Bayesian approaches to technology assessment and decision making

Until the mid-1980s, most economic analyses of healthcare technologies were based on decision theory and used decision-analytic models. The goal was to synthesize all relevant clinical and economic evidence for the purpose of assisting decision makers to efficiently allocate society's scarce resources. This was true of virtually all the early cost-effectiveness evaluations sponsored and/or published by the U.S. Congressional Office of Technology Assessment (OTA) (15), Centers of Disease Control and Prevention (CDC), the National Cancer Institute, other elements of the U.S. Public Health Service, and of healthcare technology assessors in Europe and elsewhere around the world. Methodologists routinely espoused, or at minimum assumed, that these economic analyses were based on decision theory (8;24;25). Since decision theory is rooted in—in fact, an informal application of—Bayesian statistical theory, these analysts were conducting studies to assist healthcare decision making by appealing to a Bayesian rather than a classical, or frequentist, inference approach. But their efforts were not so labeled. Oddly, the statistical training of these decision analysts was invariably classical, not Bayesian. Many were not—and still are not—conversant with Bayesian statistical approaches

Likelihood decision functions

In both classical and Bayesian approaches, statistical inference is unified and generalized by the corresponding decision theory. This is not the case for the likelihood approach to statistical inference, in spite of the manifest success of the likelihood methods in statistics. The goal of the present work is to fill this gap, by extending the likelihood approach in order to cover decision making as well. The resulting decision functions, called likelihood decision functions, generalize the usual likelihood methods (such as ML estimators and LR tests), in the sense that these methods appear as the likelihood decision functions in particular decision problems. In general, the likelihood decision functions maintain some key properties of the usual likelihood methods, such as equivariance and asymptotic optimality. By unifying and generalizing the likelihood approach to statistical inference, the present work offers a new perspective on statistical methodology and on the connections among likelihood methods

Statistical Decision Theory for Sensor Fusion

This article is a brief introduction to statistical decision theory. It provides background for understanding the research problems in decision theory motivated by the sensor-fusion problem

Research on new techniques for the analysis of manual control systems Progress report, 15 Jun. 1969 - 15 Jun. 1970

Applying statistical decision theory to manual adaptive control system

Statistical decision problems in large scale biological experiments Final report

Statistical decision theory applied to problems associated with Martian biological exploration progra

Robustness

The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted? Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics. Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.decision-making, uncertainty, statistical models, control techniques, economic modeling, dynamic microeconomics, misspecification

The Sequencing Problem in Sequential Investigation Processes

Many decision problems in various fields of application can be characterized as diagnostic problems trying to assess the true state (of the world) of given cases. The investigation of assessment criteria improves the initial information according to observed signal outcomes, which are related to the possible states. Such sequential investigation processes can be analyzed within the framework of statistical decision theory, in which prior probability distributions of classes of cases are updated, allowing for a sorting of particular cases into ever smaller subclasses. However, receiving such information causes investigation costs. Besides the question about the set of relevant criteria, this defines two additional problems of statistical decision problems: the optimal stopping of investigations and the optimal sequence of investigating a given set of criteria. Unfortunately, no solution exists with which the optimal sequence can generally be determined. Therefore, the paper characterizes the associated problems and analyzes existing heuristics trying to approximate an optimal solution.Decision-Making, Uncertainty, Information, Bayesian Analysis, Statistical Decision Theory

Continuation of studies in statistical decision theory in large scale biological experiments Final report, 1 May 1965 - 31 Jul. 1966

Statistical decision theory applied to Martian atmosphere analysis, life detection experiments, and gas chromatogram measurements of n-alkane distributions in material