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Development of medicines in rare oncologic patient populations are growing, but well-powered randomized controlled trials are typically extremely challenging or unethical to conduct in such settings. External control arms using real-world data are increasingly used to supplement clinical trial evidence where no or little control arm data exists. The construction of an external control arm should always aim to match the population, treatment settings and outcome measurements of the corresponding treatment arm. Yet, external real-world data is typically fraught with limitations including missing data, measurement error and the potential for unmeasured confounding given a nonrandomized comparison. Quantitative bias analysis (QBA) comprises a collection of approaches for modelling the magnitude of systematic errors in data which cannot be addressed with conventional statistical adjustment. Their applications can range from simple deterministic equations to complex hierarchical models. QBA applied to external control arm represent an opportunity for evaluating the validity of the corresponding comparative efficacy estimates. We provide a brief overview of available QBA approaches and explore their application in practice. Using a motivating example of a comparison between pralsetinib single-arm trial data versus pembrolizumab alone or combined with chemotherapy real-world data for RET fusion-positive advanced non-small cell lung cancer (aNSCLC) patients (1-2% among all NSCLC), we illustrate how QBA can be applied to external control arms. We illustrate how QBA is used to ascertain robustness of results despite a large proportion of missing data on baseline ECOG performance status and suspicion of unknown confounding. The robustness of findings is illustrated by showing that no meaningful change to the comparative effect was observed across several 'tipping-point' scenario analyses, and by showing that suspicion of unknown confounding was ruled out by use of E-values. Full R code is also provided.

Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health technology assessment (HTA) agencies. Unmeasured confounding is a primary concern with this type of evidence, as it may result in biased treatment effect estimates, which has led to much criticism of NRS by HTA agencies. Quantitative bias analyses are a group of methods that have been developed in the epidemiological literature to quantify the impact of unmeasured confounding and adjust effect estimates from NRS. Key considerations for application in HTA proposed in this article reflect the need to balance methodological complexity with ease of application and interpretation, and the need to ensure the methods fit within the existing frameworks used to assess nonrandomized evidence by HTA bodies.

Due to uncertainty regarding the potential impact of unmeasured confounding, health technology assessment (HTA) agencies often disregard evidence from nonrandomized studies when considering new technologies. Quantitative bias analysis (QBA) methods provide a means to quantify this uncertainty but have not been widely used in the HTA setting, particularly in the context of cost-effectiveness modelling (CEM). This study demonstrated the application of an aggregate and patient-level QBA approach to quantify and adjust for unmeasured confounding in a simulated nonrandomized comparison of survival outcomes. Application of the QBA output within a CEM through deterministic and probabilistic sensitivity analyses and under different scenarios of knowledge of an unmeasured confounder demonstrates the potential value of QBA in HTA.

Aim: There are different methods to identify chronic kidney disease (CKD) in Clinical Practice Research Datalink (CPRD)-Hospital Episode Statistics (HES). Methods: Using CPRD-HES, nonvalvular atrial fibrillation patients were classified according to CKD category. Results: Using glomerular filtration rate/estimated glomerular filtration rate tests only to identify patients with CKD resulted in 3.5% stage 2, 2.7% stage 3, 0.3% stage 4 and 0.03% stage 5. Using data from diagnostic codes to identify patients with CKD resulted in 1.4% stage 3, 0.4% stage 4 and 0.3% stage 5. Using test records and codes resulted in 3.5% stage 2, 4.0% stage 3, 0.6% stage 4 and 0.4% stage 5. Conclusion: To identify CKD status in CPRD-HES, a combination of test records and codes should be used. Using diagnostic codes only significantly underestimates CKD prevalence.

Aim: The impact of different strategies to handle patients with data recorded under multiple Clinical Practice Research Datalink (CPRD) identifiers (IDs) is unknown. Patients and methods: Six approaches to handling patients appearing under multiple CPRD IDs were defined. The impact of the approaches was illustrated using a case study describing the clinical characteristics of a population of nonvalvular atrial fibrillation patients. Results: 5.6% of patients had more than one CPRD ID. Across all six approaches implemented, no material difference in the characteristics of nonvalvular atrial fibrillation patients were observed. Conclusion: While strategies which longitudinally append patient registration periods under different CPRD IDs maintain independence while using all available data, their implementation had little impact on the results of our case study.

Aim: This study aimed to evaluate the risk of major bleeding among two cohorts of nonvalvular atrial fibrillation patients newly initiating a vitamin K antagonist (VKA) or apixaban in a real-world setting in Italy., Patients & Methods: A retrospective study using a large administrative database of Italian local health units was performed, using data from ten local health units and patients were included from the date of new initiation of apixaban or VKAs from January 2012 to June 2015., Results: Risk of major bleeding was calculated using an adjusted Cox regression model. Compared with VKA, apixaban had a significantly lower risk of major bleeding (hazard ratio = 0.44 [95% CI: 0.12-0.97])., Conclusion: In this analysis, apixaban was associated with a lower risk of major bleeding compared with VKA.