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.

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.

Assessing the prognostic utility of hematologic response for overall survival in patients with newly diagnosed AL amyloidosis: results of a meta-analysis.

OBJECTIVES: Amyloid light-chain (AL) amyloidosis is a rare disease characterized by amyloid fibril deposits made up of toxic light chains causing progressive organ dysfunction and death. Recent studies suggest that hematologic response may be an important prognostic indicator of overall survival (OS) in AL amyloidosis. The aim of this study was to evaluate the trial-level association between hematologic complete response (CR) or very good partial response or better (≥ VGPR) and OS in newly diagnosed patients. METHODS: Studies were identified via systematic literature review. Pooled effect estimates were generated by a random-effects model. RESULTS: Nine observational studies reporting hematologic CR or ≥VGPR and OS hazard ratios (HRs) were included in the meta-analysis. Achieving hematologic CR was associated with improved OS (HR, 0.21; 95% confidence interval [CI] 0.13-0.34). Achieving ≥ VGPR was also associated with improved OS (HR 0.21; 95% CI 0.17-0.26). Results of a sensitivity analysis excluding one outlier study revealed no heterogeneity and a better overall HR estimate. Potential limitations of this meta-analysis include the small number of eligible studies (consistent with the rarity of the disease) and inconsistencies in reporting of results. CONCLUSIONS: Overall, our findings support the use of deep hematologic response (CR or ≥VGPR) as a clinical trial endpoint in newly diagnosed AL amyloidosis. This study provides evidence that early hematologic response is a strong patient-level surrogate for long-term OS in patients with AL amyloidosis receiving frontline therapy. Structured data collection of depth of response in future trials will further strengthen these observations.

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.

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.

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.

Involved free light chain: an early independent predictor of response and progression in multiple myeloma.

Serum and urine protein electrophoresis (sPEP/uPEP) are the standard methods for monitoring of multiple myeloma (MM). However, a method of detection with shorter half-life, such as serum-free light chain (FLC), could detect the response or progression earlier. In total, 450 MM patients were assessed in first, second, and third line. Response and progression were classified according to International myeloma working group guidelines. The overall median time to partial response or better was detectable significantly earlier with involved free light chain (iFLC) 1.94 months (IQR: 1.61-2.23) compared to sPEP 5.39 months (IQR: 3.88-7.00). In first line, iFLC detected progression earlier compared to sPEP, particularly in patients with progression more than 18 months after best response. In conclusion, a response observed by iFLC occurs at least a median of 3 months before response is detected by sPEP/uPEP.

Comparative evaluation of involved free light chain and monoclonal spike as markers for progression from monoclonal gammopathy of undetermined significance to multiple myeloma.

Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant clonal plasma cell disorder, with a 1% yearly risk of progression to multiple myeloma (MM). Evolution of M-spike and serum free light chain (sFLC) during follow-up could identify patients at high risk of progression. In this region-wide study, including 4756 individuals, 987 patients with MGUS were identified, and baseline factors as well as evolving involved FLC (iFLC) were evaluated as potential markers for risk of progression from MGUS to MM. Furthermore, evolving iFLC and M-spike were assessed quarterly for a median of 5 years. At baseline, patients that progressed had significantly higher iFLC compared to non-progressors. The risk factors of M-spike >1.5 g/dL, age >65 years and iFLC >100 mg/L were all independently associated with increased risk of MGUS to MM progression. For patients that had any two or three risk factors, the 5-year cumulative probability of progression was significantly higher (31%) compared to no risk factors (2%). Evolving iFLC >100 mg/L during follow-up was consistently associated with increased risk of progression. Based on our observations, we propose to include iFLC as a monitoring tool for all MGUS patients. Furthermore, we recommend a quarterly monitoring in all high-risk patients. Finally, we suggest that the risk of MGUS progression should be stratified with age, M-spike, and iFLC at baseline.