Comparison of the Efficacy in Clinical Trials Versus Effectiveness in the Real-World of Treatments for Multiple Myeloma: A Population-Based Cohort Study
Bibliographic record
Abstract
Introduction: Improvements in outcomes of patients with multiple myeloma (MM) depend on the use of regimens approved based on results from large phase III randomized controlled trials (RCTs) demonstrating their efficacy. However, many real-world (RW) patients would not have met the stringent RCTs inclusion criteria. Therefore, the effectiveness of these drugs in the RW setting is unknown. Understanding this efficacy-effectiveness gap is important to contextualize the expected outcomes in the current RW setting. Therefore, we conducted a retrospective population-based study to compare the efficacy versus effectiveness of registration RCTs with RW patients using standard of care (SoC) MM regimens for the primary outcomes of 1) progression free survival [PFS]; 2) overall survival [OS] and 3) serious adverse events (AEs). Methods: RW data was obtained from the Institute for Clinical Evaluative Sciences, an administrative database capturing all health records in the publicly funded health care system in Ontario, Canada. Adult patients treated between Jan 2007 to Dec 2020 with SoC regimens were included. Only regimens with corresponding registrational phase III RCTs which led to the public reimbursement in Ontario were included (lenalidomide/dex [Rd] and bortezomib/Rd [VRd] for newly diagnosed transplant ineligible patients, and relapsed MM (RRMM) regimens included carfilzomib/Rd [KRd], carfilzomib/dex [Kd], daratumumab/Rd [DRd], daratumumab/bortezomib/dex [DVd], pomalidomide/dex [Pd]). In the RW cohort, PFS was defined as the time from initiation of index regimen to death, initiation of subsequent MM treatment, or last follow-up, and patients remaining on the index regimen as last follow up were censored. Kaplan-Meier curves from pivotal phase 3 RCTs were manually digitized to provide individual patient-level estimates of PFS and OS. Meta-analyses were performed to compare the gap of PFS and OS outcomes of RW versus RCT patients, and effect estimates were summarized using hazard ratios (HR). The frequency of serious AE data was abstracted from published RCTs. Given that serious AEs in RCTs would have resulted in hospitalization, hospital admission during treatment with the index regimen was used as a surrogate for serious AEs in the RW cohort. Differences in RW and RCT safety outcomes were reported descriptively. Results & Discussion: A total of 3951 RW MM patients, treated with 7 standard of care MM regimens, were included. Baseline characteristics of patients in the RW and RCT cohorts are shown in table 1. Overall, patients in the RW cohort were older than in the RCTs. For relapsed regimens, there was a longer time between MM diagnosis and start of the regimen in the real-world versus RCT. With regards to the efficacy-effectiveness gap, MM patients treated in routine practise in the RW had a worse PFS despite overestimated of RW PFS compared to highly selected RCT patients for 6 of the 7 MM regimens evaluated, with a pooled HR of 1.44 (95% CI 1.34-1.54) in the meta-analysis (Figure 1A). Similarly, RW patients had a worse OS compared to RCT patients treated with 6 of the 7 regimens, with a pooled HR of 1.75 (95% ci 1.63-1.88) in the meta-analysis (Figure 1B). RRMM patients in the RW had higher rates of prior lenalidomide exposure compared to RCT patients. The only regimen which showed a trend towards performing better in the RW as compared to RCTs was Pd. The reason for this is likely multifactorial but perhaps patients included in the MM-003 RCT may have had more refractory MM (given the higher prior immunomodulatory drug exposure and longer time from diagnosis to treatment among Pd RCT patient) compared to RW patients in this study. With regards to safety, the percentage of patients with inpatient hospitalization during treatment in the real-world cohort and reported serious AEs in RCT were comparable (VRD 57% vs not reported [NR]; Rd 64% vs NR; Kd 57% vs 59%; KRd 53% vs 60%; DVd 36% vs NR; DRd 46% vs 49%; Pd 59% vs 61%). Conclusion: This is one the largest population-level studies highlighting the significant efficacy-effectiveness gap between registrational RCTs and RW usage of these regimens, with RW patients experiencing 44% worse PFS and 75% worse OS compared to RCT patients. Our data emphasize the importance of ongoing evaluation of RW data to contextualize effectiveness and toxicity of selected regimens in the clinic, and better inform both clinicians and patients for shared treatment decision making.
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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 | 0.083 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".