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Value of sample information in dynamic, structurally uncertain resource systems.

ABSTRACT: Few if any natural resource systems are completely understood and fully observed. Instead, there almost always is uncertainty about the way a system works and its status at any given time, which can limit effective management. A natural approach to uncertainty is to allocate time and effort to the collection of additional data, on the reasonable assumption that more information will facilitate better understanding and lead to better management. But the collection of more data, either through observation or investigation, requires time and effort that often can be put to other conservation activities. An important question is whether the use of limited resources to improve understanding is justified by the resulting potential for improved management. In this paper we address directly a change in value from new information collected through investigation. We frame the value of information in terms of learning through the management process itself, as well as learning through investigations that are external to the management process but add to our base of understanding. We provide a conceptual framework and metrics for this issue, and illustrate them with examples involving Florida scrub-jays (Aphelocoma coerulescens).

SUBMITTER: Williams BK

PROVIDER: S-EPMC6025880 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Value of sample information in dynamic, structurally uncertain resource systems.

Williams Byron K BK Johnson Fred A FA

PloS one 20180629 6

Few if any natural resource systems are completely understood and fully observed. Instead, there almost always is uncertainty about the way a system works and its status at any given time, which can limit effective management. A natural approach to uncertainty is to allocate time and effort to the collection of additional data, on the reasonable assumption that more information will facilitate better understanding and lead to better management. But the collection of more data, either through obs ...[more]

PMID: 29958290

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Project description:ObjectiveThis systematic review aimed to: (1) determine the prevalence and scope of existing research on human resource information systems (HRIS) in health organizations; (2) analyze, classify, and synthesize evidence on the processes and impacts of HRIS development, implementation, and adoption; and (3) generate recommendations for HRIS research, practice, and policy, with reference to the needs of different stakeholders.MethodsA structured search strategy was used to interrogate 10 electronic databases indexing research from the health, social, management, technology, and interdisciplinary sciences, alongside gray literature sources and reference lists of qualifying studies. There were no restrictions on language or publication year. Two reviewers screened publications, extracted data, and coded findings according to the innovation stages covered in the studies. The Critical Appraisal Skills Program checklist was adopted to assess study quality. The process of study selection was charted using a Preferred Items for Systematic Reviews and Meta-Analysis (PRISMA) diagram.ResultsOf the 6824 publications identified by the search strategy, 68, covering 42 studies, were included for final analysis. Research on HRIS in health was interdisciplinary, often atheoretical, conducted primarily in the hospital sector of high-income economies, and largely focused uncritically on use and realized benefits.Discussion and conclusionsWhile studies of HRIS in health exist, the overall lack of evaluative research raises unanswered questions about their capacity to improve quality and efficiency and enable learning health systems, as well as how sociotechnical complexity influences implementation and effectiveness. We offer this analysis to decision makers and managers considering or currently implementing an HRIS, and make recommendations for further research.Trial registrationInternational Prospective Register of Systematic Reviews (PROSPERO): CRD42015023581. http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42015023581#.VYu1BPlVjDU .

Project description:IntroductionAdaptive designs allow changes to an ongoing trial based on prespecified early examinations of accrued data. Opportunities are potentially being missed to incorporate health economic considerations into the design of these studies.MethodsWe describe how to estimate the expected value of sample information for group sequential design adaptive trials. We operationalize this approach in a hypothetical case study using data from a pilot trial. We report the expected value of sample information and expected net benefit of sampling results for 5 design options for the future full-scale trial including the fixed-sample-size design and the group sequential design using either the Pocock stopping rule or the O'Brien-Fleming stopping rule with 2 or 5 analyses. We considered 2 scenarios relating to 1) using the cost-effectiveness model with a traditional approach to the health economic analysis and 2) adjusting the cost-effectiveness analysis to incorporate the bias-adjusted maximum likelihood estimates of trial outcomes to account for the bias that can be generated in adaptive trials.ResultsThe case study demonstrated that the methods developed could be successfully applied in practice. The results showed that the O'Brien-Fleming stopping rule with 2 analyses was the most efficient design with the highest expected net benefit of sampling in the case study.ConclusionsCost-effectiveness considerations are unavoidable in budget-constrained, publicly funded health care systems, and adaptive designs can provide an alternative to costly fixed-sample-size designs. We recommend that when planning a clinical trial, expected value of sample information methods be used to compare possible adaptive and nonadaptive trial designs, with appropriate adjustment, to help justify the choice of design characteristics and ensure the cost-effective use of research funding.HighlightsOpportunities are potentially being missed to incorporate health economic considerations into the design of adaptive clinical trials.Existing expected value of sample information analysis methods can be extended to compare possible group sequential and nonadaptive trial designs when planning a clinical trial.We recommend that adjusted analyses be presented to control for the potential impact of the adaptive designs and to maintain the accuracy of the calculations.This approach can help to justify the choice of design characteristics and ensure the cost-effective use of limited research funding.

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Value of sample information in dynamic, structurally uncertain resource systems.

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Value of sample information in dynamic, structurally uncertain resource systems.

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