Substantial improvements in survival have been seen in multiple myeloma (MM) over recent years, associated with the introduction and widespread use of multiple novel agents and regimens, as well as the emerging treatment paradigm of continuous or long-term therapy. However, these therapies and approaches may have limitations in the community setting, associated with toxicity burden, patient burden, and other factors including cost. Consequently, despite improvements in efficacy in the rigorously controlled clinical trials setting, the same results are not always achieved in real-world practice. Furthermore, the large number of different treatment options and regimens under investigation in various MM settings precludes the feasibility of obtaining head-to-head clinical trial data, and there is a temptation to use cross-trial comparisons to evaluate data across regimens. However, multiple aspects, including patient-related, disease-related, and treatment-related factors, can influence clinical trial outcomes and lead to differences between studies that may confound direct comparisons between data. In this review, we explore the various factors requiring attention when evaluating clinical trial data across available agents/regimens, as well as other considerations that may impact the translation of these findings into everyday MM management. We also investigate discrepancies between clinical trial efficacy and real-world effectiveness through a literature review of non-clinical trial data in relapsed/refractory MM on novel agent−based regimens and evaluate these data in the context of phase 3 trial results for recently approved and commonly used regimens. We thereby demonstrate the complexity of interpreting data across clinical studies in MM, as well as between clinical studies and routine-care analyses, with the aim to help clinicians consider all the necessary issues when tailoring individual patients’ treatment approaches.
Objectives The objective of this analysis was to estimate costs for lung cancer care and evaluate trends in the share of costs that are the responsibility of Medicare beneficiaries. Methods SEER-Medicare data from 1991–2003 on n=60,231 lung cancer patients were used to estimate monthly and patient-liability costs for clinical phases of lung cancer care (pre-diagnosis, staging, initial, continuing, and terminal), stratified by treatment, stage, and non- vs. small cell lung cancer. Lung cancer-attributable costs were estimated by subtracting each patient's own pre-diagnosis costs. Costs were estimated as the sum of Medicare reimbursements (payments from Medicare to the service provider), co-insurance reimbursements, and patient-liability costs (deductibles and `co-pays' that are the patient's responsibility). Costs and patient-liability costs were fit with regression models to compare trends by calendar year, adjusting for age at diagnosis. Results For a 72-year old diagnosed with lung cancer in 2000, monthly costs in the first 6 months of care ranged from $2,687 (no active treatment) to $9,360 (chemo-radiotherapy), and varied by stage at diagnosis and histologic type. Patient-liability costs represented up to 21.6% of care costs and increased over the period 1992–2003 for most stage and treatment categories, even when care costs decreased or remained unchanged. The greatest monthly patient liability was incurred by chemo-radiotherapy patients ranging across stages from $1,617 to $2,004 per month. Conclusions Costs for lung cancer care are substantial and Medicare beneficiaries are responsible for an increasing share of the cost.
Despite randomized clinical trials (RCTs) of novel agents showing prolonged survival for patients with relapsed/refractory multiple myeloma (RRMM), we have found that most patients with RRMM receiving routine care will not be eligible for these trials. Furthermore, survival has been significantly worse for patients with RRMM not meeting the eligibility criteria, resulting in a gap between RCT efficacy and real-world effectiveness. Background: Concern has been increasing in oncology regarding randomized clinical trial (RCT) eligibility limiting the generalizability of the findings to real-world populations. Using a large US electronic health record database, we investigated the real-world generalizability of the findings from recent RCTs for relapsed and/or refractory multiple myeloma (RRMM). Patients and Methods: Patients with RRMM initiating second-to fourth-line therapy with the control arm of the following RCTs were retrospectively identified and categorized as "RCT eligible" or "RCT ineligible" according to the eligibility criteria: (1) Rd (lenalidomide, dexamethasone)-ASPIRE, TOURMALINE-MM1, POLLUX, and ELOQUENT-2; and (2) Vd (bortezomib, dexamethasone)-CASTOR and ENDEAVOR. Predictors of RCT ineligibility and overall survival were analyzed using logistic regression and Cox regression analysis. Results: Variations in the individual trial ineligibility rates were noted, with up to 72.3% (range, 47.9%-72.3%) of patients not meeting the eligibility criteria for 1 of the 6 hallmark RCTs (n ¼ 788 for Rd; n ¼ 477 for Vd). Other malignancies, cardiovascular disease, acute infection, and renal dysfunction were the common reasons for ineligibility. Advanced age, Charlson comorbidity score of ! 2, later therapy lines (3-4), and refractory status to the previous line were independently predictive of RCT ineligibility. RCT-ineligible versus RCT-eligible patients had a significantly greater mortality risk (hazard ratio, Rd, 1.46; Vd, 1.51). Conclusion: Most real-world patients with RRMM were ineligible for the hallmark RCTs. The eligibility rates varied across the RCTs, underlining the flawed nature of cross-study comparisons without RCT validation. Overall survival was significantly affected by the inability to meet the criteria, highlighting the limited generalizability of the RCT results. Greater efforts are required to broaden the eligibility criteria to reflect real-world clinical characteristics and narrow the gap between RCT efficacy and the observed effectiveness in real-world patients with RRMM.
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