Mixed Semi-Continuous Copula Additive Regression Models with Applications in Insurance and Health Care

混合半连续 Copula 加性回归模型在保险和医疗保健中的应用

• 批准号: EP/T033061/1
• 负责人: Rosalba Radice
• 金额: $ 56.12万
• 依托单位: City, University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT033061%2F1
• 关键词: Mixed; Semi; Continuous; Copula; Additive

Insurance and health care data often present a high proportion of zeros with possible skewed positive values. For example, insurance claims and medical expenditures are among those continuous skewed data with a spike at zero. Furthermore, these data are interconnected and researcher might wish to model them jointly. For instance, the interest can be in modelling simultaneously different types of medical expenditure (inpatient and outpatient), health outcomes (quality adjusted life years, QALYs) and costs, and multiple types of coverage provided by the same insurance policy, while accounting for both their dependence and the particular nature of their distributions (high proportion of zeros with positive values). Accounting for both features is indeed critical for many reasons including obtaining unbiased estimates, reliable confidence intervals, providing reliable predictions, to name but a few. Our aim is to propose a powerful, flexible and sophisticated approach that can deal with the nature of these data in an effective and efficient way while producing comprehensible results that are easy to interpret and understand, particularly by risk analysts and health decision makers.

保险和医疗保健数据通常呈现很高比例的零，可能有偏正值。例如，保险索赔和医疗支出是那些连续的倾斜数据，峰值为零。此外，这些数据是相互关联的，研究人员可能希望联合建模。例如，感兴趣的是同时模拟不同类型的医疗支出（住院和门诊），健康结果（质量调整生命年，QHMS）和成本，以及同一保险单提供的多种类型的保险，同时考虑其依赖性及其分布的特殊性质（高比例的零与正值）。考虑到这两个特征确实是至关重要的，原因有很多，包括获得无偏估计，可靠的置信区间，提供可靠的预测，仅举几例。我们的目标是提出一个强大的，灵活的和复杂的方法，可以处理这些数据的性质，在有效和高效的方式，同时产生易于解释和理解的可理解的结果，特别是风险分析师和健康决策者。

项目成果

A Unifying Framework for Flexible Excess Hazard Modeling with Applications in Cancer Epidemiology

灵活的过度危险建模的统一框架及其在癌症流行病学中的应用

• DOI: 10.48550/arxiv.2204.05178
• 发表时间: 2022
• 作者: Eletti A

A bivariate relative poverty line for leisure time and income poverty: Detecting intersectional differences using distributional copulas

休闲时间和收入贫困的双变量相对贫困线：使用分布联结函数检测交叉差异

• DOI: 10.1111/roiw.12635
• 发表时间: 2023
• 期刊: Review of Income and Wealth
• 影响因子: 2
• 作者: Dorn F

A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health.

具有伯努利和特威迪边际的灵活联结回归模型，用于估计支出对心理健康的影响。

• DOI: 10.1002/hec.4668
• 发表时间: 2023
• 期刊: Health economics
• 影响因子: 2.1
• 作者: Marra G

A unifying switching regime regression framework with applications in health economics

• DOI: 10.1080/07474938.2023.2255438
• 发表时间: 2023-10
• 期刊: Econometric Reviews
• 影响因子: 1.2
• 作者: G. Marra;Rosalba Radice;David Zimmer

A spline-based framework for the flexible modelling of continuously observed multistate survival processes

基于样条的框架，用于连续观察的多状态生存过程的灵活建模

• DOI: 10.1177/1471082x231176120
• 发表时间: 2023
• 期刊: Statistical Modelling
• 影响因子: 1
• 作者: Eletti A