How can health economics be used in the design and analysis of adaptive clinical trials? A qualitative analysis

Introduction Adaptive designs offer a flexible approach, allowing changes to a trial based on examinations of the data as it progresses. Adaptive clinical trials are becoming a popular choice, as the prudent use of finite research budgets and accurate decision-making are priorities for healthcare providers around the world. The methods of health economics, which aim to maximise the health gained for money spent, could be incorporated into the design and analysis of adaptive clinical trials to make them more efficient. We aimed to understand the perspectives of stakeholders in health technology assessments to inform recommendations for the use of health economics in adaptive clinical trials. Methods A qualitative study explored the attitudes of key stakeholders—including researchers, decision-makers and members of the public—towards the use of health economics in the design and analysis of adaptive clinical trials. Data were collected using interviews and focus groups (29 participants). A framework analysis was used to identify themes in the transcripts. Results It was considered that answering the clinical research question should be the priority in a clinical trial, notwithstanding the importance of cost-effectiveness for decision-making. Concerns raised by participants included handling the volatile nature of cost data at interim analyses; implementing this approach in global trials; resourcing adaptive trials which are designed and adapted based on health economic outcomes; and training stakeholders in these methods so that they can be implemented and appropriately interpreted. Conclusion The use of health economics in the design and analysis of adaptive clinical trials has the potential to increase the efficiency of health technology assessments worldwide. Recommendations are made concerning the development of methods allowing the use of health economics in adaptive clinical trials, and suggestions are given to facilitate their implementation in practice

A review of clinical trials with an adaptive design and health economic analysis

An adaptive design uses data collected as a clinical trial progresses to inform modifications to the trial. Hence, adaptive designs and health economics aim to facilitate efficient and accurate decision-making. However, it is unclear whether the methods are considered together in the design, analysis and reporting of trials. This review aims to establish how health economic outcomes are utilised in the design, analysis and reporting of adaptive designs. Registered and published trials up to August 2016 with an adaptive design and health economic analysis were identified. The use of health economics in the design, analysis and reporting was assessed. Summary statistics are presented and recommendations formed based on the research team’s experiences and a practical interpretation of the results. Thirty-seven trials with an adaptive design and health economic analysis were identified. It was not clear whether the health economic analysis accounted for the adaptive design in 17/37 trials where this was thought necessary, nor whether health economic outcomes were utilised at the interim analysis for 18/19 of trials with results. The reporting of health economic results was sub-optimal for the (17/19) trials with published results. Appropriate consideration is rarely given to the health economic analysis of adaptive designs. Opportunities to utilise health economic outcomes in the design and analysis of adaptive trials are being missed. Further work is needed to establish whether adaptive designs and health economic analyses can be used together to increase the efficiency of health technology assessments without compromising accuracy

Discussion of Preston, "Learning about monetary policy rules when long-horizon expectations matter"

The design of interest rate rules for conducting monetary policy have recently been examined for two key concerns. The first issue is determinacy of equilibria. Indeterminacy (multiplicity of stationary rational expectations equilibria) is a concern in models of monopolistic competition and price stickiness are currently a popular framework for the study of monetary policy. The second issue is stability of equilibria under adaptive learning. Some interest rate rules do not perform well when the expectations of the agents get out of equilibrium, e.g. as a result of structural shifts.Equilibrium (Economics) ; Monetary policy ; Macroeconomics

Distributed Computing with Adaptive Heuristics

We use ideas from distributed computing to study dynamic environments in which computational nodes, or decision makers, follow adaptive heuristics (Hart 2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly "best replying" to others' actions, and minimizing "regret", that have been extensively studied in game theory and economics. We explore when convergence of such simple dynamics to an equilibrium is guaranteed in asynchronous computational environments, where nodes can act at any time. Our research agenda, distributed computing with adaptive heuristics, lies on the borderline of computer science (including distributed computing and learning) and game theory (including game dynamics and adaptive heuristics). We exhibit a general non-termination result for a broad class of heuristics with bounded recall---that is, simple rules of behavior that depend only on recent history of interaction between nodes. We consider implications of our result across a wide variety of interesting and timely applications: game theory, circuit design, social networks, routing and congestion control. We also study the computational and communication complexity of asynchronous dynamics and present some basic observations regarding the effects of asynchrony on no-regret dynamics. We believe that our work opens a new avenue for research in both distributed computing and game theory.Comment: 36 pages, four figures. Expands both technical results and discussion of v1. Revised version will appear in the proceedings of Innovations in Computer Science 201

Adaptive Policymaking: Evolving and Applying Emergent Solutions for U.S. Communications Policy

This Article presents some specific ways that U.S. policymakers should use teachings from the latest thinking in economics to create a conceptual framework in order to grapple with current controversies in communications law and regulation. First, it provides a brief overview of Emergence Economics, with an emphasis on the rough formula of emergence and the unique role of technological change in creating and furthering innovation and economic growth. Second, this paper explicates the general concept of Adaptive Policymaking by governments and includes some proposed guiding principles, an outline of the public policy design space, and an adaptive toolkit to be used by policymakers. Third, this Article discusses devising a policy design space specifically for communications policy, with an emphasis on the institutional and organizational challenges facing the FCC as it seeks to fulfill the suggested goal of furthering More Good Ideas. Finally, this paper explores the conceptual framework for the fitness landscape, including a searching critique of the notion of enabling without dictating evolutionary forces in the marketplace

DOING POLICY IN THE LAB! OPTIONS FOR THE FUTURE USE OF MODEL-BASED POLICY ANALYSIS FOR COMPLEX DECISION-MAKING

For models to have an impact on policy-making, they need to be used. Exploring the relationships between policy models, model uptake and policy dynamics is the core of this article. What particular role can policy models play in the analysis and design of policies? Which factors facilitate (inhibit) the uptake of models by policy-makers? What are possible pathways to further develop modelling approaches to better meet the challenges facing agriculture today? In this paper, we address these issues from three different points of view, each of which should shed some light on the subject. The first point of view discusses models in the framework of complex adaptive systems and uncertainty. The second point of view looks at the dynamic interplay between policies and models using the example of modelling in agricultural economics. The third point of view addresses conditions for a successful application of models in policy analysis.modelling, complexity, participatory modelling, policy analysis, model use, Agricultural and Food Policy, Research Methods/ Statistical Methods,

Global adaptation in networks of selfish components: emergent associative memory at the system scale

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning

Information systems for adaptive shariah compliant financial services: defining adaptation constructs

Asymmetry of information in financial service creates excessive uncertainty termed gharar, which makes a financial transaction invalid (haram) in Islamic Law (Shariah). Information systems customised to shariah compliant financial service (SCFS) can make information flow more symmetric and can in turn reduce gharar. Based on information related to emergent SCFS design stakeholders i.e. financial regulators, bankers and customers make adaptation and migration decisions. However, unique nature of SCFS design requires adaptation (migration) of emergent SCFS in compliance to shariah. We discuss general service and SCFS literatures to define structural constructs of SCFS. We then discuss qiyas, which is the juridical principle of defining emergence for expansion in shariah rulings, and theory of deferred action, which is a design adaptability theory drawing in complexity. The adaptation construct for SCFS designs is defined and discussed in the joint framework of qiyas and theory of deferred action