A prospective evaluation of the safety and efficacy of the TAXUS Element paclitaxel-eluting coronary stent system for the treatment of de novo coronary artery lesions: Design and statistical methods of the PERSEUS clinical program

<p>Abstract</p> <p>Background</p> <p>Paclitaxel-eluting stents decrease angiographic and clinical restenosis following percutaneous coronary intervention compared to bare metal stents. TAXUS Element is a third-generation paclitaxel-eluting stent which incorporates a novel, thinner-strut, platinum-enriched metal alloy platform. The stent is intended to have enhanced radiopacity and improved deliverability compared to other paclitaxel-eluting stents. The safety and efficacy of the TAXUS Element stent are being evaluated in the pivotal PERSEUS clinical trials.</p> <p>Methods/Design</p> <p>The PERSEUS trials include two parallel studies of the TAXUS Element stent in single, de novo coronary atherosclerotic lesions. The PERSEUS Workhorse study is a prospective, randomized (3:1), single-blind, non-inferiority trial in subjects with lesion length ≤28 mm and vessel diameter ≥2.75 mm to ≤4.0 mm which compares TAXUS Element to the TAXUS Express²paclitaxel-eluting stent system. The Workhorse study employs a novel Bayesian statistical approach that uses prior information to limit the number of study subjects exposed to the investigational device and thus provide a safer and more efficient analysis of the TAXUS Element stent. PERSEUS Small Vessel is a prospective, single-arm, superiority trial in subjects with lesion length ≤20 mm and vessel diameter ≥2.25 mm to <2.75 mm that compares TAXUS Element with a matched historical bare metal Express stent control.</p> <p>Discussion</p> <p>The TAXUS PERSEUS clinical trial program uses a novel statistical approach to evaluate whether design and metal alloy iterations in the TAXUS Element stent platform provide comparable safety and improved procedural performance compared to the previous generation Express stent. PERSEUS trial enrollment is complete and primary endpoint data are expected in 2010. PERSEUS Workhorse and Small Vessel are registered at <url>http://www.clinicaltrials.gov</url>, identification numbers NCT00484315 and NCT00489541.</p

A causal roadmap for generating high-quality real-world evidence

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases

Cause-specific mortality in a cohort of patients with diabetes mellitus:A population-based study in Sweden

A cohort of patients with diabetes mellitus hospitalised in Sweden from 1965 to 1983 was followed up until 1989, by linkages of population-based registers. Standardised mortality ratios (SMR), adjusted for confounding variables, and 95% confidence intervals (CIs) were calculated. After exclusion of the first year of follow-up (to reduce the effect of selection bias), the cohort consisted of 144,427 patients, of whom 92,248 patients died during follow-up. The SMR for all causes of death combined was 2.62 (95% CI 2.58- 2.67) among men and 3.23 (95% CI 3.18-3.28) among women. The excess mortality was still evident 20 years after first hospitalisation. but became less marked with longer follow-up time. Patients with presumably insulin-dependent diabetes mellitus (IDDM) had the highest SMRs (10.2: CI 9.5-11.0); however, there was a significant (34%) improvement over time in their mortality risk. We conclude that excess mortality persisted throughout all calendar periods and at all ages, indicating the need for health care prevention measures. (C) 2001 Elsevier Science Inc. All rights reserved

Desirability of Outcome Ranking (DOOR) and Response Adjusted for Duration of Antibiotic Risk (RADAR)

Clinical trials that compare strategies to optimize antibiotic use are of critical importance but are limited by competing risks that distort outcome interpretation, complexities of noninferiority trials, large sample sizes, and inadequate evaluation of benefits and harms at the patient level. The Antibacterial Resistance Leadership Group strives to overcome these challenges through innovative trial design. Response adjusted for duration of antibiotic risk (RADAR) is a novel methodology utilizing a superiority design and a 2-step process: (1) categorizing patients into an overall clinical outcome (based on benefits and harms), and (2) ranking patients with respect to a desirability of outcome ranking (DOOR). DOORs are constructed by assigning higher ranks to patients with (1) better overall clinical outcomes and (2) shorter durations of antibiotic use for similar overall clinical outcomes. DOOR distributions are compared between antibiotic use strategies. The probability that a randomly selected patient will have a better DOOR if assigned to the new strategy is estimated. DOOR/RADAR represents a new paradigm in assessing the risks and benefits of new strategies to optimize antibiotic use