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Daniel Malone博士生国际化公开课

Appendix II    Syllabus for

Applied Health Technology Assessment

Course Instructor: Daniel C. Malone, Ph.D., R.Ph.

Professor

Course Description:

This course will provide an introduction to methods and techniques for conducting pharmacoeconomic studies.  The material will include decision analysis and cost-effectiveness methods. 

Required Software:

Microsoft Excel

Required Computer:

Students will be expected to bring laptop computers to select class periods to work on assignments/problems. 

Readings

Beck JR, Pauker SG.  The Markov process in medical prognosis.  Medical Decision Making 1983; 3:419-458.

Briggs AH, Weinstein MC, Fenwick EAL, Karnon J, Sculpher MJ, Paltiel.  Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Practices Task Force-6.  Value in Health 2012: 15:835-842.

Caldwell DM, Ades AE, Higgins JPT.  Simultaneous comparison of multiple treatments: combing direct and indirect evidence.  BMJ 2005;331:897-900.

Cairo JJ, Briggs AH, Siebert U, Kuntz KM.  Good research practices- overview: a report of the ISPOR-SMDM Modeling Good Practices Task Force-1.  Value in Health 2012: 15:796-803.

Eddy DM, Hollingworth W, Cairo JJ, Tsevat J. McDonald KM, Wong JB.  Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Practices Task Force-7.  Value in Health 2012: 15:843-850.

Elliot WJ, Meyer PM. Incident diabetes of clinical trials of antihypertensive drugs: a network meta-analysis. Lancet 2007: 369:201-07.

Fryback DG, Stout NK, Rosenburg MA.  An elementary introduction to Bayesian computing using WinBugs.  International Journal of Technology Assessment in Health Care 2001;17:98-113.

Husereau et al. Consolidated health economic evaluation reporting standards (CHEERS) – explanation and elaboration: a report of the ISPOR health economic evaluation publication guidelines good reporting practices task force. Value in Health 2013; 16:213-250.

Krahn MD, Naglie G, Naimark D, et al.  Primer on medical decision analysis: part 4 – analyzing the model and interpreting the results.  Medical Decision Making 1997; 17:142-151.

Naglie G, Krahn MD, Naimark D, et al. Primer on medical decision analysis: part 3 – estimating probabilities and utilities.  Medical Decision Making 1997; 17:136-141.

Naimark D, Krahn MD, Naglie G, et al.  Primer on medical decision analysis: Part 5- working with Markov processes.  Medical Decision Making 1997; 17:152-159.

Pitman R, Fisman D, Zaric GS, Postma M, Kretzschmar M, Edmunds J, Brisson M.  Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Practices Task Force-5.  Value in Health 2012: 15:828-834.

Roberts M, Russell LB, Paltiel AD, et al.  Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Practices Task Force-2.  Value in Health 2012: 15:804-811

Song F, Altman DG, Glenny AM, Deeks JJ. Validity of indirect comparisons for estimating efficacy of competing interventions: empirical evidence from published meta-analyses. BMJ. 2003; 326: 472.

Sonnenberg FA, Beck JR.  Markov models in medical decision making: a practical guide.  Medical Decision Making 1993; 13:332-338.

  

Spiegelhalter DJ,Myles JP, Jones DR, Abrams KR. Methods in health service research. An introduction to bayesian methods in health technology assessment. BMJ 1999 Aug 21;319(7208):508-12.

Course Objectives:

  1. Understanding basic concepts of meta-analysis, decision      analysis, and Markov analysis.

  2. Be able to construct a simple and Markov cost-effectiveness      analysis using a decision model framework.

  3. Run a micro-simulation to generate results for a      cost-effectiveness analysis

  4. Be able to construct a probabilistic decision analysis in      Excel.


 Topic

Readings

Class   session

Introduction to Decision   Analysis

Beck JR, Pauker SG.  The Markov process in medical   prognosis.  Medical Decision Making   1983; 3:419-458.

  

Cairo JJ, Briggs AH,   Siebert U, Kuntz KM.  Good research   practices- overview: a report of the ISPOR-SMDM Modeling Good Practices Task   Force-1.  Value in Health 2012:   15:796-803.

  

Naglie G, Krahn MD, Naimark   D, et al. Primer on medical decision analysis: part 3 – estimating   probabilities and utilities.  Medical   Decision Making 1997; 17:136-141.

  

Roberts M, Russell LB,   Paltiel AD, et al.  Conceptualizing a   model: a report of the ISPOR-SMDM Modeling Good Practices Task Force-2.  Value in Health 2012: 15:804-811

  

  

1

  

Markov modeling

Naimark D, Krahn MD, Naglie   G, et al.  Primer on medical decision   analysis: Part 5- working with Markov processes.  Medical Decision Making 1997; 17:152-159.

  

Sonnenberg FA, Beck   JR.  Markov models in medical decision   making: a practical guide.  Medical   Decision Making 1993; 13:332-338.

  

2

Probabilistic decision   models

Briggs AH, Weinstein MC,   Fenwick EAL, Karnon J, Sculpher MJ, Paltiel.    Model parameter estimation and uncertainty: a report of the ISPOR-SMDM   Modeling Good Practices Task Force-6.    Value in Health 2012: 15:835-842.

  

Pitman R, Fisman D, Zaric   GS, Postma M, Kretzschmar M, Edmunds J, Brisson M.  Dynamic transmission modeling: a report of   the ISPOR-SMDM Modeling Good Practices Task Force-5.  Value in Health 2012: 15:828-834.

  

3

Probability estimation from   meta-analyses

Elliot WJ, Meyer PM.   Incident diabetes of clinical trials of antihypertensive drugs: a network   meta-analysis. Lancet 2007: 369:201-07.

  

4

Presenting   Cost-effectiveness Data



   Eddy DM, Hollingworth W, Cairo JJ, Tsevat J. McDonald KM, Wong JB.  Model transparency and validation: a report   of the ISPOR-SMDM Modeling Good Practices Task Force-7.  Value in Health 2012: 15:843-850.

  

  

  

5

Indirect treatment comparisons   using Excel

  

Caldwell DM, Ades AE,   Higgins JPT.  Simultaneous comparison   of multiple treatments: combing direct and indirect evidence.  BMJ 2005;331:897-900.

  

Song F, Altman DG, Glenny   AM, Deeks JJ. Validity of indirect comparisons for estimating efficacy of   competing interventions: empirical evidence from published meta-analyses. BMJ. 2003; 326: 472.

6

Each class session to last approximately 120 minutes.

Student Assessment Plan

A total of 6 exercises will be provided to students to complete.  Each exercise will illustrate a different concept with respect to cost-effectiveness analysis and health technology assessment.  Students will be graded on the successful completion of each exercise and also class discussions related to the topics and exercises. 

Students will also be asked to critique a cost-effectiveness article with respect to the content covered in the course.  This critique will include both a verbal and written component.  




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