Cost Benefit Analysis: Current Perspectives and Future Directions

Thursday 4th February 2010, 10.30am-4.30pm, Lakeside Conference Centre, The Food and Environment Research Agency, York

This meeting, organised by the British and Irish region of the International Biometric Society and the Statistics and Infomatics team at The Food and Environment Research Agency, aims to bring together researchers in Cost Benet Analysis in health economics and agri-ecological and environmental science, in order to draw out key methodological themes in cost benet analysis and encourage cross fertilisation.

Programme

To register

Pre-registration is ESSENTIAL. To register, email conferences@fera.gsi.gov.uk with the subject heading: Cost Benefit Analysis, and include your name, contact telephone number, whether you are vegetarian (for the lunch menu) and whether you wish to park your car on the site (with car registration please).
Closer to the conference date you will be contacted with the security arrangements for access to the site/conference.
The cut-off for registrations is noon, Friday 29th January.

Cost

BIR members: £20; non-members £40; Students: free.
Payment: post a cheque (payable to Biometric Society) c/o Carol Hill/Nicola Smith, Conference Office, Fera, Sand Hutton, YO41 1LZ, or pay on-the-door by cheque or cash.
The cost includes bu et lunch, tea/co ee plus mid morning and afternoon tea/co ee. Parking on site is free.

Directions

The Lakeside Conference Centre at the Food and Environment Agency is a lovely setting for meetings and is easily accessible by road and rail - see www.fera.defra.gov.uk/contactUs/howToFindUs.cfm. For further details of our facilities go to a www.fera.defra.gov.uk/conferenceCentre. Follow the link to `Accommodation' for a list of discounted hotels - you may need to mention that Fera used to be CSL until 1 April 2009 to obtain the discounted rate.

Abstracts

Graham Smith (Food and Environment Research Agency, York) Costs and Bene ts of culling Badgers for control of bovine TB

Bovine tuberculosis (TB) is an important economic livestock disease in the UK and elsewhere. Badgers (Meles meles) are the wildlife source implicated in many cattle outbreaks of TB in Britain, and extensive badger control is a controversial option to reduce the disease. A stochastic spatial badger and cattle population model will be described that simulates badger and cattle population dynamics and TB epidemiology including post- cull social perturbation. The economics of each action (farm restriction, badger management etc.) is included within the model, so the model is capable of finding not only the most effective management scenarios, but also the partial cost- benefit of each. All direct monetary costs are included, but the economics does not account for the conservation value of badgers, the willingness to pay not to have particular badger management, or other indirect costs. I will also describe the basics of badger perturbation and how this interacts with the economics benefits that could be obtained.

Glyn Jones (ADAS Ltd) Cost Benefit Analysis and the Evaluation of Agri-Environment Schemes

Agri-environment policies commonly have multiple policy objectives relating to landscape, biodiversity, climate change and so on. They may involve prescriptive management practices or taking land out of productive use. Such policies can be regulatory (e.g Cross Compliance) or voluntary (Environmental Stewardship) and they can be evaluated both ex-ante and ex-poste. This presentation will outline the role of CBA in evalutaing such policies using recent examples to highlight the strengths and weaknesses.

Andrea Manca (Centre for Health Economics, York University) Cost benefit analysis: why? which? what? - a NICE example

The National Institute for Health and Clinical Excellence (NICE) provides guidance to the NHS in England and Wales on the clinical and cost effectiveness of selected new and established technologies. This process is informed by a formal assessment of the available clinical and economic evidence base, resulting in a technical report which is used to support the work of the appraisal committee at NICE.

This talk focuses on the assessment element of the decision making process, describing the process of gathering, structuring and evaluating the scientific evidence needed to inform NICE decisions.

I begin presenting the rationale which underpins the use of cost-effectiveness analysis (CEA) in the NHS for England and Wales (i.e why). The focus will then shift towards the statistical modelling requirements that CEAs submitted to NICE should meet (i.e. which). Finally, using one or more real life examples I shall illustrate some statistical challenges faced by health economists and modellers when working with an often heterogeneous and fragmented evidence base (i.e. what).

Richard Grieve (London School of Hygiene & Tropical Medicine) Developing statistical methods in cost effectiveness analysis of health care interventions: where should we focus our energy

Health policy-makers use cost-effectiveness analysis (CEA) to decide which health care interventions should be provided. For CEA to provide accurate results, it is essential that they use appropriate statistical methods. Much recent progress has been made in developing appropriate methods for the analysis of randomised controlled trials where randomisation is at the level of the individual. However, less attention has been given to analytical methods for CEA that use either cluster randomised trials (CRT) or non randomised studies. This paper discusses new research comparing statistical methods for CEA that use these alternative study designs. In particular, the paper outlines alternative methods for CEA based on CRT that can handle both the correlation between costs and effects, and the clustered nature of the data. These methods include Bayesian bivariate hierarchical models (BHM), bivariate generalised estimating equations (GEEs), and the 2 stage non-parametric bootstrap. The methods are illustrated with a case study of high policy relevance. Finally, the paper identifies opportunities for further methodological research in statistical methods for health economic evaluations.