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DLP: Markov Model Toolkit: Concepts, Assumptions and Examples

DLP: Markov Model Toolkit: Concepts, Assumptions and Examples

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$40.00 - DLP: Markov Model Toolkit: Concepts, Assumptions and Examples - Non-Member Price

$36.00 - DLP: Markov Model Toolkit: Concepts, Assumptions and Examples - Member Price

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Description
Markov Model Toolkit: Concepts, Assumptions and Examples
Faculty: Stephanie R. Earnshaw, PhD and Anita J. Brogan, PhD
 
Module Description:
This course is designed to provide an overview of Markov modeling and its application to assessing the economic value of new and existing health care technologies. The course will first introduce Markov modeling in the context of other model structures and cover the basic elements of a Markov model, including health states, cycle length, transition probabilities, the Markov property, parameter values associated with the health states, discounting, half-cycle correction, and sensitivity analyses. The course will present the mathematical concepts behind evaluating and building a Markov model, describe how to use a Markov model to assess the economic value of a health care technology, and present a full-scale example.
 
Learning Objectives:
Upon completion of the Markov Model Toolkit: Concepts, Assumptions and Examples module, you will be able to:
Determine when it is appropriate to use a Markov approachUnderstand the basic mechanics of Markov models and appropriate sensitivity analysesApply Markov modeling concepts to real-world problemsConstruct a Markov model for assessing the economic value of a health care technology
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