Bayesian analysis for medical device trials.
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Bayes Theorem↗
Biomedical subjects
Scott M Berry
Publications and source records attributed to Scott M Berry.
Additive benefits of pravastatin and aspirin to decrease risks of cardiovascular disease: randomized and observational comparisons of secondary prevention trials and their meta-analyses.
BACKGROUND: In randomized trials of secondary prevention, pravastatin sodium and aspirin reduce risks of cardiovascular disease. Pravastatin has a predominantly delayed antiatherogenic effect, and aspirin has an immediate antiplatelet effect, raising the possibility of additive clinical benefits. METHODS: In 5 randomized trials of secondary prevention with pravastatin (40 mg/d), comprising 73 900 patient-years of observation, aspirin use was also prescribed in varying frequencies, and data were available on a large number of confounding variables. We tested whether pravastatin and aspirin have additive benefits in the 2 large trials (Long-term Intervention With Pravastatin in Ischaemic Disease trial and the Cholesterol and Recurrent Events trial) that were designed to test clinical benefits. We also performed meta-analyses of these 2 trials and 3 smaller angiographic trials that collected clinical end points. In all analyses, multivariate models were used to adjust for a large number of cardiovascular disease risk factors. RESULTS: Individual trials and all meta-analyses demonstrated similar additive benefits of pravastatin and aspirin on cardiovascular disease. In meta-analysis, the relative risk reductions for fatal or nonfatal myocardial infarction were 31% for pravastatin plus aspirin vs aspirin alone and 26% for pravastatin plus aspirin vs pravastatin alone. For ischemic stroke, the corresponding relative risk reductions were 29% and 31%. For the composite end point of coronary heart disease death, nonfatal myocardial infarction, coronary artery bypass graft, percutaneous transluminal coronary angioplasty, or ischemic stroke, the relative risk reductions were 24% and 13%. All relative risk reductions were statistically significant. CONCLUSION: More widespread and appropriate combined use of statins and aspirin in secondary prevention of cardiovascular disease will avoid large numbers of premature deaths.
Aged↗
Accounting for multiplicities in assessing drug safety: a three-level hierarchical mixture model.
Multiple comparisons and other multiplicities are among the most difficult of problems that face statisticians, frequentists, and Bayesians alike. An example is the analysis of the many types of adverse events (AEs) that are recorded in drug clinical trials. We propose a three-level hierarchical mixed model. The most basic level is type of AE. The second level is body system, each of which contains a number of types of possibly related AEs. The highest level is the collection of all body systems. Our analysis allows for borrowing across body systems, but there is greater potential-depending on the actual data-for borrowing within each body system. The probability that a drug has caused a type of AE is greater if its rate is elevated for several types of AEs within the same body system than if the AEs with elevated rates were in different body systems. We give examples to illustrate our method and we describe its application to other types of problems.
Biometry↗
Comparison of the dose-response relationships of 2 lipid-lowering agents: a Bayesian meta-analysis.
BACKGROUND: Comparing the dose-response of a new drug to that of a previously studied drug can aid in understanding their relative potencies. Two dose-finding studies addressed the effect of a new drug, rosuvastatin, on its ability to decrease low-density lipoprotein cholesterol (LDL-C) levels. One of these studies included 2 doses of atorvastatin, and substantial additional information is available in the literature about the effect of atorvastatin on LDL-C level lowering. METHODS: The 2 dose-finding studies of rosuvastatin considered otherwise healthy patients who had hypercholesterolemia. Comparable studies of atorvastatin were identified via a MEDLINE search in December 1999. Multiple reviewer consensus identified 15 of 41 studies on atorvastatin published since 1996 that met these selection criteria: reporting of LDL-C level change from baseline at least 6 weeks after treatment initiation, doses administered, and treatment group sizes. Eligible populations had clinical evidence of hypercholesterolemia. We excluded studies with patients who had severe illness or a previous history of transplantation. Data extraction of the mean, sample sizes, and SDs (or CIs) by dose was carried out independently by multiple reviewers. We combined the results from the various studies with Bayesian hierarchical modeling and analyzed them with Markov chain Monte Carlo techniques. RESULTS: Combining this study and literature results substantially increased the power to compare the dose-response relationships of rosuvastatin and atorvastatin. Rosuvastatin reduced LDL-C level by an estimated 10 to 17 percentage points more than atorvastatin when both were given at the same dose. Approximately one quarter of the dose of rosuvastatin achieved about the same magnitude of LDL-C level reduction as atorvastatin at dosages as high as 80 mg. This finding does not imply a 4-fold difference in efficacy overall and specifically does not describe the results at higher dosage levels. CONCLUSIONS: Bayesian meta-analysis of results from related studies allows the comparison of the dose-response relationships of 2 drugs, better estimates of a particular dose-response relationship within an individual study, and the expression of relative benefits (of dose and drug) in terms of probabilities. Explicitly comparing a study's results with historical data using Bayesian meta-analysis allows clinicians to view the study in the larger context of medical research.
Anticholesteremic Agents↗