Statistical Methods in Economic Evaluation for HTA - Advanced

Objectives

By the end of the course, participants will be able to:

• Understand the advantages of using more advanced statistical methods to analyse individual-patient level cost-effectiveness data for HTA;
• Use Stata® to apply robust statistical methods for the analysis of different kinds of HEOR data relevant to HTA (e.g. survival, health-related quality of life and costs);
• Gain insight into how to interpret (and critically assess) the output of these analyses and to use this to derive parameters of interest in a cost-effectiveness model (e.g. probabilities, health state utility values, costs);
• Appreciate what methods can be used to analyse data obtained from non-randomised studies and how to apply these in cost-effectiveness analysis;
• Assess their study results, report and present the output of such analyses to policy makers.

Outline programme

Please note that the exact programme is subject to change although the material covered will remain largely the same.

This course uses a simulated, but realistic, patient-level dataset to illustrate the key concepts, which are like building blocks introduced with increasing sophistication. Ultimately the course aims to show students how to analyse these kinds of data to estimate within-study quantities (e.g. differential mean costs) or to derive key input parameters to populate a cost-effectiveness model to inform HTA decisions.

Three-day course

Day one

• Economic evaluation for decision-making: policy context;
• Economic evaluation for decision-making: methodological context
• Introduction to the analysis of experimental (i.e. RCT) and observational data to estimate treatment effects;
• Statistical methods for the analysis of binary and time-to-event outcomes;

Day two 

• Statistical analysis of generic health-related quality of life (HRQoL) data and how to derive health-states utility values
• Developing and applying mapping algorithms to predict EQ-5D from other HRQoL instruments
• Advanced statistical methods for the analysis of cost data and how to quantify health-state costs
• Evening social event: dinner

Day three

• Analysis of observational data to estimate treatment effects, beyond propensity score methods;
• Advanced methods for the analysis of time-to-event data, to derive probability estimates in realistic settings
• Summary and key messages
• Course Ends (approx. 5pm)

Fees

VAT is not payable.  Registration fees are payable in advance of the workshop dates and are fully inclusive of:  

• Tuition
• Lunch
• Course Dinner
• Course materials

Please note, accommodation is not covered within the fees.

Please use SMEEHTAPA to claim the below discount for public or academic sector companies:- 

2025	Public/academic sector	Commercial sector
Advanced Course	£1400	£1900

Please note that this course is being administered by The Continuing for Professional Development Unit who can be contacted on cpd@york.ac.uk.

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Registration

Before you register on these workshops please ensure you have secured the appropriate funding from your organisation, and (if applicable) that you allow yourself plenty of time to apply for any visas you may require to enter the UK, as you may experience some delay in getting these processed.

 

Delegates are responsible for booking their own accommodation and arranging payment directly with the hotel of their choice.

A list of some hotel options in the city will be circulated to all delegates.

For further information about York, please visit the 'Visit York' website.

In addition to the presenters below, tutors from CHE will be involved in all exercises to ensure that there will be sufficient support to maximise participants’ learning experience.

Andrea Manca (course leader)
Andrea is Professor of Health Economics based in the Team for Economic Evaluation and Health Technology Assessment. His research interests include the application of statistical methods for the analysis of cost-effectiveness and health outcomes data, as well as the use of evidence synthesis techniques in economic evaluation to support health care decision making. Andrea has worked in economic evaluations of health technologies in several clinical areas.

James Lomas
James Lomas is a Lecturer in the Department of Economics and Related Studies at the University of York. His research interests encompass the economic evaluation of health care technologies, and the care-related determinants of health more generally. His work with policymakers across the world (NHS England and Improvement, Bill and Melinda Gates Foundation, and the Patented Medicines Pricing Review Board (Canada)) has had a substantial global impact on a range of issues related to economic evaluation and pharmaceutical pricing. In 2016, a paper based on James’s doctoral research regarding the application of econometric methods to health care cost data was awarded the American Society of Health Economists’ inaugural Willard Manning Memorial Award for the best paper in the area of health econometrics.

Mark Sculpher
Mark is Professor of Health Economics and Director of the Centre for Health Economics.. He has worked on numerous applied economic evaluations including interventions in heart disease, cancer, HIV and respiratory disease. His methodological interests are handling uncertainty and decision analytical modelling.

Beth Woods
Beth is a Senior Research Fellow in the Team for Economic Evaluation and Health Technology Assessment (TEEHTA). Beth holds a BA in Economics from the University of Cambridge and an MSc in Economic Evaluation in Healthcare from City University. Prior to joining CHE Beth was a Director in the Health Economics team at Oxford Outcomes, a private consulting firm.

Beth has worked on economic evaluations of a range of technologies, including the application of advanced statistical and decision modelling methods. Beth has also contributed to methods in the field, in particular relating to model structuring in oncology, evaluation of pharmaceutical pricing policy, and evaluation and pricing of technologies to address antimicrobial resistance.

Julia Hatamyar
Julia Hatamyar earned her PhD in Economics from the University of Miami in 2020. Prior to her studies, she worked as a classical pianist, teaching undergraduate keyboard courses at New York University and performing in the Greater New York City area. She also holds a professional certification in Machine Learning.