The Inflation Reduction Act: An Opportunity to Accelerate Confidence in Real-World Evidence in the United States.

OBJECTIVES: The Inflation Reduction Act (IRA), enacted in 2022, brings substantial reforms to the US healthcare system, particularly regarding Medicare. A key aspect includes the introduction of Medicare price negotiation. The objective of this commentary is to explore the implications of the IRA for US pharmaceutical companies, with a specific focus on the role of real-world evidence (RWE) in the context of Medicare reforms. METHODS: This commentary uses a qualitative analysis of the IRA's provisions related to healthcare and pharmaceutical regulation, focusing on how these reforms change the evidence requirements for pharmaceutical companies. It discusses the methodological aspects of generating and using RWE, including techniques such as target trial emulation and quantitative bias analysis methods to address biases inherent in RWE. RESULTS: This commentary highlights that the IRA introduces a unique approach to value assessment in the United States by evaluating drug value several years after launch, as opposed to at launch, similar to health technology assessments in other regions. It underscores the central role of RWE in comparing drug effectiveness across diverse clinical scenarios to improve the accuracy of real-world data comparisons. Furthermore, this article identifies key methodologies for managing the inherent biases in RWE, which are crucial for generating credible evidence for IRA price negotiations. CONCLUSIONS: This article underscores the importance of these methodologies in ensuring credible evidence for IRA price negotiations. It advocates for an integrated approach in evidence generation, positioning RWE as pivotal for informed pricing discussions in the US healthcare landscape.

Alone, together: On the benefits of Bayesian borrowing in a meta-analytic setting.

It is common practice to use hierarchical Bayesian model for the informing of a pediatric randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction parameter (BFP). This implicitly assumes that the BFP is intuitive and corresponds to the degree of similarity between the populations. Generalizing this model to any K ≥ 1 historical studies, naturally leads to empirical Bayes meta-analysis. In this paper we calculate the Bayesian BFPs and study the factors that drive them. We prove that simultaneous mean squared error reduction relative to an uninformed model is always achievable through application of this model. Power and sample size calculations for a future RCT, designed to be informed by multiple external RCTs, are also provided. Potential applications include inference on treatment efficacy from independent trials involving either heterogeneous patient populations or different therapies from a common class.

Efficacy of non-intensive therapies approved for relapsed/refractory acute myeloid leukemia: a systematic literature review.

Aim: De novo relapsed and/or refractory acute myeloid leukemia (rrAML) has limited treatment options for patients not eligible ('unfit') to receive intensive chemotherapy-based interventions. The authors aimed to summarize outcomes for licensed therapies in this setting. Materials & methods: A systematic literature review identified licensed therapies in this setting. A feasibility assessment was made to conduct a network meta-analysis to evaluate comparative efficacy. Results: Seven unique trials were identified. Median survival months were 13.8 for gemtuzumab ozogamicin (GO), 9.3 for gilteritinib (FLT3 mutated rrAML), 5.6 for low-dose cytarabine and 3.2 for best supportive care; transplant rates with gilteritinib and GO were 25.5 and 19%, respectively. A network meta-analysis was not feasible. Conclusion: There remains a high unmet need in de novo rrAML patients not eligible for intensive therapy, with GO and gilteritinib (only FLT3-mutated AML) providing the best current options.

Some patients with acute myeloid leukemia (AML) have no response to initial treatment or have a response that is subsequently lost. Follow-on treatment options after that initial stage are limited, especially for patients who are not able to have intensive therapy, such as chemotherapy, due to age, physical or cognitive function, existing comorbidities or symptoms. This study aimed to review the published literature to identify data associated with treatments that are licensed for use in patients ineligible for intensive therapy who do not maintain a response from their initial therapy. The study found that the drug gilteritinib was an option for the subgroup of AML patients with FLT3-mutated disease with an average life expectancy just under 1 year, while gemtuzumab ozogamicin was an option for a wider group of AML patients with a life expectancy just over 1 year. Between a fifth and a quarter of patients went on to receive a stem-cell transplant after treatment with one of these. With limited options, this patient group needs further attention; however, the availability of the previously mentioned treatments is promising.

