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Populating decision-analytic models: The feasibility and efficiency of database searching for individual parameters

Published online by Cambridge University Press:  04 August 2005

Su Golder ,
Julie Glanville  and
Laura Ginnelly
Su Golder
Affiliation:
University of York
Julie Glanville
Affiliation:
University of York
Laura Ginnelly
Affiliation:
University of York
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Abstract

Objectives: The aim of the study was to investigate the feasibility and effectiveness of searching selected databases to identify information required to populate a decision-analytic model.

Methods: Methods of searching for information to populate a decision-analytic model were piloted using a case study of prophylactic antibiotics to prevent recurrent urinary tract infections in children. This study explored how the information requirements for a decision-analytic model could be developed into searchable questions and how search strategies could be derived to answer these questions. The study also assessed the usefulness of three published search filters and explored which resources might produce relevant information for the various model parameters.

Results: Based on the data requirements for this case study, 42 questions were developed for searching. These questions related to baseline event rates, health-related quality of life and outcomes, relative treatment effects, resource use and unit costs, and antibiotic resistance. A total of 1,237 records were assessed by the modeler, and of these, 48 were found to be relevant to the model. Search precision ranged from 0 percent to 38 percent, and no single database proved the most useful for all the questions.

Conclusions: The process of conducting specific searches to address each of the model questions provided information that was useful in populating the case study model. The most appropriate resources to search were dependent on the question, and multiple database searching using focused search strategies may prove more effective in finding relevant data than thorough searches of a single database.

Keywords

Information storage and retrievalDecision support techniquesDatabases
Type
GENERAL ESSAYS
Information
International Journal of Technology Assessment in Health Care , Volume 21 , Issue 3 , July 2005 , pp. 305 - 311
Copyright
© 2005 Cambridge University Press

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References

Akehurst R, Anderson P, Brazier J, et al. 2000 Decision analytic modelling in the economic evaluation of health technologies—A consensus statement. Pharmacoeconomics. 17: 443– 444.Google Scholar
Brettle AJ, Long AF, Grant MJ, et al. 1998 Searching for information on outcomes: Do you need to be comprehensive? Qual Health Care. 7: 163– 167.Google Scholar
Centre for Reviews and Dissemination (CRD). How are studies identified for inclusion in NHS EED? Available at: http://www.york.ac.uk/inst/crd/nfaq2.htm. Accessed 26 October 2004.
Centre for Reviews and Dissemination (CRD). Information resources in health economics. Available at: http://www.york.ac.uk/inst/crd/econ.htm. Accessed 18 August 2004.
Claxton K, Ginnelly L, Sculpher M, et al. 2004 A pilot study on the use of decision theory and value of information analysis as part of the NHS health technology assessment programme. Health Technol Assess. 8: 31.Google Scholar
The Cochrane Collaboration. Reviewer's handbook online version 4.2.2. Available at: http://www.cochrane.org/resources/handbook/index.htm. Accessed 18 August 2004.
Coyle D, Lee K. 2002 Evidence-based economic evaluation: How the use of different data sources can impact results. In: Donaldson C, Mugford M, Vale L, eds. Evidence-based health economics. London: BMJ Books; 55– 66.
Dickersin K, Scherer R, Lefebvre C. 1994 Systematic reviews: Identifying relevant studies for systematic reviews. BMJ. 309: 1286– 1291.Google Scholar
Eddy D. 1985 Technology assessment: The role of mathematical modelling. In: Mosteller F, ed. Assessing medical technologies. Washington, DC: National Academy Press; 144– 160.
Halpern MT, McKenna M, Hutton J. 1998 Modeling in economic evaluation: An unavoidable fact of life. Health Econ. 7: 741– 742.Google Scholar
Halpern MT, Luce BR, Brown RE, et al. 1998 Health and economic outcomes modeling practices: A suggested framework. Value Health. 1: 131– 147.Google Scholar
Hay J, Jackson J. 1999 Panel 2: Methodological issues in conducting pharmacoeconomic evaluations - modeling studies. Value Health. 2: 78– 81.Google Scholar
ISPOR Task Force. 2003; Principles of good practice for decision analytic modeling in health care evaluation. Value Health. 6: 9– 17.
McCabe C, Dixon S. 2000 Testing the validity of cost-effectiveness models. Pharmacoeconomics. 17: 501– 513.Google Scholar
Mugford M. 2001 Using systematic reviews for economic evaluation. In: Egger M, Davey Smith G, Altman D, eds. Systematic reviews in health care: Meta analysis in context. London: BMJ Publishing Group; 419– 428.
Napper M, Varney J. Health economics information. In: Etext on Health Technology Assessment (HTA) Information Resources. Available at: http://www.nlm.nih.gov/nichsr/ehta/chapter11.html. Accessed 18 August 2004.
NHS Centre for Reviews and Dissemination. 2001. Improving access to cost-effectiveness information for health care decision-making: The NHS Economic Evaluation Database. Report Number 6. 2nd ed. York: NHS CRD;
NHS Centre for Reviews and Dissemination. 2001. Undertaking systematic reviews of research on effectiveness: CRD's guidance for those carrying out or commissioning reviews. Report Number 4. 2nd ed. York: NHS CRD;
Nuijten MJ. 1998 The selection of data sources for use in modelling studies. Pharmacoeconomics. 13: 305– 316.Google Scholar
Office of Health Economics (OHE). Health economic evaluations database: About HEED. Available at: http://www.ohe-heed.com/about.htm. Accessed 26 October 2004.
Paisley S. 2002. Rapid reviews: Less than, greater than or equal to systematic reviews. Presented at NICE Information Specialists' meeting: Methodologies for technology assessment reports. London: NICE;
Paisley S, Booth A, Mensinkai S. Health-related quality of life studies. In: Etext on Health Technology Assessment (HTA) Information Resources. Available at: http://www.nlm.nih.gov/nichsr/ehta/chapter12.html. Accessed 18 August 2004.
Philips Z, Ginnelly L, Sculpher M, et al. 2004 A review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess. 8: 36.Google Scholar
Ramsey SD, Sullivan SD. 1999 Weighing the economic evidence: Guidelines for critical assessment of cost-effectiveness analyses. J Am Board Fam Pract. 12: 477– 485.Google Scholar
Sculpher M, Fenwick E, Claxton K. 2000 Assessing quality in decision analytic cost-effectiveness models: A suggested framework and example of application. Pharmacoeconomics. 17: 461– 477.Google Scholar
Sonnenberg FA, Roberts MS, Tsevat J, et al 1994; Toward a peer review process for medical decision analysis models. Med Care. 32: S52– S64.Google Scholar
Soto J. 2002 Health economic evaluations using decision analytic modeling. Principles and practices-utilization of a checklist to their development and appraisal. Int J Technol Assess Health Care. 18: 94– 111.Google Scholar
Weinstein MC, Toy EL, Sandberg EA, et al. 2001 Modeling for health care and other policy decisions: Uses, roles, and validity. Value Health. 4: 348– 361.Google Scholar
White VJ, Glanville JM, Lefebvre C, et al. 2001 A statistical approach to designing search filters to find systematic reviews: Objectivity enhances accuracy. J Inform Sci. 27: 357– 370.Google Scholar