Statistical Methods in Economic Evaluation for HTA: Foundations

Manca, A. (Chair)
Griffin, S. (Member of programme committee)
Lomas, J. R. S. (Member of programme committee)

Centre for Health Economics

Activity: Participating in or organising an event › Seminar/workshop/course

Description

Short course

Period	2017
Event type	Course
Location	York, United KingdomShow on map
Degree of Recognition	International

Documents & Links

Overview This is a two-day foundations course designed for those wishing to develop an introductory understanding of the fundamental statistical concepts used in economic evaluation for Health Technology Assessment (HTA). The course includes a mixture of taught modules and practical exercises, where participants will learn the relevant statistical concepts and their estimation using the statistical software package Stata®. Although Stata® will be used as a vehicle to demonstrate a variety of statistical concepts in HTA, no prior knowledge of Stata® is required to be able to complete these practical exercises. Teaching methods The course includes a mixture of presentations from members of the Faculty, together with computer-based exercises using Stata®. The course will take place in a computer laboratory within the University of York campus and each participant will have access to a PC with Stata® version 14 installed. Stata® code (do-files) required to complete the exercises will be provided and all exercises will be supported by Faculty and a group of tutors. Participants are expected to have a basic familiarity with the concepts of cost-effectiveness analysis. Objectives By the end of the course, participants will be able to: - understand the key statistical concepts relevant to economic evaluation for HTA. - manipulate existing evidence to estimate statistical parameters, relevant to economic evaluation. - produce descriptive statistics, tabulation, and correlations from patient-level data using Stata® to derive: (i) inputs for models and (ii) CEA results. - understand good practice in the reporting of cost-effectiveness results to decision makers and recognise the analytical issues involved when dealing with patient-level data. - appreciate the potential for using a regression approach to derive parameter estimates to populate a cost-effectiveness model while controlling for patient characteristics.