Comparison of Parametric Survival Extrapolation Approaches Incorporating General Population Mortality for Adequate Health Technology Assessment of New Oncology Drugs.

OBJECTIVES: Survival extrapolation of trial outcomes is required for health economic evaluation. Generally, all-cause mortality (ACM) is modeled using standard parametric distributions, often without distinguishing disease-specific/excess mortality and general population background mortality (GPM). Recent National Institute for Health and Care Excellence guidance (Technical Support Document 21) recommends adding GPM hazards to disease-specific/excess mortality hazards in the log-likelihood function ("internal additive hazards"). This article compares alternative extrapolation approaches with and without GPM adjustment. METHODS: Survival extrapolations using the internal additive hazards approach (1) are compared to no GPM adjustment (2), applying GPM hazards once ACM hazards drop below GPM hazards (3), adding GPM hazards to ACM hazards (4), and proportional hazards for ACM versus GPM hazards (5). The fit, face validity, mean predicted life-years, and corresponding uncertainty measures are assessed for the active versus control arms of immature and mature (30- and 75-month follow-up) multiple myeloma data and mature (64-month follow-up) breast cancer data. RESULTS: The 5 approaches yielded considerably different outcomes. Incremental mean predicted life-years vary most in the immature multiple myeloma data set. The lognormal distribution (best statistical fit for approaches 1-4) produces survival increments of 3.5 (95% credible interval: 1.4-5.3), 8.5 (3.1-13.0), 3.5 (1.3-5.4), 2.9 (1.1-4.5), and 1.6 (0.4-2.8) years for approaches 1 to 5, respectively. Approach 1 had the highest face validity for all data sets. Uncertainty over parametric distributions was comparable for GPM-adjusted approaches 1, 3, and 4, and much larger for approach 2. CONCLUSION: This study highlights the importance of GPM adjustment, and particularly of incorporating GPM hazards in the log-likelihood function of standard parametric distributions.

Comparing cure rates for gemtuzumab ozogamicin plus standard chemotherapy vs standard chemotherapy alone in acute myeloid leukemia patients.

Aim: Assess the suitability of standard parametric, piecewise and mixture cure models (MCMs) for modeling long-term survival of acute myeloid leukemia patients achieving remission following treatment with gemtuzumab ozogamicin (GO) + standard chemotherapy (SC) or SC alone. MCMs can model survival data comprising of statistically cured (patients in long-term remission) and uncured patients. Materials & methods: Models were fit to patient-level data corresponding to individual treatment arms. Results: Visual inspection showed that MCMs fit the clinical data best. Survival modeling with MCMs showed that treatment with GO + SC versus SC alone results in higher statistical cure rates for event-free survival (rates: 26-35% vs 21-23%) and overall survival (rates: 48-52% vs 38-44%). Conclusion: MCMs are well suited to modeling long-term survival in acute myeloid leukemia patients. Clinical trial registration: NCT00927498 (ClinicalTrials.gov).

Lay abstract To assess the effectiveness of acute myeloid leukemia (AML) treatments, researchers use statistical models to estimate the survival rate of patients who receive a particular treatment. Some patients receiving certain AML treatments can achieve long-term remission and are often considered 'cured'. Standard statistical models cannot differentiate between cured and uncured patients and so tend to underestimate the survival rates of cured patients. Mixture cure models (MCMs) can account separately for the survival of cured versus uncured patients. We tested MCMs and standard statistical models using data from a clinical trial comparing gemtuzumab ozogamicin (GO) + standard chemotherapy against standard chemotherapy alone in AML patients. Of all the models tested, MCMs generated survival extrapolations over time that most closely resembled the data from the clinical trial. Through our analyses, we demonstrated that GO + standard chemotherapy can result in higher survival rates than standard chemotherapy alone.

Extrapolating Survival Data Using Historical Trial-Based a Priori Distributions.

