1,720,987 research outputs found
Dynamic, economic approaches to HTA under uncertainty
No full textA simple, two period framework is used to interpret existing contributions to the literature on decision rules for HTA under uncertainty and to contrast them with a dynamic, economic model solved using backward induction
A Bayesian decision-theoretic model of sequential experimentation with delayed response
No full textWe 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
Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies
No full textWe 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
A Bayesian decision-theoretic model of sequential experimentation with delayed response.
No full textWe solve a Bayesian decision-theoretic model of a sequential experiment in which the real-valued primary end point is observed with delay. The solution yields a unified policy defining the optimal 'do notexperiment'/'fixed sample size experiment'/'sequential experiment' regions as a function of the prior mean. The model can value the expected benefits accruing to study units, the fixed costs of switching from control to treatment, and allows the number of study units to benefit from a stopping decision to fall as the number of study units recruited to the experiment rises. We apply the model to the field of medical statistics, using data from a published trial investigating the clinical- and cost-effectiveness of drug-eluting stents versus bare metal stents. We demonstrate the model’s superiority over alternative trial designs when judged according to the maximisation of the net benefits of the trial, minus sampling costs, and we investigate how the size of the delay determines the optimal choice of trial design. The optimal policy also performs well when judged according to the probability of making the correct selection of health technology
Optimal Decision Rules for HTA Under Uncertainty: a Wider, Dynamic Perspective
No full textWe present a two period framework which combines real option and decision-theoretic approaches to health technology assessment (HTA) under uncertainty. By viewing adoption, treatment and research decisions as a single economic project, we illustrate how their key dimensions affect optimal rules. We consider the results in relation to the existing literature and argue that developments in this direction could contribute substantially to efficiency gains in resource allocation
Late-stage pharmaceutical R&D and pricing policies under two-stage regulation
No full textWe present a model combining the two regulatory stages relevant to the approval of a new health technology: the authorisation of its commercialisation and the insurer's decision about whether to reimburse its cost. We show that the degree of uncertainty concerning the true value of the insurer's maximum willingness to pay for a unit increase in effectiveness has a non-monotonic impact on the optimal price of the innovation, the firm's expected profit and the optimal sample size of the clinical trial. A key result is that there exists a range of values of the uncertainty parameter over which a reduction in uncertainty benefits the firm, the insurer and patients. We consider how different policy parameters may be used as incentive mechanisms, and the incentives to invest in R&D for marginal projects such as those targeting rare diseases. The model is calibrated using data on a new treatment for cystic fibrosis
Authors’ Reply to Garattini and Freemantle: “Value-Based Pricing Alternatives for Personalised Drugs: Implications of Asymmetric Information and Competition”
No full textPersonalised drugs will increase the heterogeneity in patients’ responses to treatment; this opens new scenarios with regard to the relationship between price regulation and listing strategies by the industry. In a static framework, if the effectiveness differential across patients can be observed only by the manufacturer, the main definitions currently proposed for value-based prices lead to the same listing strategy. This is no longer true in a dynamic setting where competition by a new entrant is possible. To predict accurately the implications of alternative pricing policies, it is essential for decision makers to adopt a dynamic perspective that takes the role of competition into account
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
