Our interim analysis showed that elective repair of subclinical stenosis in AVFs with Qa > 500 mL/min cost-effectively reduces the risk of thrombosis and access loss in comparison with the approach of the Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines, raising the question of whether the currently recommended criteria for assessing and treating stenosis should be reconsidered.

We propose a Bayesian decision theoretic model of a fully sequential experiment in which the real-valued primary end point is observed with delay. The goal is to identify the sequential experiment which maximizes the expected benefits of technology adoption decisions, minus sampling costs. The solution yields a unified policy defining the optimal 'do not experiment'-'fixed sample size experiment'-'sequential experiment' regions and optimal stopping boundaries for sequential sampling, as a function of the prior mean benefit and the size of the delay. We apply the model to the field of medical statistics, using data from published clinical trials.

Managed entry agreements (MEAs) have been used for several years, with the aim of curbing the growth of pharmaceutical expenditure and enhancing patient access to innovation. Yet, much remains to be understood about their economic implications. This paper studies the impact of MEAs on list prices, that is, prices before the deduction of any discount. Using a theoretical model, we show that, under most price setting regimes, the introduction of an MEA leads to a higher list price. This is confirmed by our empirical analysis of a sample of 156 medicines in six countries, providing a conservative estimate of the increase in price due to the MEA of 5.9%. A relevant policy implication is that payers may overestimate the financial gains that can be achieved through this  tool.

We present a Bayes sequential economic evaluation model for health technologies in which an investigator has flexibility over the timing of a decision to stop carrying out research and to conclude that one technology is preferred to another on cost-effectiveness grounds. We implement the model by using an evaluation of the treatment of bacterial sinusitis and derive approximations of the optimal stopping rule as a function of accumulated sample size. We compare the performance of the model with existing frequentist and Bayes sequential designs and investigate the sensitivity of the stopping rule to changes in the parameters of the model. Our results suggest that accounting for the dynamic nature of experimentation, together with its economic parameters, should lead to greater efficiency in resource allocation within healthcare systems.

5We present a model combining the two regulatory stages relevant to the approval of a 6 new health technology: the authorisation of its commercialisation and the insurer's decision 7 about whether to reimburse its cost. We show that the degree of uncertainty concerning the 8 true value of the insurer's maximum willingness to pay for a unit increase in effectiveness 9 has a non-monotonic impact on the optimal price of the innovation, the firm's expected profit 10 and the optimal sample size of the clinical trial. A key result is that there exists a range of 11 values of the uncertainty parameter over which a reduction in uncertainty benefits the firm, 12 the insurer and patients. We consider how different policy parameters may be used as in-