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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[superscript 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.

Two tests to examine the effectiveness of omega-3 free fatty acids in reducing the rate of relapse of Crohn disease were conducted. Results concluded that omega-3 fatty acids are not effective in reducing rate of relapse in patients with Crohn disease.

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.

The purpose of this study was to develop a brief physical activity interview for older adults (Phone-FITT) and evaluate its test-retest reliability and validity. Summary scores were derived for household, recreational, and total PA. Reliability was evaluated in a convenience sample from a fall-prevention study (N = 43, 79.4 +/- 2.9 years, 51% male), and validity, in a random sample of individuals in older adult exercise programs (N = 48, 77.4 +/- 4.7 years, 25% male). Mean time to complete the Phone-FITT was 10 min for participants sampled from exercise programs. Evaluation of test-retest reliability indicated substantial to almost perfect agreement for all scores, with intraclass correlation coefficients (95% confidence intervals) ranging from .74 (.58-.85) to .88 (.8-.94). For validity, Spearman's rho correlations of Phone-FITT scores with accelerometer counts ranged from .29 (.01-.53) to .57 (.34-.73). Correlations of Phone-FITT recreational scores with age and seconds to complete a self-paced step test ranged from -.29 (-.53 to -.01) to -.45 (-.68 to -.14). This study contributes preliminary evidence of the reliability and validity of the Phone-FITT.

Testing for departures from the assumption of Hardy-Weinberg equilibrium (HWE) has been widely recommended as a preliminary step in the analysis of genetic case-control studies. Some authors suggest using a two-stage procedure in which gene/disease associations are ultimately evaluated using either the Pearson chi-square procedure or the Cochran-Armitage test for trend. Other authors go further and encourage investigators to discard data that are in violation of HWE, essentially using the test as a tool for identifying genotyping errors. In this paper we show that 1) testing for HWE should not be used as a tool to identify genotyping errors; and 2) it is not necessary, and possibly even harmful, to test the HWE assumption before testing for association between alleles and disease. Instead one should inherently account for deviations from HWE with an adjusted chi-square test statistic, a procedure which in the present context is identical to the trend test. Examples from previous reports are used to illustrate the methodology.