Confidence intervals are widely accepted as a preferred way to present study results. They encompass significance tests and provide an estimate of the magnitude of the effect. However, comparisons of correlations still rely heavily on significance testing. The persistence of this practice is caused primarily by the lack of simple yet accurate procedures that can maintain coverage at the nominal level in a nonlopsided manner. The purpose of this article is to present a general approach to constructing approximate confidence intervals for differences between (a) 2 independent correlations, (b) 2 overlapping correlations, (c) 2 nonoverlapping correlations, and (d) 2 independent R 2 s. The distinctive feature of this approach is its acknowledgment of the asymmetry of sampling distributions for single correlations. This approach requires only the availability of confidence limits for the separate correlations and, for correlated correlations, a method for taking into account the dependency between correlations. These closed-form procedures are shown by simulation studies to provide very satisfactory results in small to moderate sample sizes. The proposed approach is illustrated with worked examples.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy–Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

Julian Little and colleagues present the STREGA recommendations, which are aimed at improving the reporting of genetic association studies.

Abstract-Notwithstanding the availability of antihypertensive drugs and practice guidelines, blood pressure control remains suboptimal. The complexity of current treatment guidelines may contribute to this problem. To determine whether a simplified treatment algorithm is more effective than guideline-based management, we studied 45 family practices in southwestern Ontario, Canada, using a cluster randomization trial comparing the simplified treatment algorithm with the Canadian Hypertension Education Program guidelines. The simplified treatment algorithm consisted of the following: (1) initial therapy with a low-dose angiotensin-converting enzyme inhibitor/diuretic or angiotensin receptor blocker/diuretic combination; (2) up-titration of combination therapy to the highest dose; (3) addition of a calcium channel blocker and up-titration; and (4) addition of a non-first-line antihypertensive agent. The proportion of patients treated to target blood pressure (systolic blood pressure Ͻ140 mm Hg and diastolic blood pressure Ͻ90 mm Hg for patients without diabetes mellitus or systolic blood pressure Ͻ130 mm Hg and diastolic blood pressure Ͻ80 mm Hg for diabetic patients) at 6 months was analyzed at the practice level. Key Words: hypertension management Ⅲ randomized, controlled trial Ⅲ fixed-dose combination therapy Ⅲ cluster randomization Ⅲ hypertension T reatment of hypertension remains suboptimal despite the development of novel therapies and the widespread implementation of education programs. Although multiple barriers exist to achieving better control of blood pressure, these mainly consist of patient-and practitioner-centered factors.From the patient perspective, poor adherence to antihypertensive regimens is a significant component of the "treatment gap." 1-3 An important part of this problem is current prescribing practices. Unfortunately, most patients require Ն2 medications to achieve optimal control, 4 and these multidrug regimens are associated with lower adherence. 5,6 This has been suggested to be the most important contribution to inadequate blood pressure control. 7 Furthermore, switching medications, a strategy featured prominently in most national treatment guidelines, has also been linked to poor adherence. 8 Thus, the use of simpler, more effective drug regimens might improve blood pressure control.From a practitioner perspective, a number of behavioral factors have been associated with poor blood pressure control. One of the most important factors is "therapeutic inertia," whereby practitioners fail to appropriately escalate the intensity of therapy despite the presence of poorly controlled hypertension. 9 We speculate that the increasingly complex treatment regimens currently advocated by experts, national guidelines, and the pharmaceutical industry might contribute to this undesirable behavior.Given these issues, a simple, step-care-based algorithm for the pharmacological management of hypertension (Simplified Treatment Intervention to Control Hypertension [STITCH]) was developed. This algorithm...