OBJECTIVES: To show how clinical trial data can be extrapolated using historical trial data-based a priori distributions. METHODS: Extrapolations based on 30-month pivotal multiple myeloma trial data were compared with 75-month data from the same trial. The 30-month data represent a typical decision-making scenario where early results from a clinical trial are extrapolated. Mature historical trial data with the same comparator as in the pivotal trial were incorporated in 2 stages. First, the parametric distribution selection was based on the historical trial data. Second, the shape parameter estimate of the historical trial was used to define an informative a priori distribution for the shape of the 30-month pivotal trial data. The method was compared with standard approaches, fitting parametric distributions to the 30-month data with noninformative prior. The predicted survival of each method was compared with the observed survival (ΔAUC) in the 75-month trial data. RESULTS: The Weibull had the best fit to the historical trial and the log-normal to the 30-month pivotal trial data. The ΔAUC of the Weibull with informative priors was considerably smaller compared with the standard Weibull. Also, the predicted median survival based on the Weibull with informative priors was more accurate (melphalan and prednisone [MP] 40 months, and bortezomib [V] combined with MP [VMP] 62 months) than based on the standard Weibull (MP 45 months and VMP 72 months) when compared with the observed median (MP 41.3 months and VMP 56.4 months). CONCLUSIONS: Extrapolation of clinical trial data is improved by using historical trial data-based informative a priori distributions.

Do Surrogate Endpoints Better Correlate with Overall Survival in Studies That Did Not Allow for Crossover or Reported Balanced Postprogression Treatments? An Application in Advanced Non-Small Cell Lung Cancer.

BACKGROUND: In previous studies, correlation between overall survival (OS) and surrogate endpoints like objective response rate (ORR) or progression-free survival (PFS) in advanced non-small cell lung cancer (NSCLC) was poor. This can be biased by crossover and postprogression treatments. OBJECTIVES: To evaluate the relationship between these two surrogate endpoints and OS in advanced NSCLC studies that did not allow for crossover or reported balanced post-progression treatments. METHODS: A systematic review in patients with advanced NSCLC receiving second- and further-line therapy was performed. The relationship between the absolute difference in ORR or median PFS (mPFS) and the absolute difference in median OS (mOS) was assessed using the correlation coefficient (R) and weighted regression models. The analysis was repeated in predefined data cuts based on crossover and balance of postprogression treatments. When the upper limit of R's 95% confidence interval (CI) was more than 0.7, the surrogate threshold effect (STE) was estimated. RESULTS: In total, 146 randomized clinical trials (43,061 patients) were included. The mean ORR, mPFS, and mOS were 12.2% ± 11.2%, 3.2 ± 1.3 months, and 9.6 ± 4.1 months, respectively. The correlation coefficients of ORR and mPFS were 0.181 (95% CI 0.016-0.337) and 0.254 (95% CI 0.074-0.418), respectively, with mOS. Nevertheless, in trials that did not allow crossover and reported balanced postprogression treatments, the correlation coefficients of ORR and mPFS were 0.528 (95% CI 0.081-0.798) and 0.778 (95% CI 0.475-0.916), respectively, with mOS. On the basis of STE estimation, in trials showing significant treatment effect size of 41.0% or more ORR or 4.15 or more mPFS months, OS benefit can be expected with sufficient certainty. CONCLUSIONS: Crossover and postprogression treatments may bias the relationship between surrogate endpoints and OS. Presented STE calculation can be used to interpret treatment effect on either ORR or PFS when used as primary endpoints.

Proteasome inhibitors and IMiDs can overcome some high-risk cytogenetics in multiple myeloma but not gain 1q21.

