Dimensions of design space: a decision-theoretic approach to optimal research design.
Bayesian decision theory can be used not only to establish the optimal sample size and its allocation in a single clinical study but also to identify an optimal portfolio of research combining different types of STUDY DESIGN: Within a single study, the highest societal payoff to proposed research is...
| Publicado en: | Medical Decision Making Vol. 29; no. 6; pp. 643 - 661 |
|---|---|
| Autores principales: | , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Nov/Dec2009
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105259170&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105259170 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0272989X DKI jtl: Medical Decision Making issn: 0272989X maglogo: Y pubinfo: dt: Nov/Dec2009 vid: 29 iid: 6 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 105259170 45691395 2010497979 10.1177/0272989X09336142 NLM19605884 105259170 ppf: 643 ppct: 18 formats: tig: atl: Dimensions of design space: a decision-theoretic approach to optimal research design. aug: au: Conti S Claxton K affil: Centre for Health Economics, University of York, York, UK; Stefano.Conti@hpa.org.uk. sug: subj: Decision Making, Clinical Study Design Cost Benefit Analysis Decision Trees Quality of Life Evaluation Research Methodology ab: Bayesian decision theory can be used not only to establish the optimal sample size and its allocation in a single clinical study but also to identify an optimal portfolio of research combining different types of STUDY DESIGN: Within a single study, the highest societal payoff to proposed research is achieved when its sample sizes and allocation between available treatment options are chosen to maximize the expected net benefit of sampling (ENBS). Where a number of different types of study informing different parameters in the decision problem could be conducted, the simultaneous estimation of ENBS across all dimensions of the design space is required to identify the optimal sample sizes and allocations within such a research portfolio. This is illustrated through a simple example of a decision model of zanamivir for the treatment of influenza. The possible study designs include: 1) a single trial of all the parameters, 2) a clinical trial providing evidence only on clinical endpoints, 3) an epidemiological study of natural history of disease, and 4) a survey of quality of life. The possible combinations, samples sizes, and allocation between trial arms are evaluated over a range of cost-effectiveness thresholds. The computational challenges are addressed by implementing optimization algorithms to search the ENBS surface more efficiently over such large dimensions. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|