Health economic models simulate the costs and effects of health technologies for use in health technology assessment (HTA) to inform efficient use of scarce resources. Models have historically been developed using spreadsheet software (e.g., Microsoft Excel) and while use of R is growing, general purpose modeling software is still limited. hesim is an R package that helps fill this gap by facilitating parameterization, simulation, and analysis of economic models in an integrated manner. Supported model types include cohort discrete time state transition models (cDTSTMs), individual continuous time state transition models (iCTSTMs), and partitioned survival models (PSMs), encompassing Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. A modular design based on R6 and S3 classes allows users to combine separate submodels for disease progression, costs, and utility in a flexible way. Probabilistic sensitivity analysis (PSA) is used to propagate uncertainty in model parameters to model outputs. Simulation code is written in C++ so complex simulations such as those combining PSA and individual simulation can be run much more quickly than previously possible. Decision analysis within a cost-effectiveness framework is performed using simulated costs and quality-adjusted life years (QALYs) from a PSA.
Data Integration and spatio temporal statistics can quantify relative risk of medico-legal reforms: the example of police emergency mental health responses in Queensland (Australia)
Curtin University（科廷大学）；Australian Institute of Tropical Health and Medicine, James Cook University（詹姆斯库恩大学澳大利亚热带健康与医学研究所）；Cairns Hospital（凯恩斯医院）
Learning to Address Health Inequality in the United States with a Bayesian Decision Network
Stanford University（斯坦福大学）；Indraprastha Institute of Information Technology, Delhi（德里英德拉普拉斯塔信息技术学院）；Netaji Subhas University of Technology, Delhi（德里内塔吉·苏巴斯科技大学）