Chromosomal aberrations have significant prognostic importance in multiple myeloma (MM). However, proteasome inhibitors (PI) and IMiDs may partly overcome the poor prognostic impact of some of them. In this study, we investigated a population-based consecutive cohort newly diagnosed patients with MM admitted during a defined time period to hospitals in Denmark, Norway, and Sweden. The impact of treatment modality on the prognostic importance of specific chromosomal aberration was investigated, with special reference to gain 1q21. The median follow-up of patients still alive at analysis was 40 months for the high-dose (HDT)-treated ones and 29 months for the whole population. Three hundred forty-seven patients with a known 1q21 status were included in this study. The 347 patients were divided into three groups, that is, 119 patients with the 1q21 gain, 105 patients with other aberrations (OA), that is, del(13q), del(17p), t(4,14), and/or (14;16), and 123 patients with no aberrations (NA). The groups were compared in terms of overall survival (OS), time to progression (TTP), and response. The 3-yr OS for patients with gain 1q21 was 60% compared to patients with OA 74% and NO 82% (gain 1q21 vs. NO P < 0.001; gain 1q21 vs. OA P = 0.095). If treated with PI or IMiDs, the 3-yr OS was 58% for patients with gain 1q21 compared to patients with OA 78% and NO 78%, respectively (P = 0.041, P = 0.140). In HDT patients, the 3-yr OS was 69% for patients with gain 1q21 compared to patients with OA 84% and NO 88%, respectively (P < 0.008, P = 0.600). Thus, neither HDT nor using PI or IMiDs could overcome the poor prognostic impact of gain 1q21, while these drugs and HDT seemed to improve OS in patients with OA, approaching the survival in NO. Further, gain 1q21 appears to be one of the most important poor prognostic chromosomal aberrations in multiple myeloma with current treatments. Trials using new drugs or allogeneic transplantation are warranted.

Which factors enhance positive drug reimbursement recommendation in Scotland? A retrospective analysis 2006-2013.

OBJECTIVES: To identify the factors that influence the Scottish Medicines Consortium (SMC) in deciding whether to accept pharmaceutical technologies for use within the Scottish health care system. METHODS: A database of SMC submissions between 2006 and 2013 was created, containing a range of clinical, economic, and other factors extracted from published health technology assessment reports. A binomial outcome variable was used, defined as the decision to "accept for use" or "not recommend" a technology. Univariate and multivariate analyses were conducted to assess the impact by means of odds ratios (ORs) of the submitted evidence on the recommendation decision. RESULTS: Out of 463 applications, 265 were accepted for use (57%) and 198 (43%) were not recommended for use within National Health Service Scotland. Univariate analyses showed that 13 variables significantly affected the SMC decision. Of these 13 variables, 7 variables were shown to have a meaningful impact in the multivariate analysis. Four of these concerned the outcome of cost-effectiveness analyses; the fact that a submission was supported by a cost-minimization analysis was the strongest positive variable (OR = 10.30) and a submission showing a product not being cost-effective (i.e., incremental cost-effectiveness ratio above £30,000/quality-adjusted life-year gained) was the strongest negative predictor (OR = 0.47). The other variables concerned whether the submission was related to a product indicated for a nervous system disease (OR = 0.41), whether it was indicated for nonchronic use (OR = 1.66), and whether the submission was performed by a big company (OR = 2.83). CONCLUSIONS: This study demonstrated that the outcome of cost-effectiveness analyses is an important factor affecting the SMC's reimbursement recommendation decision.

Improved estimation of overall survival and progression-free survival for state transition modeling.

Aim: National Institute for Health and Care Excellence guidance (Technical Support Document 19) highlights a key challenge of state transition models (STMs) being their difficulty in achieving a satisfactory fit to the observed within-trial endpoints. Fitting poorly to data over the trial period can then have implications for long-term extrapolations. A novel estimation approach is defined in which the predicted overall survival (OS) and progression-free survival (PFS) extrapolations from an STM are optimized to provide closer estimates of the within-trial endpoints. Materials & methods: An STM was fitted to the SQUIRE trial data in non-small-cell lung cancer (obtained from Project Data Sphere). Two methods were used: a standard approach whereby the maximum likelihood was utilized for the individual transitions and the best-fitting parametric model selected based on AIC/BIC, and a novel approach in which parameters were optimized by minimizing the area between the STM-predicted OS and PFS curves and the corresponding OS and PFS Kaplan-Meier curves. Sensitivity analyses were conducted to assess uncertainty. Results: The novel approach resulted in closer estimations to the OS and PFS Kaplan-Meier for all combinations of parametric distributions analyzed compared with the standard approach. Though the uncertainty associated with the novel approach was slightly larger, it provided better estimates to the restricted mean survival time in 10 of the 12 parametric distributions analyzed. Conclusion: A novel approach is defined which provides an alternative STM estimation method enabling improved fits to modeled endpoints, which can easily be extended to more complex model structures.

