7 July | Full day
Arne Risa Hole, University of Sheffield, UK
Discrete Choice Modelling using Stata
The aim of this course is to give the participants a solid grounding in the econometric methods used to analyse discrete choices. The lectures will cover the theory behind the methods and give examples of their use in health economics and related fields. The practical work will give participants the opportunity to use the methods to analyse real-world datasets. The intended learning outcome is that by the end of the course the participants will be able to apply the methods in their own research. No prior knowledge of discrete choice methods is assumed. The practical work will be carried out using the Stata statistical software package, and participants are required to bring their own laptop with Stata (version 13 or newer) installed.
7 July | Morning
Bjørn-Atle Reme, National Institute of Public Health, Norway
An introduction to machine learning - with applications in R
This mini-course gives an introduction to the basics of machine learning - what, why and how. An important focus in the course is to discuss its relation to traditional statistical methods, and develop an understanding of its potential and limitations. The course is non-technical, with focus on practical examples coded in R. The course also presents some examples of use in health research.
7 July | Afternoon
Andrew Jones, University of York, UK
Data visualisation and health econometrics
This pre-conference workshop will review econometric methods for health outcomes and health care costs that are used for prediction and forecasting, risk adjustment, resource allocation, technology assessment and policy evaluation. It focuses on the principles and practical application of data visualization and how graphics can enhance applied econometric analysis. Practical examples show how these methods can be applied to data on individual healthcare costs and health outcomes using Stata and Python. Topics include: an introduction to data visualization; data description and regression; generalized linear models; flexible parametric models; semiparametric models; and an application to biomarkers in the UKHLS.
7 July | Afternoon
Luigi Siciliani, University of York, UK
Health economics theory for empirical economists: Modelling the demand for and supply of health services
This course provides an overview of microeconomic models that can be used to investigate the demand for and the supply of health services. The course is aimed at empirical researchers who would like to motivate, guide and complement their empirical analyses by developing a theoretical model.
The module therefore focuses on relatively simple and tractable microeconomic models that can be used to frame an empirical research question. On the demand side, the course will describe models that i) use a reduced-form specification of aggregate demand, ii) adopt a representative consumer (individual, patient) approach, iii) allow for heterogeneity in relevant dimensions (e.g. severity, benefits, distance). Factors affecting demand will include services, quality, waiting times, and co-payments.
On the supply side, the course will give an overview of theoretical models that describe provider incentives towards quantity, quality and costs under a range of assumptions (altruistic concerns, capacity constraints, non-profit status) and payment systems (e.g. fixed price regulation/capitation, and pay for performance) under different specifications of the demand function, as described above. The focus will be on large organisations, such as hospitals, and to a lower extent primary care providers.
By the end of module, the student should have a clearer idea on how to set-up their own theoretical model that matches their empirical analysis, and an idea of key modelling strategies and trade-offs.