Cost-effectiveness analysis of 3-D computerized tomography colonography versus optical colonoscopy for imaging symptomatic gastroenterology patients.

BACKGROUND: When symptomatic gastroenterology patients have an indication for colonic imaging, clinicians have a choice between optical colonoscopy (OC) and computerized tomography colonography with three-dimensional reconstruction (3-D CTC). 3-D CTC provides a minimally invasive and rapid evaluation of the entire colon, and it can be an efficient modality for diagnosing symptoms. It allows for a more targeted use of OC, which is associated with a higher risk of major adverse events and higher procedural costs. A case can be made for 3-D CTC as a primary test for colonic imaging followed if necessary by targeted therapeutic OC; however, the relative long-term costs and benefits of introducing 3-D CTC as a first-line investigation are unknown. AIM: The aim of this study was to assess the cost effectiveness of 3-D CTC versus OC for colonic imaging of symptomatic gastroenterology patients in the UK NHS. METHODS: We used a Markov model to follow a cohort of 100,000 symptomatic gastroenterology patients, aged 50 years or older, and estimate the expected lifetime outcomes, life years (LYs) and quality-adjusted life years (QALYs), and costs (£, 2010-2011) associated with 3-D CTC and OC. Sensitivity analyses were performed to assess the robustness of the base-case cost-effectiveness results to variation in input parameters and methodological assumptions. RESULTS: 3D-CTC provided a similar number of LYs (7.737 vs 7.739) and QALYs (7.013 vs 7.018) per individual compared with OC, and it was associated with substantially lower mean costs per patient (£467 vs £583), leading to a positive incremental net benefit. After accounting for the overall uncertainty, the probability of 3-D CTC being cost effective was around 60 %, at typical willingness-to-pay values of £20,000-£30,000 per QALY gained. CONCLUSION: 3-D CTC is a cost-saving and cost-effective option for colonic imaging of symptomatic gastroenterology patients compared with OC

Bayesian models for cost-effectiveness analysis in the presence of structural zero costs

Bayesian modelling for cost-effectiveness data has received much attention in both the health economics and the statistical literature in recent years. Cost-effectiveness data are characterised by a relatively complex structure of relationships linking the suitable measure of clinical benefit (\eg QALYs) and the associated costs. Simplifying assumptions, such as (bivariate) normality of the underlying distributions are usually not granted, particularly for the cost variable, which is characterised by markedly skewed distributions. In addition, individual-level datasets are often characterised by the presence of structural zeros in the cost variable. Hurdle models can be used to account for the presence of excess zeros in a distribution and have been applied in the context of cost data. We extend their application to cost-effectiveness data, defining a full Bayesian model which consists of a selection model for the subjects with null costs, a marginal model for the costs and a conditional model for the measure of effectiveness (conditionally on the observed costs). The model is presented using a working example to describe its main features.Comment: 15 pages, 2 figure

Copulas in finance and insurance

Copulas provide a potential useful modeling tool to represent the dependence structure among variables and to generate joint distributions by combining given marginal distributions. Simulations play a relevant role in finance and insurance. They are used to replicate efficient frontiers or extremal values, to price options, to estimate joint risks, and so on. Using copulas, it is easy to construct and simulate from multivariate distributions based on almost any choice of marginals and any type of dependence structure. In this paper we outline recent contributions of statistical modeling using copulas in finance and insurance. We review issues related to the notion of copulas, copula families, copula-based dynamic and static dependence structure, copulas and latent factor models and simulation of copulas. Finally, we outline hot topics in copulas with a special focus on model selection and goodness-of-fit testing

The Hyperdimensional Transform for Distributional Modelling, Regression and Classification

Hyperdimensional computing (HDC) is an increasingly popular computing paradigm with immense potential for future intelligent applications. Although the main ideas already took form in the 1990s, HDC recently gained significant attention, especially in the field of machine learning and data science. Next to efficiency, interoperability and explainability, HDC offers attractive properties for generalization as it can be seen as an attempt to combine connectionist ideas from neural networks with symbolic aspects. In recent work, we introduced the hyperdimensional transform, revealing deep theoretical foundations for representing functions and distributions as high-dimensional holographic vectors. Here, we present the power of the hyperdimensional transform to a broad data science audience. We use the hyperdimensional transform as a theoretical basis and provide insight into state-of-the-art HDC approaches for machine learning. We show how existing algorithms can be modified and how this transform can lead to a novel, well-founded toolbox. Next to the standard regression and classification tasks of machine learning, our discussion includes various aspects of statistical modelling, such as representation, learning and deconvolving distributions, sampling, Bayesian inference, and uncertainty estimation

Copulas in finance and insurance

Copulas provide a potential useful modeling tool to represent the dependence structure among variables and to generate joint distributions by combining given marginal distributions. Simulations play a relevant role in finance and insurance. They are used to replicate efficient frontiers or extremal values, to price options, to estimate joint risks, and so on. Using copulas, it is easy to construct and simulate from multivariate distributions based on almost any choice of marginals and any type of dependence structure. In this paper we outline recent contributions of statistical modeling using copulas in finance and insurance. We review issues related to the notion of copulas, copula families, copula-based dynamic and static dependence structure, copulas and latent factor models and simulation of copulas. Finally, we outline hot topics in copulas with a special focus on model selection and goodness-of-fit testing.Dependence structure, Extremal values, Copula modeling, Copula review

Nonlinear tube-fitting for the analysis of anatomical and functional structures

We are concerned with the estimation of the exterior surface and interior summaries of tube-shaped anatomical structures. This interest is motivated by two distinct scientific goals, one dealing with the distribution of HIV microbicide in the colon and the other with measuring degradation in white-matter tracts in the brain. Our problem is posed as the estimation of the support of a distribution in three dimensions from a sample from that distribution, possibly measured with error. We propose a novel tube-fitting algorithm to construct such estimators. Further, we conduct a simulation study to aid in the choice of a key parameter of the algorithm, and we test our algorithm with validation study tailored to the motivating data sets. Finally, we apply the tube-fitting algorithm to a colon image produced by single photon emission computed tomography (SPECT) and to a white-matter tract image produced using diffusion tensor imaging (DTI).Comment: Published in at http://dx.doi.org/10.1214/10-AOAS384 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

International Diversification: A Copula Approach

.Diversification; Copula; Correlation Complexity; Downside Risk; Systemic Risk