Optimisation versus Certainty; Developing the Use of Economic Evaluation for Decision-making

This thesis assesses the methods used in economic evaluation, the relationship of economic evaluation to decision-making and investigates the possible limitations of economic evaluation as it is currently used to support policies aimed at maximising population health gain. It then evaluates alternative methods of analysing data from economic evaluations to better inform policy decisions. The hypothesis of this thesis is that a greater use of subgroup analysis in policy decisions could potentially improve the efficiency of allocating scarce health care resources. This study aims to investigate the impact on population health gain and service costeffectiveness of using subgroup analysis within defined parameters to derive and evaluate estimates of effect, and compare it to the more traditional methods of statistical inference. Data from existing large trials are used to calculate cost-effectiveness ratios for the total study population and for subgroups. Total and subgroup estimates of cost-effectiveness are applied to patient populations through simulation, and outcomes predicted on the assumption that treatment decisions are guided by estimates derived from the trial. The distribution of cost-effectiveness ratios based on different rules for `allowing' the use of subgroup analysis results is compared with the distribution of cost-effectiveness ratios based on aggregate analyses. Results show that pre-selected subgroups can provide a stronger likelihood of maximising overall health gain. This thesis argues for optimisation in the use and interpretation of results rather than an over reliance on certainty and the resulting restriction on the use of available data. It concludes that under the scrutiny of a health care system for which the primary goal is health gain maximisation within resource constraints, policy decisions made using the results of subgroup analysis could result in a more efficient allocation of resources.