Methodological issues in the analysis of individual- and aggregate-participant level data for cost effectiveness analysis

Research output: ThesisDoctoral Thesis


Health care economic evaluations assess the costs and consequences of competing interventions, programmes or services. Such assessments use a decision model, with parameters informed by available evidence. Evidence, however, is rarely derived from a single source, in which case researchers are expected to combine information on multiple sources. This thesis contributes to the methodological debate on the use of evidence, particularly, the use of individual level data (IPD), for cost effectiveness analysis.
This thesis defines a taxonomy which summarises the methodological and analytical issues in the use and synthesis of evidence for cost effectiveness modelling. For alternative parameter types (e.g. relative effectiveness, costs) the taxonomy offers guidance on appropriate synthesis methodologies to use and identifies areas where further methodological contributions are needed. The thesis also explores methods of synthesis of IPD and develops novel frameworks which allow both IPD and AD to be jointly modelled, specifically in estimating relative effectiveness. The use of IPD from studies is found desirable, particularly when the estimation of subgroup effects is of interest.
An applied decision model of the cost effectiveness of smoke alarm equipment in households with pre-school children is developed within this thesis. This application offers a means to evaluate the impact of using IPD on the cost effectiveness outcomes, compared to the use of AD. The thesis examines the advantages of having access to IPD when quantifying decision uncertainty. Additionally, it discusses the use of IPD in estimating the value of further research. Specifically, a framework is used which allows considering population subgroups. It is argued that the use of IPD allows a more suitable characterisation of decision uncertainty, appropriately allowing for subgroup value of information analysis.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of York
Award date1 Dec 2012
Publication statusPublished - 2012

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