Carfilzomib, daratumumab, and dexamethasone (KdD) vs. lenalidomide-sparing pomalidomide-containing triplet regimens for relapsed/refractory multiple myeloma: an indirect treatment comparison.

Nearly all patients with multiple myeloma eventually relapse or become refractory to treatment. Lenalidomide is increasingly administered in the frontline until disease progression or intolerance to therapy, resulting in the need for highly effective, lenalidomide-sparing options. In this study, carfilzomib plus daratumumab and dexamethasone were evaluated against lenalidomide-sparing, pomalidomide-containing triplets using matching-adjusted indirect comparison in the absence of head-to-head data. The analyses utilized long-term follow-up data from the CANDOR study (NCT03158688). Treatment with carfilzomib, daratumumab, and dexamethasone resulted in significantly longer progression-free survival (hazard ratio 0.60 [95% confidence interval: 0.37, 0.88])vs. pomalidomide plus bortezomib and dexamethasone, and numerically longer progression-free survival (hazard ratio 0.77 [95% confidence interval: 0.50, 1.08]) vs. daratumumab plus pomalidomide and dexamethasone in patients with relapsed/refractory multiple myeloma and previous lenalidomide exposure, the majority of whom were lenalidomide refractory. Carfilzomib plus daratumumab and dexamethasone offers a highly effective, lenalidomide-sparing treatment option for this population.

Feasibility of Indirect Treatment Comparisons Between Niraparib Plus Abiraterone Acetate and Other First-Line Poly ADP-Ribose Polymerase Inhibitor Treatment Regimens for Patients with BRCA1/2 Mutation-Positive Metastatic Castration-Resistant Prostate Cancer.

INTRODUCTION: Poly(ADP-ribose) polymerase inhibitors (PARPi) are a novel option to treat patients with metastatic castration-resistant prostate cancer (mCRPC). Niraparib plus abiraterone acetate and prednisone (AAP) is indicated for BRCA1/2 mutation-positive mCRPC. Niraparib plus AAP demonstrated safety and efficacy in the phase 3 MAGNITUDE trial (NCT03748641). In the absence of head-to-head studies comparing PARPi regimens, the feasibility of conducting indirect treatment comparisons (ITC) to inform decisions for patients with first-line BRCA1/2 mutation-positive mCRPC has been explored. METHODS: A systematic literature review was conducted to identify evidence from randomized controlled trials on relevant comparators to inform the feasibility of conducting ITCs via network meta-analysis (NMA) or population-adjusted indirect comparisons (PAIC). Feasibility was assessed based on network connectivity, data availability in the BRCA1/2 mutation-positive population, and degree of within- and between-study heterogeneity or bias. RESULTS: NMAs between niraparib plus AAP and other PARPi regimens (olaparib monotherapy, olaparib plus AAP, and talazoparib plus enzalutamide) were inappropriate due to the disconnected network, differences in trial populations related to effect modifiers, or imbalances within BRCA1/2 mutation-positive subgroups. The latter issue, coupled with the lack of a common comparator (except for olaparib plus AAP), also rendered anchored PAICs infeasible. Unanchored PAICs were either inappropriate due to lack of population overlap (vs. olaparib monotherapy) or were restricted by unmeasured confounders and small sample size (vs. olaparib plus AAP). PAIC versus talazoparib plus enzalutamide was not possible due to lack of published arm-level baseline characteristics and sufficient efficacy outcome data in the relevant population. CONCLUSION: The current randomized controlled trial evidence network does not permit robust comparisons between niraparib plus AAP and other PARPi regimens for patients with 1L BRCA-positive mCRPC. Decision-makers should scrutinize any ITC results in light of their limitations. Real-world evidence combined with clinical experience should inform treatment recommendations in this indication.

