs Standard Control Organization (CDSCO), in order to identify lessons and opportunities that can support India's evolving regulatory framework. A qualitative comparative analysis was conducted using regulatory documents, guidance frameworks, and real-world case examples. Canada’s CanREValue initiative was explored in depth, alongside an assessment of India’s emerging digital health infrastructure and policy efforts. Health Canada has established a mature RWE framework supported by multi-stakeholder collaborations and pilot projects, facilitating dynamic regulatory decisions in oncology. In contrast, CDSCO is in the early stages of RWE adoption, with limited formal guidance. However, initiatives like the Ayushman Bharat Digital Mission (ABDM) and national health registries offer promising pathways. Canada’s regulatory progress offers valuable insights for India. Strengthening digital infrastructure, developing national RWE frameworks, and fostering collaboration could transform India’s oncology regulatory ecosystem. This study highlights the potential of RWE to improve evidence-driven decision-making and enhance access to cancer therapies. Keywords: Real-World Evidence, Oncology, Drug Regulation, CDSCO, Health Canada, CanREValue
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".