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Bayesian Analysis: A Practical Approach to Interpret Clinical Trials and Create Clinical Practice Guidelines.

ABSTRACT: Bayesian analysis is firmly grounded in the science of probability and has been increasingly supplementing or replacing traditional approaches based on P values. In this review, we present gradually more complex examples, along with programming code and data sets, to show how Bayesian analysis takes evidence from randomized clinical trials to update what is already known about specific treatments in cardiovascular medicine. In the example of revascularization choices for diabetic patients who have multivessel coronary artery disease, we combine the results of the FREEDOM trial (Future Revascularization Evaluation in Patients with Diabetes Mellitus: Optimal Management of Multivessel Disease) with prior probability distributions to show how strongly we should believe in the new Class I recommendation ("should be done") for a preference of bypass surgery over percutaneous coronary intervention. In the debate about the duration of dual antiplatelet therapy after drug-eluting stent implantation, we avoid a common pitfall in traditional meta-analysis and create a network of randomized clinical trials to compare outcomes after specific treatment durations. Although we find no credible increase in mortality, we affirm the tradeoff between increased bleeding and reduced myocardial infarctions with prolonged dual antiplatelet therapy, findings that support the new Class IIb recommendation ("may be considered") to extend dual antiplatelet therapy after drug-eluting stent implantation. In the decision between culprit artery-only and multivessel percutaneous coronary intervention in patients with ST-segment elevation myocardial infarction, we use hierarchical meta-analysis to analyze evidence from observational studies and randomized clinical trials and find that the probability of all-cause mortality at longest follow-up is similar after both strategies, a finding that challenges the older ban against noninfarct-artery intervention during primary percutaneous coronary intervention. These examples illustrate how Bayesian analysis integrates new trial information with existing knowledge to reduce uncertainty and change attitudes about treatments in cardiovascular medicine.

SUBMITTER: Bittl JA

PROVIDER: S-EPMC6421843 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Bayesian Analysis: A Practical Approach to Interpret Clinical Trials and Create Clinical Practice Guidelines.

Bittl John A JA He Yulei Y

Circulation. Cardiovascular quality and outcomes 20170801 8

Bayesian analysis is firmly grounded in the science of probability and has been increasingly supplementing or replacing traditional approaches based on <i>P</i> values. In this review, we present gradually more complex examples, along with programming code and data sets, to show how Bayesian analysis takes evidence from randomized clinical trials to update what is already known about specific treatments in cardiovascular medicine. In the example of revascularization choices for diabetic patients ...[more]

PMID: 28798016

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Project description:IntroductionAn adaptive guideline development method, as opposed to a de novo guideline development, is dependent on access to existing high-quality up-to-date clinical practice guidelines (CPGs). We described the characteristics and quality of CPGs relevant to prehospital care worldwide, in order to strengthen guideline development in low-resource settings for emergency care.MethodsWe conducted a descriptive study of a database of international CPGs relevant to emergency care produced by the African Federation for Emergency Medicine (AFEM) CPG project in 2016. Guideline quality was assessed with the AGREE II tool, independently and in duplicate. End-user documents such as protocols, care pathways, and algorithms were excluded. Data were imported, managed, and analysed in STATA 14 and R.ResultsIn total, 276 guidelines were included. Less than 2% of CPGs originated from low- and middle income-countries (LMICs); only 15% (n = 38) of guidelines were prehospital specific, and there were no CPGs directly applicable to prehospital care in LMICs. Most guidelines used de novo methods (58%, n = 150) and were produced by professional societies or associations (63%, n = 164), with the minority developed by international bodies (3%, n = 7). National bodies, such as the National Institute for Health and Care Excellence (NICE) and the Scottish Intercollegiate Guidelines Network (SIGN), produced higher quality guidelines when compared to international guidelines, professional societies, and clinician/academic-produced guidelines. Guideline quality varied across topics, subpopulations and producers. Resource-constrained guideline developers that cannot afford de novo guideline development have access to an expanding pool of high-quality prehospital guidelines to translate to their local setting.DiscussionAlthough some high-quality CPGs exist relevant to emergency care, none directly address the needs of prehospital care in LMICs, especially in Africa. Strengthening guideline development capacity, including adaptive guideline development methods that use existing high-quality CPGs, is a priority.

Dataset Information

Bayesian Analysis: A Practical Approach to Interpret Clinical Trials and Create Clinical Practice Guidelines.

Publications

Bayesian Analysis: A Practical Approach to Interpret Clinical Trials and Create Clinical Practice Guidelines.

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