Value of Information Analysis in Models to Inform Health Policy

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Christopher H. Jackson ◽  
Gianluca Baio ◽  
Anna Heath ◽  
Mark Strong ◽  
Nicky J. Welton ◽  
...  
Keyword(s):  
Value Of Information ◽  
Evidence Synthesis ◽  
Annual Review ◽  
Publication Date ◽  
Theoretic Approach ◽  
Information Analysis ◽  
Decision Theoretic Approach ◽  
Expected Benefits ◽  
Different Sources ◽  
Broad Overview

Value of information (VoI) is a decision-theoretic approach to estimating the expected benefits from collecting further information of different kinds, in scientific problems based on combining one or more sources of data. VoI methods can assess the sensitivity of models to different sources of uncertainty and help to set priorities for further data collection. They have been widely applied in healthcare policy making, but the ideas are general to a range of evidence synthesis and decision problems. This article gives a broad overview of VoI methods, explaining the principles behind them, the range of problems that can be tackled with them, and how they can be implemented, and discusses the ongoing challenges in the area. Expected final online publication date for the Annual Review of Statistics, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Effects of Causes and Causes of Effects

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
A. Philip Dawid ◽  
Monica Musio
Keyword(s):  
Individual Case ◽  
Causal Link ◽  
Structural Equations ◽  
Potential Outcomes ◽  
Annual Review ◽  
Publication Date ◽  
Theoretic Approach ◽  
Additional Structure ◽  
Decision Theoretic Approach ◽  
Application Volume

We describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention (effects of causes) and studying whether there is a causal link between the observed exposure and outcome in an individual case (causes of effects). For each of these, we introduce and compare various formal frameworks that have been proposed for that purpose, including the decision-theoretic approach, structural equations, structural and stochastic causal models, and potential outcomes. We argue that counterfactual concepts are unnecessary for studying effects of causes but are needed for analyzing causes of effects. They are, however, subject to a degree of arbitrariness, which can be reduced, though not in general eliminated, by taking account of additional structure in the problem. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Minimal Modeling Approaches to Value of Information Analysis for Health Research

2011 ◽  
Vol 31 (6) ◽  
pp. 785-786 ◽  
Author(s):  
David O. Meltzer ◽  
Ties Hoomans ◽  
Jeannette W. Chung ◽  
Anirban Basu
Keyword(s):  
Clinical Trials ◽  
Health Outcomes ◽  
Clinical Research ◽  
Value Of Information ◽  
Clinical Applications ◽  
Information Analysis ◽  
Practical Applications ◽  
Modeling Approaches ◽  
Expected Benefits ◽  
Analytic Models

Value of information (VOI) techniques can provide estimates of the expected benefits from clinical research studies that can inform decisions about the design and priority of those studies. Most VOI studies use decision-analytic models to characterize the uncertainty of the effects of interventions on health outcomes, but the complexity of constructing such models can pose barriers to some practical applications of VOI. However, because some clinical studies can directly characterize uncertainty in health outcomes, it may sometimes be possible to perform VOI analysis with only minimal modeling. This article 1) develops a framework to define and classify minimal modeling approaches to VOI, 2) reviews existing VOI studies that apply minimal modeling approaches, and 3) illustrates and discusses the application of the minimal modeling to two new clinical applications to which the approach appears well suited because clinical trials with comprehensive outcomes provide preliminary estimates of the uncertainty in outcomes. We conclude that minimal modeling approaches to VOI can be readily applied to in some instances to estimate the expected benefits of clinical research.

Dietary and Physiological Effects of Zinc on the Immune System

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Inga Wessels ◽  
Henrike Josephine Fischer ◽  
Lothar Rink
Keyword(s):  
Immune Responses ◽  
Immune Cells ◽  
Immune Cell ◽  
Current Knowledge ◽  
Annual Review ◽  
Publication Date ◽  
Biological Processes ◽  
And Function ◽  
Broad Overview

Evidence for the importance of zinc for all immune cells and for mounting an efficient and balanced immune response to various environmental stressors has been accumulating in recent years. This article describes the role of zinc in fundamental biological processes and summarizes our current knowledge of zinc's effect on hematopoiesis, including differentiation into immune cell subtypes. In addition, the important role of zinc during activation and function of immune cells is detailed and associated with the specific immune responses to bacteria, parasites, and viruses. The association of zinc with autoimmune reactions and cancers as diseases with increased or decreased immune responses is also discussed. This article provides a broad overview of the manifold roles that zinc, or its deficiency, plays in physiology and during various diseases. Consequently, we discuss why zinc supplementation should be considered, especially for people at risk of deficiency. Expected final online publication date for the Annual Review of Nutrition, Volume 41 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Minimal Modeling Approaches to Value of Information Analysis for Health Research

2011 ◽  
Vol 31 (6) ◽  
pp. E1-E22 ◽  
Author(s):  
David O. Meltzer ◽  
Ties Hoomans ◽  
Jeanette W. Chung ◽  
Anirban Basu
Keyword(s):  
Clinical Trials ◽  
Health Outcomes ◽  
Clinical Research ◽  
Value Of Information ◽  
Clinical Applications ◽  
Information Analysis ◽  
Practical Applications ◽  
Modeling Approaches ◽  
Expected Benefits ◽  
Analytic Models

