AM1 ANALYZING PERSON-LEVEL COST-EFFECTIVENESS DATA: ESTIMATES AND UNCERTAINTY

Saturday, October 20, 2012: 9:00 AM-12:30 PM
Russell AB (Hyatt Regency)
Course Type: Half Day
Course Level: Beginner

Format Requirements: The class will be structured with theory bursts followed by group work exercises. Participants should have some familiarity with how to do regression as well as how to use Excel since the examples will use Excel and/or regression to illustrate a concept or how to do a technique. In order to get the most from the session and undertake the exercises, participants should bring laptops with Excel to the session. A fundamental understanding of Estimation and Uncertainty concepts learned in a person-level setting can enrich one’s understanding in a decision modeling context. Participants will be challenged to apply their conceptual understanding developed in the theory bursts using Excel and materials given to them in this class. A final objective for this short course is that participants be able to do and explain their CEA techniques and results in an intuitive, non-technical manner.

Background: This class introduces the principles and practices of estimation and uncertainty as applied to person-level cost-effectiveness analysis (CEA). The purpose of this class is to build capacity among SMDM attendees to do and explain CEA in an intuitive, non-technical manner. The fundamental activities of CEA—estimation and uncertainty—will be taught with the first part of the class focusing on estimation and uncertainty skills involving the incremental cost-effectiveness ratio (ICER). The second part of the class will focus on estimation and uncertainty skills involving the incremental net benefit (INB). At the course’s conclusion, participants will be able to make ICER and INB estimates as well as 95% confidence ellipses and cost-effectiveness acceptability curves. Participants will also have a deeper understanding of estimation and uncertainty concepts and the ability to explain them.

Description and Objectives: This class introduces estimation and uncertainty in person-level cost-effectiveness analysis (CEA). This class builds capacity among SMDM attendees to do and explain CEA in an intuitive, non-technical manner. The fundamental activities of CEA—estimation and uncertainty—will be taught with the first part of the class focusing on estimation and uncertainty skills involving the incremental cost-effectiveness ratio (ICER). The second part of the class will focus on estimation and uncertainty skills involving the incremental net benefit (INB). At the course’s conclusion, participants will be able to make ICER and INB estimates as well as 95% confidence ellipses and cost-effectiveness acceptability curves.  Participants will also have a deeper understanding of estimation and uncertainty concepts and the ability to explain them.

The class will be structured with theory bursts followed by group work exercises.  Participants should have some familiarity with how to do regression as well as how to use Excel since the examples will use Excel and/or regression to illustrate a concept or how to do a technique. In order to get the most from the session and undertake the exercises, participants should bring laptops with Excel to the session.

This introductory course provides insight about person-level CEA.  A deeper understanding of person-level CEA is becoming even more important as clinical trials continue to add economic components and countries invest in comparative effectiveness research.  In addition, a fundamental understanding of Estimation and Uncertainty in a person-level setting can enrich one’s understanding in decision modeling.  The objectives of this course are for participants to learn the principles and practice of

  • Estimating and interpreting the ICER and INB
  • Characterizing uncertainty of ICER and INB estimates.

A final objective for this short course is that participants be able to do and explain their CEA techniques and results in an intuitive, non-technical manner.

Course Faculty:
Katia Noyes, PhD, MPH and Ahmed M. Bayoumi, MD, MSc