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03608nam a2200349 u 4500 |
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EB001029776 |
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EBX01000000000000000823329 |
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00000000000000.0 |
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| 008 |
150411 r ||| eng |
| 100 |
1 |
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|a Sanders, Gillian D.
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| 245 |
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|a Use of Bayesian techniques in randomized clinical trials
|h Elektronische Ressource
|b a CMS case study
|c Duke Evidence-based Practice Center; Gillian D. Sanders, Lurdes Inoue, Gregory Samsa, Shalini Kulasingam, David Matchar ; prepared for Agency for Healthcare Research and Quality
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| 260 |
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|a Rockville, Maryland
|b AHRQ, Technology Assessment Program
|c September 18, 2009, 2009
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| 300 |
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|a 1 PDF file (184 pages)
|b illustrations
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|a Includes bibliographical references
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| 653 |
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|a Death, Sudden, Cardiac / prevention & control
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| 653 |
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|a Defibrillators, Implantable
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| 653 |
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|a Randomized Controlled Trials as Topic
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| 653 |
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|a Bayes Theorem
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| 653 |
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|a United States Government Agencies
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| 700 |
1 |
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|a Inoue, Lurdes Y. T.
|e [author]
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| 700 |
1 |
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|a Samsa, Gregory Paul
|e [author]
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| 700 |
1 |
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|a Kulasingam, Shalini L.
|e [author]
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| 710 |
2 |
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|a Duke University Evidence-based Practice Center
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| 710 |
2 |
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|a Technology Assessment Program (Agency for Healthcare Research and Quality)
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b NCBI
|a National Center for Biotechnology Information
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| 490 |
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|a Technology assessment report
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| 500 |
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|a Title from PDF title page. - "Project ID: STAB0508."
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| 856 |
4 |
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|u https://www.ncbi.nlm.nih.gov/books/NBK253213
|3 Volltext
|n NLM Bookshelf Books
|3 Volltext
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| 082 |
0 |
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|a 580
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| 520 |
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|a The use of Bayesian statistical approaches has gained broader acceptance within the clinical trial community. The impact of these methods on CMS decisional contexts and policy-level decisionmaking however was uncertain. Our analyses explore the main proclaimed advantages of Bayesian statistics (namely, the use of prior information, sample size determination, borrowing strength from different trials, and sequential monitoring of trials) and provide examples of decisionmaking situations where the findings reached using these approaches both agree with and differ from findings reached using frequentist statistical techniques. Our findings confirm that, like classical techniques, Bayesian approaches are affected by the problems of model specification (i.e., the relationship between various factors - patient, provider, intervention, and other contextual features - and the outcome of interest). In addition, Bayesian approaches can be substantially affected by the "Bayesian prior" - the representation of beliefs about the quantity of interest (e.g., relative risk of events when a new device is used vs. a conventional device). Thus, when considering using or interpreting Bayesian analyses, the focus of attention and thoughtful ex ante agreement are the specification of the model and specification of the Bayesian prior. The case study of the use of ICD therapy in the prevention of sudden cardiac death demonstrates the application of these techniques and highlights some of the practical challenges. The use of Bayesian statistical approaches, and incorporation of our findings concerning their strengths and limitations into the CMS decisionmaking process will enable policymakers to harness the power of the available sources of clinical evidence, explore subgroup effects within a trial and across trials in a methodologically rigorous manner, assess the uncertainty in clinical trial findings, and - ideally - improve health outcomes for Medicare beneficiaries
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