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Aim: This study aimed to improve comparative effectiveness estimates and discuss challenges encountered through the application of Bayesian borrowing (BB) methods to augment an external control arm (ECA) constructed from real-world data (RWD) using historical clinical trial data in first-line non-small cell lung cancer (NSCLC). Materials & methods: An ECA for a randomized controlled trial (RCT) in first-line NSCLC was constructed using Concert AI Patient360™ to assess chemotherapy with or without cetuximab, in the bevacizumab-inappropriate subpopulation. Cardinality matching was used to match patient characteristics between the treatment arm (cetuximab + chemotherapy) and ECA. Overall survival (OS) was assessed as the primary outcome using Cox proportional hazards (PH). BB was conducted using a static power prior under a Weibull PH parameterization with borrowing weights from 0.0 to 1.0 and augmentation of the ECA from a historical control trial. Results: The constructed ECA yielded a higher overall survival (OS) hazard ratio (HR) (HR = 1.53; 95% CI: 1.21–1.93) than observed in the matched population of the RCT (HR = 0.91; 95% CI: 0.73–1.13). The OS HR decreased through the incorporation of BB (HR = 1.30; 95% CI: 1.08–1.54, borrowing weight = 1.0). BB was applied to augment the RCT control arm via a historical control which improved the precision of the observed HR estimate (1.03; 95% CI: 0.86–1.22, borrowing weight = 1.0), in comparison to the matched population of the RCT alone. Conclusion: In this study, the RWD ECA was unable to successfully replicate the OS estimates from the matched population of the selected RCT. The inability to replicate could be due to unmeasured confounding and variations in time-periods, follow-up and subsequent therapy. Despite these findings, we demonstrate how BB can improve precision of comparative effectiveness estimates, potentially aid as a bias assessment tool and mitigate challenges of traditional methods when appropriate external data sources are available.

Abstract Background Treatment personalization via tumor molecular testing holds promise for improving outcomes for patients with pediatric low-grade glioma (PLGG). We evaluate the health economic impact of employing tumor molecular testing to guide treatment for patients diagnosed with PLGG, particularly the avoidance of radiation therapy (RT) for patients with BRAF-fusion. Methods We performed a model-based cost-utility analysis comparing two strategies: molecular testing to determine BRAF fusion status at diagnosis against no molecular testing. We developed a microsimulation to model the lifetime health and cost outcomes (in quality-adjusted life years (QALYs) and 2018 CAD, respectively) for a simulated cohort of 100,000 patients newly diagnosed with PLGG after their initial surgery. Results The life expectancy after diagnosis for individuals who did not receive molecular testing was 39.01 (95% Confidence Intervals (CI): 32.94;44.38) years and 40.08 (95% CI: 33.19;45.76) years for those who received testing. Our findings indicate that patients who received molecular testing at diagnosis experienced a 0.38 (95% CI: 0.08;0.77) gain in QALYs and $1384 (95% CI: $-3486; $1204) reduction in costs over their lifetime. Cost and QALY benefits were driven primarily by the avoidance of long-term adverse events (stroke, secondary neoplasms) associated with unnecessary use of radiation. Conclusions We demonstrate the clinical benefit and cost-effectiveness of molecular testing in guiding the decision to provide RT in PLGG. While our results do not consider the impact of targeted therapies, this work is an example of the value of simulation modeling in assessing the long-term costs and benefits of precision oncology interventions for childhood cancer, which can aid decision-making about health system reimbursement.

BACKGROUND:Flavivirus diseases such as dengue fever (DENV), West Nile virus (WNV), Zika and yellow fever represent a substantial global public health concern. Preexisting chronic conditions such as cardiovascular diseases, diabetes, obesity, and asthma were thought to predict risk of progression to severe infections. OBJECTIVE:We aimed to quantify the frequency of chronic comorbidities in flavivirus diseases to provide an estimate for their prevalence in severe and non-severe infections and examine whether chronic diseases contribute to the increased risk of severe viral expression. METHODS:We conducted a comprehensive search in PubMed, Ovid MEDLINE(R), Embase and Embase Classic and grey literature databases to identify studies reporting prevalence estimates of comorbidities in flavivirus diseases. Study quality was assessed with the risk of bias tool. Age-adjusted odds ratios (ORs) were estimated for severe infection in the presence of chronic comorbidities. RESULTS:We identified 65 studies as eligible for inclusion for DENV (47 studies) and WNV (18 studies). Obesity and overweight (i.e., BMI> 25 kg/m2, prevalence: 24.5%, 95% CI: 18.6-31.6%), hypertension (17.1%, 13.3-21.8%) and diabetes (13.3%, 9.3-18.8%) were the most prevalent comorbidities in DENV. However, hypertension (45.0%, 39.1-51.0%), diabetes (24.7%, 20.2-29.8%) and heart diseases (25.6%, 19.5-32.7%) were the most prevalent in WNV. ORs of severe flavivirus diseases were about 2 to 4 in infected patients with comorbidities such as diabetes, hypertension and heart diseases. The small number of studies in JEV, YFV and Zika did not permit estimating the prevalence of comorbidities in these infections. CONCLUSION:Higher prevalence of chronic comorbidities was found in severe cases of flavivirus diseases compared to non-severe cases. Findings of the present study may guide public health practitioners and clinicians to evaluate infection severity based on the presence of comorbidity, a critical public health measure that may avert severe disease outcome given the current dearth of clear prevention practices for some flavivirus diseases.