Identifying the optimal use of CTCs in the early staging phase of breast cancer

Objectives: Circulating tumour cells (CTCs) in the blood can give important information about the prognosis and treatment options for cancer patients. Methods like cell-search are not sensitive enough because the blood samples are small (7,5 mL). Currently a technique is developed which can separate CTCs from the whole blood and is called the CTC Trap. This study addresses the potential impact of implementing the CTC Trap in addition to currently used imaging techniques in early staging of primary stage I-III breast cancer in women. Methods: The early staging process has been identified using the Dutch breast cancer guideline. This process is displayed in a decision tree. Three points in this process have been identified as possible implementation options for the CTC Trap. A simulation model has been built in Excel to simulate the cost-effectiveness of implementing the CTC Trap at these three different points. Results: Potentially relevant points for the CTC trap are: 1) following negative sentinel lymph node procedure to test for micro metastases, 2) following negative result of initial MRI to test for (micro-) metastases, 3) following negative results of further imaging. Usual care resulted in an average survival of 2,42 years, a 3-year survival of 93,71%, 1,51 QALYs and a cost of € 992,56. When implemented at all 3 implementation points simultaneously CTC Trap resulted in an average survival of 2,84 years, a 3-year survival of 97,46 %, 1,84 QALYS and a total cost of € 6.035,45. Conclusions: CTCs clearly have the potential to improve overall survival. Use of CTCs can potentially improve survival with 0,42 years and improve QALYs with 0,34. Costs do increase at all options but from a health economic perspective it is most valuable to implement CTC Trap in option 1) following negative sentinel lymph node procedure to test for (micro-) metastases

A scoping review of metamodeling applications and opportunities for advanced health economic analyses

Introduction: Metamodels, also known as meta-models, surrogate models, or emulators, are used in several fields of research to negate runtime issues with analyzing computational demanding simulation models. This study introduces metamodeling and presents results of a review on metamodeling applications in health economics. Areas covered: A scoping review was performed to identify studies that applied metamodeling methods in a health economic context. After search and selection, 13 publications were found to employ metamodeling methods in health economics. Metamodels were used to perform value of information analysis (n = 5, 38%), deterministic sensitivity analysis (n = 4, 31%), model calibration (n = 1, 8%), probabilistic sensitivity analysis (n = 1), or optimization (n = 1, 8%). One study was found to extrapolate a simulation model to other countries (n = 1, 8%). Applied metamodeling techniques varied considerably between studies, with linear regression being most frequently applied (n = 7, 54%). Expert commentary: Although it has great potential to enable computational demanding analyses of health economic models, metamodeling in health economics is still in its infancy, as illustrated by the limited number of applications and the relatively simple metamodeling methods applied. Comprehensive guidance specific to health economics is needed to provide modelers with the information and tools needed to utilize the full potential of metamodels

Communicating Uncertainty in Economic Evaluations:Verifying Optimal Strategies

Background. In cost-effectiveness analysis (CEA), it is common to compare a single, new intervention with 1 or more existing interventions representing current practice ignoring other, unrelated interventions. Sectoral CEAs, in contrast, take a perspective in which the costs and effectiveness of all possible interventions within a certain disease area or health care sector are compared to maximize health in a society given resource constraints. Stochastic league tables (SLT) have been developed to represent uncertainty in sectoral CEAs but have 2 shortcomings: 1) the probabilities reflect inclusion of individual interventions and not strategies and 2) data on robustness are lacking. The authors developed an extension of SLT that addresses these shortcomings. Methods. Analogous to non-probabilistic MAXIMIN decision rules, the uncertainty of the performance of strategies in sectoral CEAs may be judged with respect to worst possible outcomes, in terms of health effects obtainable within a given budget. Therefore, the authors assessed robustness of strategies likely to be optimal by performing optimization separately on all samples and on samples yielding worse than expected health benefits. The approach was tested on 2 examples, 1 with independent and 1 with correlated cost and effect data. Results. The method was applicable to the original SLT example and to a new example and provided clear and easily interpretable results. Identification of interventions with robust performance as well as the best performing strategies was straightforward. Furthermore, the robustness of strategies was assessed with a MAXIMIN decision rule. Conclusion. The SLT extension improves the comprehensibility and extends the usefulness of outcomes of SLT for decision makers. Its use is recommended whenever an SLT approach is considered

Data reconciliation of immersive heart inspection

IVUS images are complicated medical datasets suffering from some artifacts caused by the data acquisition method of immersive heart inspection. Data reconciliation, which removes tracing and tracking uncertainties of these datasets, is an important step for the medical application of remodeling the arteries in virtual reality to aid diagnosing and treating heart diseases. This paper provides an empirical data reconciliation method, which fuses the features of the coronary longitudinal movement with motion compensation model. It explains the distortion of the data set well and provides a method to analyze and reconcile the dataset

Introduction to metamodeling for reducing computational burden of advanced analyses with health economic models : a structured overview of metamodeling methods in a 6-step application process

Metamodels can be used to reduce the computational burden associated with computationally demanding analyses of simulation models, though applications within health economics are still scarce. Besides a lack of awareness of their potential within health economics, the absence of guidance on the conceivably complex and time-consuming process of developing and validating metamodels may contribute to their limited uptake. To address these issues, this paper introduces metamodeling to the wider health economic audience and presents a process for applying metamodeling in this context, including suitable methods and directions for their selection and use. General (i.e., non-health economic specific) metamodeling literature, clinical prediction modeling literature, and a previously published literature review were exploited to consolidate a process and to identify candidate metamodeling methods. Methods were considered applicable to health economics if they are able to account for mixed (i.e., continuous and discrete) input parameters and continuous outcomes. Six steps were identified as relevant for applying metamodeling methods within health economics, i.e. 1) the identification of a suitable metamodeling technique, 2) simulation of datasets according to a design of experiments, 3) fitting of the metamodel, 4) assessment of metamodel performance, 5) conduct the required analysis using the metamodel, and 6) verification of the results. Different methods are discussed to support each step, including their characteristics, directions for use, key references, and relevant R and Python packages. To address challenges regarding metamodeling methods selection, a first guide was developed towards using metamodels to reduce the computational burden of analyses of health economic models. This guidance may increase applications of metamodeling in health economics, enabling increased use of state-of-the-art analyses, e.g. value of information analysis, with computationally burdensome simulation models