Brian L. Claggett

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When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for(More)
Online publish-ahead-of-print 17 January 2013 This editorial refers to 'Evaluation of early percutaneous coronary intervention vs. standard therapy after fibrinoly-sis for ST-segment elevation myocardial infarction: contribution of weighting the composite endpoint' † , by J.A. The randomized controlled clinical trial currently represents the gold standard(More)
BACKGROUND Although clear evidence shows that chronic kidney disease is a predictor of cardiovascular events, death, and accelerated coronary artery disease (CAD) progression, it remains unknown whether CAD is a predictor of progression of chronic kidney disease to end-stage renal disease. We sought to assess whether CAD adds prognostic information to(More)
In a typical randomized clinical study to compare a new treatment with a control, oftentimes each study subject may experience any of several distinct outcomes during the study period, which collectively define the "risk-benefit" profile. To assess the effect of treatment, it is desirable to utilize the entirety of such outcome information. The times to(More)
AIMS In this analysis, we utilized data from PARADIGM-HF to test the hypothesis that participants who exhibited any dose reduction during the trial would have similar benefits from lower doses of sacubitril/valsartan relative to lower doses of enalapril. METHODS AND RESULTS In a post-hoc analysis from PARADIGM-HF, we characterized patients by whether they(More)
Meta-analysis is a valuable tool for combining information from independent studies. However, most common meta-analysis techniques rely on distributional assumptions that are difficult, if not impossible, to verify. For instance, in the commonly used fixed-effects and random-effects models, we take for granted that the underlying study-level parameters are(More)
Meta-analysis is a valuable tool for combining information from independent studies. However, most common meta-analysis techniques rely on distributional assumptions that are difficult, if not impossible, to verify. For instance, in the commonly used fixed-effects and random-effects models, we take for granted that the underlying study parameters are either(More)
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