Network meta-interpolation: Effect modification adjustment in network meta-analysis using subgroup analyses.

Effect modification (EM) may cause bias in network meta-analysis (NMA). Existing population adjustment NMA methods use individual patient data to adjust for EM but disregard available subgroup information from aggregated data in the evidence network. Additionally, these methods often rely on the shared effect modification (SEM) assumption. In this paper, we propose Network Meta-Interpolation (NMI): a method using subgroup analyses to adjust for EM that does not assume SEM. NMI balances effect modifiers across studies by turning treatment effect (TE) estimates at the subgroup- and study level into TE and standard errors at EM values common to all studies. In an extensive simulation study, we simulate two evidence networks consisting of four treatments, and assess the impact of departure from the SEM assumption, variable EM correlation across trials, trial sample size and network size. NMI was compared to standard NMA, network meta-regression (NMR) and Multilevel NMR (ML-NMR) in terms of estimation accuracy and credible interval (CrI) coverage. In the base case non-SEM dataset, NMI achieved the highest estimation accuracy with root mean squared error (RMSE) of 0.228, followed by standard NMA (0.241), ML-NMR (0.447) and NMR (0.541). In the SEM dataset, NMI was again the most accurate method with RMSE of 0.222, followed by ML-NMR (0.255). CrI coverage followed a similar pattern. NMI's dominance in terms of estimation accuracy and CrI coverage appeared to be consistent across all scenarios. NMI represents an effective option for NMA in the presence of study imbalance and available subgroup data.

MET Exon 14 Skipping in NSCLC: A Systematic Literature Review of Epidemiology, Clinical Characteristics, and Outcomes.

INTRODUCTION: MET exon 14 (METex14) skipping is a rare oncogenic driver in non-small-cell lung cancer (NSCLC) for which targeted therapy with MET tyrosine kinase inhibitors (TKIs) was recently approved. Given the heterogeneity in published data of METex14 skipping NSCLC, we conducted a systematic literature review to evaluate its frequency, patient characteristics, and outcomes. METHODS: On June 13, 2022 we conducted a systematic literature review of publications and conference abstracts reporting frequency, patient characteristics, or outcomes of patients with METex14 skipping NSCLC. RESULTS: We included 139 studies reporting frequency or patient characteristics (350,997 patients), and 39 studies reporting clinical outcomes (3989 patients). Median METex14 skipping frequency was 2.0% in unselected patients with NSCLC, with minimal geographic variation. Median frequency was 2.4% in adenocarcinoma or nonsquamous subgroups, 12.0% in sarcomatoid, and 1.3% in squamous histology. Patients with METex14 skipping NSCLC were more likely to be elderly, have adenocarcinoma histology; there was no marked sex or smoking status distribution. In first line of treatment, median objective response rate ranged from 50.7% to 68.8% with targeted therapies (both values correspond to MET TKIs), was 33.3% with immunotherapy, and ranged from 23.1% to 27.0% with chemotherapy. CONCLUSIONS: Patients with METex14 skipping are more likely to have certain characteristics, but no patient subgroup can be ruled out; thus, it is crucial to test all patients with NSCLC to identify suitable candidates for MET inhibitor therapy. MET TKIs appeared to result in higher efficacy outcomes, although no direct comparison with chemotherapy or immunotherapy regimens was found.

Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity.

Aim: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH) WMC NMA to inform long-term survival of therapies. Materials & methods: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumab and atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. Results: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15) and 0.03 (0.00; 0.09) for docetaxel, nivolumab, pembrolizumab and atezolizumab, respectively, with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37) and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20) and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96) and 1.36 (-0.05; 3.64). Conclusion: BH-WMC-NMA impacts incremental mean LYs and cost-effectiveness ratios, potentially affecting reimbursement decisions.

Evaluation of Cost-Effectiveness of Adjuvant Osimertinib in Patients with Resected EGFR Mutation-Positive Non-small Cell Lung Cancer.

