Comparing the clinical trial efficacy <i>versus</i> real-world effectiveness of treatments for multiple myeloma: a population-based study
Bibliographic record
Abstract
Comparing the clinical trial efficacy versus real-world effectiveness of treatments for multiple myeloma: a population-based studyPhase III randomized control trials (RCT) are the "gold standard" used to obtain marketing and regulatory approval for novel multiple myeloma (MM) treatments, inform patients about treatment outcomes, and inform treatment guidelines.Yet, numerous indirect real-world (RW) and RCT comparisons have shown that RW patient tend to have inferior outcomes compared to RCT patients.However, to date, no study has directly quantified the differences in outcomes between RW and RCT patients with MM treated with standard of care (SoC) therapies.Understanding and quantifying the difference in efficacy, the outcome in an "ideal" RCT setting, and effectiveness, the outcome in the "real life" clinical practice setting, is needed to contextualize the generalizability of RCT data to the general population.To fill this knowledge gap, we conducted a population-based cohort study to compare and quantify the difference in the RW effectiveness versus RCT efficacy of SoC MM regimens with respect to the progression-free survival (PFS) and overall survival (OS).The RCT cohort was identified from registrational phase III RCT which led to the public reimbursement of SoC regimens in Ontario between January 1, 2013 to December 31, 2021.Regimens included lenalidomide/dexamethasone (Rd) bortezomib/Rd (VRd) in patients with transplant-ineligible newly diagnosed MM patients (TIE-NDMM).Relapsed refractory MM (RRMM) regimens included carfilzomib/Rd (KRd), carfilzomib/dexamethasone (Kd), daratumumab/Rd (DRd), daratumumab/bortezomib/dexamethasone (DVd), and pomalidomide/dexamethasone (Pd).The most recent published Kaplan-Meier PFS 1-7 and OS 1,2,[7][8][9][10][11] curves were manually digitized using the WebPlotDigitizer software (version 4.6), then reconstructed using an established algorithm 12 to provide individual patient-level estimates of PFS and OS for the experimental arm in the RCT cohorts.RW data was obtained using from Ontario's ICES administrative database.Ontario has a universal, publicly funded healthcare system which provides access to chemotherapy, and the provincial administrative database captures virtually all health care encounters and has a loss to follow-up rate of 0.25%.Treatment data was accessed through the Ontario Drug Benefit database for regimens containing only oral medications and the Cancer Activity Level Reporting database for treatment regimens containing injected or infused medications.Patients diagnosed with MM between January 1, 2013 to December 31, 2020 and initiating treatment with SoC regimens either at diagnosis or relapse were included in this study.Provincial reimbursement criteria
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".