Value of information (VOI) techniques can provide estimates of the expected benefits from clinical research studies that can inform decisions about the design and priority of those studies. Most VOI studies use decision-analytic models to characterize the uncertainty of the effects of interventions on health outcomes, but the complexity of constructing such models can pose barriers to some practical applications of VOI. However, because some clinical studies can directly characterize uncertainty in health outcomes, it may sometimes be possible to perform VOI analysis with only minimal modeling. This article 1) develops a framework to define and classify minimal modeling approaches to VOI, 2) reviews existing VOI studies that apply minimal modeling approaches, and 3) illustrates and discusses the application of the minimal modeling to 2 new clinical applications to which the approach appears well suited because clinical trials with comprehensive outcomes provide preliminary estimates of the uncertainty in outcomes. The authors conclude that minimal modeling approaches to VOI can be readily applied in some instances to estimate the expected benefits of clinical research.

scholarly journals BAYESIAN VALUE-OF-INFORMATION ANALYSIS

2001 ◽  
Vol 17 (1) ◽  
pp. 38-55 ◽  
Author(s):  
Karl Claxton ◽  
Peter J. Neumann ◽  
Sally Araki ◽  
Milton C. Weinstein
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Treatment Strategy ◽  
Value Of Information ◽  
The United States ◽  
Information Value ◽  
Theoretic Approach ◽  
Information Analysis ◽  
Value Of Information Analysis ◽  
Optimal Sample

A framework is presented that distinguishes the conceptually separate decisions of which treatment strategy is optimal from the question of whether more information is required to inform this choice in the future. The authors argue that the choice of treatment strategy should be based on expected utility, and the only valid reason to characterize the uncertainty surrounding outcomes of interest is to establish the value of acquiring additional information. A Bayesian decision theoretic approach is demonstrated through a probabilistic analysis of a published policy model of Alzheimer's disease. The expected value of perfect information is estimated for the decision to adopt a new pharmaceutical for the population of patients with Alzheimer's disease in the United States. This provides an upper bound on the value of additional research. The value of information is also estimated for each of the model inputs. This analysis can focus future research by identifying those parameters where more precise estimates would be most valuable and indicating whether an experimental design would be required. We also discuss how this type of analysis can also be used to design experimental research efficiently (identifying optimal sample size and optimal sample allocation) based on the marginal cost and marginal benefit of sample information. Value-of-information analysis can provide a measure of the expected payoff from proposed research, which can be used to set priorities in research and development. It can also inform an efficient regulatory framework for new healthcare technologies: an analysis of the value of information would define when a claim for a new technology should be deemed substantiated and when evidence should be considered competent and reliable when it is not cost-effective to gather any more information.

Online Learning Algorithms

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Nicolò Cesa-Bianchi ◽  
Francesco Orabona
Keyword(s):  
Online Learning ◽  
Convex Optimization ◽  
Online Algorithms ◽  
Analysis Of Algorithms ◽  
Annual Review ◽  
Publication Date ◽  
Sequential Data ◽  
Theoretic Approach ◽  
Mirror Descent ◽  
Information Theoretic Approach

Online learning is a framework for the design and analysis of algorithms that build predictive models by processing data one at the time. Besides being computationally efficient, online algorithms enjoy theoretical performance guarantees that do not rely on statistical assumptions on the data source. In this review, we describe some of the most important algorithmic ideas behind online learning and explain the main mathematical tools for their analysis. Our reference framework is online convex optimization, a sequential version of convex optimization within which most online algorithms are formulated. More specifically, we provide an in-depth description of online mirror descent and follow the regularized leader, two of the most fundamental algorithms in online learning. As the tuning of parameters is a typically difficult task in sequential data analysis, in the last part of the review we focus on coin-betting, an information-theoretic approach to the design of parameter-free online algorithms with good theoretical guarantees. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Evaluation of inspection features including exposure risk using a value of information analysis

2021 ◽  
Vol 38 (1) ◽  
pp. 36-58
Author(s):  
Domenic Di Francesco ◽  
Marios Chryssanthopoulos ◽  
Michael Havbro Faber ◽  
Ujjwal Bharadwaj
Keyword(s):  
Value Of Information ◽  
Information Analysis ◽  
Exposure Risk ◽  
Value Of Information Analysis ◽  
A Value

Endogenous Retroviruses Drive Resistance and Promotion of Exogenous Retroviral Homologs

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Elliott S. Chiu ◽  
Sue VandeWoude
Keyword(s):  
Immune Modulation ◽  
Viral Infections ◽  
Viral Evolution ◽  
Endogenous Retroviruses ◽  
Annual Review ◽  
Publication Date ◽  
Biological Functions ◽  
Self Tolerance ◽  
Vertebrate Hosts ◽  
Insight Into

Endogenous retroviruses (ERVs) serve as markers of ancient viral infections and provide invaluable insight into host and viral evolution. ERVs have been exapted to assist in performing basic biological functions, including placentation, immune modulation, and oncogenesis. A subset of ERVs share high nucleotide similarity to circulating horizontally transmitted exogenous retrovirus (XRV) progenitors. In these cases, ERV–XRV interactions have been documented and include ( a) recombination to result in ERV–XRV chimeras, ( b) ERV induction of immune self-tolerance to XRV antigens, ( c) ERV antigen interference with XRV receptor binding, and ( d) interactions resulting in both enhancement and restriction of XRV infections. Whereas the mechanisms governing recombination and immune self-tolerance have been partially determined, enhancement and restriction of XRV infection are virus specific and only partially understood. This review summarizes interactions between six unique ERV–XRV pairs, highlighting important ERV biological functions and potential evolutionary histories in vertebrate hosts. Expected final online publication date for the Annual Review of Animal Biosciences, Volume 9 is February 16, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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