BACKGROUND: For many patients with resected epidermal growth factor receptor mutation-positive (EGFRm) non-small cell lung cancer (NSCLC), current standard of care (SoC) is adjuvant chemotherapy; however, disease recurrence remains high. Based on positive results from ADAURA (NCT02511106), adjuvant osimertinib was approved for treatment of resected stage IB‒IIIA EGFRm NSCLC. OBJECTIVE: The aim was to assess the cost-effectiveness of adjuvant osimertinib in patients with resected EGFRm NSCLC. METHODS: A five-health-state, state-transition model with time dependency was developed to estimate lifetime (38 years) costs and survival of resected EGFRm patients treated with adjuvant osimertinib or placebo (active surveillance), with/without prior adjuvant chemotherapy, using a Canadian Public Healthcare perspective. Transitions between health states were modeled using ADAURA and FLAURA (NCT02296125) data, Canadian life tables, and real-world data (CancerLinQ Discovery®). The model used a 'cure' assumption: patients remaining disease free for 5 years after treatment completion for resectable disease were deemed 'cured.' Health state utility values and healthcare resource usage estimates were derived from Canadian real-world evidence. RESULTS: In the reference case, adjuvant osimertinib treatment led to a mean 3.20 additional quality-adjusted life-years (QALYs; (11.77 vs 8.57) per patient, versus active surveillance. The modeled median percentage of patients alive at 10 years was 62.5% versus 39.3%, respectively. Osimertinib was associated with mean added costs of Canadian dollars (C$)114,513 per patient and a cost/QALY (incremental cost-effectiveness ratio) of C$35,811 versus active surveillance. Model robustness was demonstrated by scenario analyses. CONCLUSIONS: In this cost-effectiveness assessment, adjuvant osimertinib was cost-effective compared with active surveillance for patients with completely resected stage IB‒IIIA EGFRm NSCLC after SoC.

Bridging the gap between oncology clinical trials and real-world data: evidence on replicability of efficacy results using German claims data.

Aims: Using German claims, the authors replicated the CHAARTED trial in metastatic hormone-sensitive prostate cancer. Methods: The authors identified metastatic hormone-sensitive prostate cancer patients replicating the inclusion/exclusion criteria of CHAARTED. Patients treated with docetaxel in combination with androgen deprivation therapy (ADT) at first line (docetaxel group) were compared with patients treated with ADT monotherapy (ADT mono group). After propensity score matching, overall survival was compared between the matched cohorts. Results: The authors included 441 patients. After propensity score matching, two equally sized matched cohorts of 74 patients each were compared in terms of overall survival. The hazard ratio (HR) was 0.71 (95% CI: 0.42-1.19), comparable to the HR in CHAARTED (HR: 0.72; 95% CI: 0.59-0.89). Conclusions: Using early comparative evidence from real-world data for regulatory and health technology assessment decisions is useful.

Indirect Treatment Comparison of Daratumumab, Pomalidomide, and Dexamethasone Versus Standard of Care in Patients with Difficult-to-Treat Relapsed/Refractory Multiple Myeloma.

INTRODUCTION: The phase 3 APOLLO study demonstrated significantly better progression-free survival (PFS) and clinical responses with daratumumab, pomalidomide, and dexamethasone (D-Pd) versus pomalidomide and dexamethasone (Pd) in patients with relapsed/refractory multiple myeloma (RRMM). On the basis of these results and those from the phase 1b EQUULEUS trial, D-Pd was approved in this patient population. In the absence of head-to-head data comparing D-Pd with further standard of care (SOC) therapies, indirect treatment comparisons (ITCs) can provide important information to help optimize treatment selection. The objective of this study was to indirectly compare PFS improvement with D-Pd versus daratumumab, bortezomib, and dexamethasone (D-Vd) and D-Pd versus bortezomib and dexamethasone (Vd) in patients with RRMM. METHODS: Patient-level data were from APOLLO, EQUULEUS, and CASTOR. Three methods of adjusting imbalances in baseline characteristics including stabilized inverse probability of treatment weighting (sIPTW), cardinality matching (CM), and propensity score matching (PSM) were initially considered. CM offers mathematically guaranteed largest matched sample meeting pre-specified maximum standardized mean difference criteria for matching covariates. sIPTW and PSM were based on propensity scores derived from logistic regression. Feasibility assessment of the PSM method returned too low effective sample size to support a meaningful comparison. CM was chosen as the base case and sIPTW as a sensitivity analysis. RESULTS: After harmonized eligibility criteria were applied, 253, 104, and 122 patients from the D-Pd, D-Vd, and Vd cohorts, respectively, were included in the ITC analyses. Some imbalances in baseline characteristics were identified between D-Pd and D-Vd/Vd cohorts that remained after adjustment. PFS hazard ratios showed significant improvement for D-Pd over D-Vd and Vd for CM and sIPTW analyses. CONCLUSIONS: Results showed consistent PFS benefit for D-Pd versus D-Vd and Vd regardless of the adjustment technique used. These findings support the use of D-Pd versus D-Vd or Vd in patients with difficult-to-treat RRMM. TRIAL REGISTRATION: NCT03180736; NCT02136134, NCT01998971.

