Bayesian data analysis in observational comparative effectiveness research: rationale and examples

Domperidone increases harmful cardiac events in Parkinson's disease: A Bayesian re-analysis of an observational study

OBJECTIVES

To assess the risks of ventricular tachyarrhythmia/sudden cardiac death (VT/SCD) with domperidone use in Parkinson's disease (PD).

STUDY DESIGNS AND SETTINGS

Using Bayesian methods, results from an observationalstudy were combined with prior beliefs to calculate posterior probabilities of increasedrelative risk (RR)) of VT/SCD with use of domperidone compared to non-use and ofharm, defined as risk exceeding 15%. The analyses were carried with normallydistributed priors (log (RR)): uninformative (N(0,10)) or informative (N(0.53,179)),derived from a meta-analysis (OR (95%CI):1.70 (1.47-1.97)). Sensitivity analyses used:different priors' strengths, different priors, and Bayesian meta-analysis RESULTS: The uninformative prior yielded a RR: 1.23 (95% credible interval (CrI):0.94-1.62), like the published frequentist RR: 1.22 (95% CI:0.99-1.50), with 69% probabilityof harm. With an informative prior weighted at 100%, 50% and 10%, the RR were 1.63(1.41-1.88), 1.57 (1.31-1.91) and 1.39 (1.10-1.93), respectively. The correspondingprobabilities of harm were 100%, 99%, and 94%, respectively.

CONCLUSION

While both the frequentist and Bayesian approaches with anuninformative prior were unable to reach a definitive conclusion concerning thearrhythmic risk of domperidone in PD patients, the Bayesian analysis with informativepriors showed a high probability of increased risk that was robust to multiple priorsensitivity analyses.

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

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.