Karl Claxton and colleagues at the University of York have recently published a working paper on Methods for the Estimation of the NICE Cost Effectiveness Threshold. Since a guideline was issued in 2004, NICE has used standard values of £20-30,000 per QALY as the official cost-effectiveness threshold. These are effectively equivalent to the cost per quality adjusted life year gained by investing in a new technology at the expenses of an already existing intervention. Decisions on reimbursements have been based on this decision rule (eg if the cost per QALY exceeded this range of thresholds, then the new intervention was not cost-effective).
This paper tries to produce an updated version of the threshold, based on empirical evidence (eg programme budgeting data for the English NHS). The methodology used is quite complex $-$ the full report is over 400 pages and I have only skimmed through it, reading with some care only some parts. Technically, the analysis is based on an instrumental variables approach within a structural equations setting and aims at simultaneously estimating the impact of the level of investment (and other variables) on health outcomes and the impact the overall budget constraint (and other variables) on the level of spending for a given health programme. Their main result is to suggest a slightly lower value to be used by NICE (£18,317 in some sort of baseline scenario).
As I said, I only skimmed through the report, but I think it looks like a substantial piece of work. Nevertheless, I think there are some major limitation (which, to be fair, the authors acknowledge in the text. The Office for Health Economics has also produced a critique of this paper, which is available here).
The main one, seems to be the (lack of) availability of data for all the different programmes, to be used to translate the impact of expenditure into changes in quality of life). On the other hand, the paper tries to deal carefully with the issue of uncertainty propagation; for example, there is a whole section on the evaluation of structural and parametric uncertainty $-$ although this is not directly based on a full Bayesian model (which is kind of strange, given Karl is the main author on the paper...).