Resources Repository
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GuidelinesPublication 2012State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Task Force-3
State-transition modeling includes both Markov model cohort simulation as well as individual-based (first-order Monte Carlo) …
State-transition modeling includes both Markov model cohort simulation as well as individual-based (first-order Monte Carlo) microsimulation. These models have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs. Recommendations are made on choice of model type (cohort vs. individual-level model), model…
Microsimulation | Mathematical Models | State-Transition | Health/Medicine -
GuidelinesPublication 2012Dynamic Transmission Modeling: A Report of the ISPOR-SMDM Modeling Task Force-5
This paper reports the consensus-based guidelines on dynamic transmission modeling in health care. The transmissible …
This paper reports the consensus-based guidelines on dynamic transmission modeling in health care. The transmissible nature of communicable diseases is what sets them apart from other diseases modeled by health economists. The probability of a susceptible individual becoming infected at any one point in time (the force of infection) is related to the number of infectious individuals in the population, will change over time, and will feed back into the future force of infection. These…
Dynamic Transmission | Mathematical Models | Dynamic Simulation | Infectious Diseases | Health/Medicine -
GuidelinesPublication 2012Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Task Force-6
This paper discusses methods for the reporting of uncertainty, both in terms of deterministic sensitivity …
This paper discusses methods for the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty. The article describes the process of estimating model inputs, whether these are point estimates or distributions. It also explores the link between parameter uncertainty, decision uncertainty, and value-of-information analysis.…
Value of Information | Mathematical Models | Calibration/Validation | Health/Medicine