Inefficacy of different strategies to improve guideline awareness – 5-year follow-up of the hypertension evaluation project (HEP)

<p>Abstract</p> <p>Background</p> <p>In spite of numerous guidelines for evidence based diagnostic and therapy adequate knowledge of current recommendations is disappointingly low. In the Hypertension Evaluation Project (HEP I) we showed that awareness of national hypertension guidelines under German practitioners was less than 25% in the year 2000. This indicates the need for efficient strategies to relevantly improve guideline awareness.</p> <p>Methods</p> <p>To asses different tools for amending guideline knowledge we used three strategies (guideline in print, interactive guideline, expert seminars) to train 8325 randomised physicians, who had participated in the HEP I trial. Guideline knowledge of the trained physicians was again tested with the HEP questionnaire and compared to a control group of HEP I physicians.</p> <p>Results</p> <p>The return rate of questionnaires was 57.9% without a significant distinction between the groups. Overall guideline awareness was still low but remarkably improved compared to the results of HEP I (37.1% vs. 23.7%, p < 0.0001). There was no difference between the trained physicians and the control group (35.8% and 35.9% vs. 39.7%, p = n.s.).</p> <p>Conclusion</p> <p>We investigated the influence of different strategies to improve guideline awareness among German physicians. None of our interventions (guideline in print, interactive guideline, expert seminars) brought a notable benefit compared to control group. However, overall knowledge of guideline contents increased from 23.7% to 37.1% over five years. Therefore, other probably multimodal interventions are necessary to significantly improve guideline awareness beyond spontaneous advancement.</p> <p>Trial Registration</p> <p>ISRCTN53383289</p

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs

Recent advances in methodology for clinical trials in small populations : the InSPiRe project

Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017. The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods

Group sequential and confirmatory adaptive designs in clinical trials

This book provides an up-to-date review of the general principles of and techniques for confirmatory adaptive designs. Confirmatory adaptive designs are a generalization of group sequential designs. With these designs, interim analyses are performed in order to stop the trial prematurely under control of the Type I error rate. In adaptive designs, it is also permissible to perform a data-driven change of relevant aspects of the study design at interim stages. This includes, for example, a sample-size reassessment, a treatment-arm selection or a selection of a pre-specified sub-population. Essentially, this adaptive methodology was introduced in the 1990s. Since then, it has become popular and the object of intense discussion and still represents a rapidly growing field of statistical research. This book describes adaptive design methodology at an elementary level, while also considering designing and planning issues as well as methods for analyzing an adaptively planned trial. This includes estimation methods and methods for the determination of an overall p-value. Part I of the book provides the group sequential methods that are necessary for understanding and applying the adaptive design methodology supplied in Parts II and III of the book. The book contains many examples that illustrate use of the methods for practical application. The book is primarily written for applied statisticians from academia and industry who are interested in confirmatory adaptive designs. It is assumed that readers are familiar with the basic principles of descriptive statistics, parameter estimation and statistical testing. This book will also be suitable for an advanced statistical course for applied statisticians or clinicians with a sound statistical background

A Note on repeated p-values for group sequential designs

One-sided confidence intervals and overall p-values for group-sequential designs are typically based on a sample space ordering which determines both the overall p-value and the corresponding confidence bound. Accordingly, the strength of evidence against the null hypothesis is consistently measured by both quantities such that the order of the p-values of two distinct sample points is consistent with the order of the respective confidence bounds. An exception is the commonly used repeated p-values and repeated confidence intervals. We show that they are not ordering-consistent in the above sense and propose an alternative repeated p-value which is ordering-consistent and has the monitoring property of the classical repeated p-value in being valid even when deviating from the prefixed stopping rule. Copyright 2008, Oxford University Press.