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In order to identify ways to improve cost estimation, especially early in design, cost estimation needs to be viewed and represented as a process. An important activity within the cost estimation process is assessing the cost risk of a system. A decision-support tool that assesses cost risk should represent the impact of subsystem or system-level uncertainty and provide mechanisms to help select among competing designs. In order to address these problems, a generic cost estimation process was developed. It is based on an extensive review of the cost estimation literature. Also, a hierarchicial product structure, model-based approach and tool to estimate system-level cost risk was developed. This tool provides a link between cost models and cost elements for each component, mechanisms for determining the impact of risk on the cost of the design, and outputs used for selecting among alternative competing designs.

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Background: Copula methods have been proposed as a way of modeling dependence between random variables because it lies in the flexibility of the assumption on marginals. As previous authors stated, "A copula is a function which joins or “couples” a multivariate distribution function to its one-dimensional marginal distribution functions. Given that cost and effectiveness are often related to each other and therefore they show statistical dependence, the use of copulas to handle uncertainty caused by sampling variation could be potentially useful when cost-effectiveness analyses (CEA) are performed using patient-level data. The objective of this study was to empirically compare various copula distributions with two traditional methods, namely, the bootstrapping approach and the Bayesian approach assuming that incremental cost and LYs gained are bivariate normally distributed. Methods: The patient-level data from a previously published observational study were analyzed using four copula distributions: independent, Farlie-Gumbel-Morgenstern (FGM), Frank and Clayton copulas. Using the results from the traditional methods previously published, models were compared in terms of incremental cost, incremental life years (LYs) gained and the cost-effectiveness acceptability curves (CEACs) based on the net monetary benefit (NMB). Results: Using the traditional methods provided similar results. The most pronounced impact was the improvement in precision given that the confidence intervals were so much narrower for the copulas methods in comparison to the traditional methods. Consequently, the probability of being optimal derived from the Frank and Clayton copulas were close to 1.0 at a willingness to pay (𝜆) of CA$20,000. By contrast, the traditional methods were optimal for a 𝜆 of $100,000 CAD. Conclusions: The results of this study demonstreate the potential impact and importance of copulas in patient-level cost-effectiveness analysis. This approach could be particularly important in those situations where the data suggests some kind of dependence and some restrictions on the marginals, as observed in our case study. / Thesis / Master of Science (MSc)