Compliance With Guideline-Directed Medical Therapy in Contemporary Coronary Revascularization Trials

Background: Despite the well-established benefits of secondary cardiovascular prevention, the importance of concurrent medical therapy in clinical trials of coronary revascularization is often overlooked. Objectives: The goal of this study was to assess compliance with guideline-directed medical therapy (GDMT) in clinical trials and its potential impact on the comparison between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Methods: The Cochrane Central Register of Controlled Trials and MEDLINE were searched from 2005 to August 2017. Clinical trial registries and reference lists of relevant studies were also searched. Randomized controlled trials comparing PCI with drug-eluting stents versus CABG and reporting medical therapy after revascularization were included. The study outcome was compliance with GDMT, defined as the following: 1) any antiplatelet agent plus beta-blocker plus statin (GDMT1); and 2) any antiplatelet agent plus beta-blocker plus statin plus angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (GDMT2). Data collection and analysis were performed according to the methodological recommendations of The Cochrane Collaboration. Results: From a total of 439 references, 5 trials were included based on our inclusion and exclusion criteria. Overall, compliance with GDMT1 was low and decreased over time from 67% at 1 year to 53% at 5 years. Compliance with GDMT2 was even lower and decreased from 40% at 1 year to 38% at 5 years. Compliance with both GDMT1 and GDMT2 was higher in PCI than in CABG at all time points. Meta-regression suggested an association between lower use of GDMT1 and adverse clinical outcomes in PCI versus CABG at 5 years. Conclusions: Compliance with GDMT in contemporary clinical trials remains suboptimal and is significantly lower after CABG than after PCI, which may influence the comparison of clinical trial endpoints between those study groups

Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data

We consider random effects meta-analysis where the outcome variable is the occurrence of some event of interest. The data structures handled are where one has one or more groups in each study, and in each group either the number of subjects with and without the event, or the number of events and the total duration of follow-up is available. Traditionally, the meta-analysis follows the summary measures approach based on the estimates of the outcome measure(s) and the corresponding standard error(s). This approach assumes an approximate normal within-study likelihood and treats the standard errors as known. This approach has several potential disadvantages, such as not accounting for the standard errors being estimated, not accounting for correlation between the estimate and the standard error, the use of an (arbitrary) continuity correction in case of zero events, and the normal approximation being bad in studies with few events. We show that these problems can be overcome in most cases occurring in practice by replacing the approximate normal within-study likelihood by the appropriate exact likelihood. This leads to a generalized linear mixed model that can be fitted in standard statistical software. For instance, in the case of odds ratio meta-analysis, one can use the non-central hypergeometric distribution likelihood leading to mixed-effects conditional logistic regression. For incidence rate ratio meta-analysis, it leads to random effects logistic regression with an offset variable. We also present bivariate and multivariate extensions. We present a number of examples, especially with rare events, among which an example of network meta-analysis. Copyright (C) 2010 John Wiley & Sons, Ltd.Development and application of statistical models for medical scientific researc