Clinical trials : journal of the Society for Clinical Trials
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This article presents some real-life challenges faced by clinical trial Data Monitoring Committees (DMCs), with the aim of clarifying some of the controversial issues that relate to both statistical stopping boundaries and DMC decision-making. Specific attention is given to what constitutes a sensible statistical boundary for stopping a trial early for benefit, bearing in mind that one usually needs proof beyond reasonable doubt of a treatment benefit sufficient to alter future clinical practice. Appropriate choices of stopping boundary for harm and futility are also discussed. The examples serve to illustrate that the practicalities of DMC decision-making require wise judgements based on a totality of evidence, making any statistical boundary just an objective guideline rather than a definitive stopping rule.
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The objective of this research was to identify determinants of the magnitude of intracluster correlation coefficients (ICCs) in cluster randomized trials from the field of implementation research. A survey of experts was conducted to generate a priori hypotheses of factors that might affect ICC size. Hypotheses were tested on empirical estimates of ICCs calculated from 21 implementation research datasets, mainly from the UK. ⋯ In conclusion, accurate estimates of ICCs are essential for sample size calculations for cluster randomized trials of professional behaviour change interventions. This study demonstrates that ICCs are sensitive to a number of trial factors, particularly setting and outcome type. These factors must be considered when planning such cluster randomized trials.
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Comparative Study
Rationale and design of the Optimal Macro-Nutrient Intake Heart Trial to Prevent Heart Disease (OMNI-Heart).
The DASH (Dietary Approaches to Stop Hypertension) diet is a carbohydrate-rich, reduced-fat diet that lowers blood pressure (BP) and LDL-cholesterol. Whether partial replacement of some carbohydrate (C) with either protein (P) or unsaturated fat (U) can further improve these and other cardiovascular (CVD) risk factors is unknown. ⋯ OMNI-Heart should advance our fundamental knowledge of the effects of diet on both traditional and emerging risk factors, and, in the process, guide policy makers, health care providers and the general public on the relative benefits of carbohydrate, protein, and unsaturated fat as a means to reduce CVD risk.
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Cluster randomized trials are increasingly common. Obtaining informed patient consent to participation in these trials raises practical challenges and ethical issues. The aims of this paper were to 1) develop a typology of interventions employed in cluster randomized trials in primary care; 2) assess whether the likelihood of seeking individual consent to participation varies by intervension type; 3) assess whether this likelihood has increased over time; 4) assess evidence for under reporting of consent procedures; 5) articulate reasons for not obtaining consent; and 6) make recommendations for future trial investigators. ⋯ Where feasible, they should allow patients to opt out of the trial. Lay individuals should represent trial participants as part of the process of cluster consent to participation, and lay individuals could also be involved in considering ethical issues during trial planning. A more public debate may clarify the general acceptability of not obtaining consent in certain situations.
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The magnitude of the effect of an intervention on a quantitative outcome may be expressed as a standardized mean difference by dividing the difference in means by the standard deviation of the outcome. This is useful to compare outcomes measured using different scales, especially in meta-analysis. However, uncertainty about the standard deviation leads to complicated formulae to avoid bias and to compute the correct standard error. ⋯ We then extend the formulae to cluster-randomized trials, and show how the calculations may be implemented using published results. We also describe methods for estimating the standard deviation. Various pitfalls are identified which can lead to major errors especially in the cluster-randomized setting.