Health impact of tafamidis in transthyretin amyloid cardiomyopathy patients: an analysis from the Tafamidis in Transthyretin Cardiomyopathy Clinical Trial (ATTR-ACT) and the open-label long-term extension studies.

AIM: The Tafamidis in Transthyretin Cardiomyopathy Clinical Trial (ATTR-ACT) showed that tafamidis reduced all-cause mortality and cardiovascular-related hospitalizations in patients with transthyretin amyloid cardiomyopathy (ATTR-CM). This study aimed to estimate the impact of tafamidis on survival and quality-adjusted life-years (QALYs). METHODS AND RESULTS: A multi-state, cohort, Markov model was developed to simulate the disease course of ATTR-CM throughout a lifetime. For survival extrapolation, survival curves were fitted by treatment arm and New York Heart Association (NYHA) Class I/II (68% of patients) and NYHA Class III (32% of patients) cohorts using the individual patient-level data from both the ATTR-ACT and the corresponding long-term extension study. Univariate and multivariate sensitivity analyses were conducted. The predicted mean survival for the total population (NYHA Class I/II + III) was 6.73 years for tafamidis and 2.85 years for the standard of care (SoC), resulting in an incremental mean survival of 3.88 years [95% confidence interval (CI) 1.32-5.66]. Of the 6.73 life-years, patients on tafamidis spend, on average, 4.82 years in NYHA Class I/II, while patients on SoC spend an average of 1.60 life-years in these classes. The combination of longer survival in lower NYHA classes produced a QALY gain of 5.39 for tafamidis and 2.11 for SoC, resulting in 3.29 incremental QALYs (95% CI 1.21-4.74) in favour of tafamidis. CONCLUSION: Based on the disease simulation model results, tafamidis is expected to more than double the life expectancy and QALYs of ATTR-CM patients compared to SoC. Longer-term follow-up data from the ATTR-ACT extension study will further inform these findings. CLINICAL TRIALS.GOV IDENTIFIER: NCT01994889 (date of registration: 26 November 2013).

Novel and existing flexible survival methods for network meta-analyses.

Aim: Technical Support Document 21 discusses trial-based, flexible relative survival models. The authors generalized flexible relative survival models to the network meta-analysis (NMA) setting while accounting for different treatment-effect specifications. Methods: The authors compared the standard parametric model with mixture, mixture cure and nonmixture cure, piecewise, splines and fractional polynomial models. The optimal treatment-effect parametrization was defined in two steps. First, all models were run with treatment effects on all parameters and subsequently the optimal model was defined by removing uncertain treatment effects, for which the parameter was smaller than its standard deviation. The authors used a network in previously treated advanced non-small-cell lung cancer. Results: Flexible model-based NMAs impact fit and incremental mean survival and they increase corresponding uncertainty. Treatment-effect specification impacts incremental survival, reduces uncertainty and improves the fit statistic. Conclusion: Extrapolation techniques already available for individual trials can now be used for NMAs to ensure that the most plausible extrapolations are being used for health technology assessment submissions.