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High‐precision spectral detection and broadband wavelength sensors have attracted considerable interest for important application in color image sensing. Due to the absorption limitations of silicon, conventional complementary metal–oxide–semiconductors have a relatively limited wavelength region from the ultraviolet to near‐infrared spectrum. Here, a simple‐structured device is developed by assembling two back‐to‐back monolayer graphene (MLG)/Si/MLG heterojunctions for high‐resolution wavelength sensing applications. The single MLG/Si/MLG photodetector exhibits a responsivity of 0.18 A W−1, a specific detectivity of 1.36 × 109 Jones, and an external quantum efficiency of 28.5%. Thanks to the unique device geometry, the distribution of photo‐generation rates in the two photodetectors is completely different, which brings about different photoelectric properties. It is found that the relationship between the photocurrent ratio of both photodetectors and the light wavelength can be fitted by a monotonic function, according to which the wavelength of incident light in the range from 260 to 1050 nm can be accurately estimated. A high resolution of 1 nm is demonstrated across a broadband wavelength from 260 to 1000 nm. This good resolution, along with good repeatability and stability, endow the present device with potential promise in future spectral sensing applications. [ABSTRACT FROM AUTHOR]

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.

In stratified matched-pair studies, risk difference between two proportions is one of the most frequently used indices in comparing efficiency between two treatments or diagnostic tests. This article presents five simultaneous confidence intervals and two bootstrap simultaneous confidence intervals for risk differences in stratified matched-pair designs. The proposed confidence intervals are evaluated with respect to their coverage probabilities, expected widths, and ratios of the mesial noncoverage to noncoverage probability. Empirical results show that (1) hybrid simultaneous confidence intervals outperform nonhybrid simultaneous confidence intervals; (2) hybrid simultaneous confidence intervals based on median estimator outperform those based on maximum likelihood estimator; and (3) hybrid simultaneous confidence intervals incorporated with Wilson score and Agresti coull intervals and the bootstrap t-percentile simultaneous interval based on median unbiased estimators behave satisfactorily for small to large sample sizes in the sense that their empirical coverage probabilities are close to the prespecified nominal confidence level, and their ratios of the mesial noncoverage to noncoverage probabilities lie in [0.4,0.6] and are hence recommended. Real examples from clinical studies are used to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

Purpose: To compare the performance of eight regression models for analyzing risk of falling, focusing on the effect of physical inactivity in older veterans. Methods: This study uses data from a fall risk factor screening and modification trial in community-dwelling Canadian male veterans of World War II or the Korean War, with falls ascertained prospectively using calendars and physical activity (PA) measured at baseline with a single global question. The effect of PA on falling was assessed using eight different multivariable regression models, with three models treating falling as a non-recurrent event whereas the other five models regard falls as recurrent events. Results: Recurrent event models showed that male veterans who reported being less active than their peers were 1.42 (1.02–1.97) to 2.46 (1.18–5.14) times more likely to fall than those who reported being about as or more active than their peers (n = 270; mean age ± SD = 81.1 ± 4.0 years). None of the non-recurrent event models detected a statistically significant association between PA and falls. Conclusions: Risk of falling may be better analyzed using regression models for recurrent events. These results have important implications for the collection and analysis of fall outcome data. [Copyright &y& Elsevier]

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.

Background: There are debates on whether the conditional odds ratio or marginal odds ratio should be used in meta-analysis involving both paired and unpaired binary data. Although statistically sound, both approaches result in overall odds ratios which are known to be less meaningful to consumers., Purpose: To show that while two odds ratios can be calculated in a pair-matched study, there is only one relative risk for such design, and to discuss the implications for meta-analysis involving both paired and unpaired binary data., Methods: Algebra and an example, along with standard software for implementing relative risk regression models., Results: The choice of relative risk as the effect measure in pair-matched design not only simplifies analysis and interpretation for individual studies, but makes mata-analysis involving both paired and unpaired studies straightforward. Pooling marginal odds ratios in a meta-analysis of diabetic retinopathy treatment resulted in a summarized odds ratio of 2.25 (95% CI 1.83-2.75), compared with that of 2.44 (95% CI 1.95-3.04) from pooling conditional odds ratios. In contrast, summarizing relative risks resulted in an overall effect measure of 1.09 (95% CI 1.06-1.11), implying the treatment reduces visual deterioration rate by 9%., Conclusion: Relative risk may be the first consideration in measuring effect for analyzing prospective studies with binary outcomes.

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.

Background: Cluster randomization trials in which intact social units are randomly assigned to different intervention groups have become very popular in recent years, particularly for the evaluation of innovations in the delivery of health care. An extensive literature dealing with the associated methodological challenges has also appeared. Although the monitoring of such trials using formal stopping rules is clearly indicated when the outcomes are irreversible and individual-level data are available sequentially, simple and reliable statistical methods that may be used for this purpose are currently not available., Purpose: To investigate the validity of standard group sequential methods when applied to cluster randomization trials having binary outcomes., Methods: The large sample distributions for each of five test statistics computed from sequentially accumulated data are derived. A simulation study is performed to evaluate the finite sample properties of these statistics when applied to the interim analysis of cluster randomization trials. Data from the World Health Organization antenatal care trial are used to illustrate the methods., Results: Each of the joint distributions is shown to be characterized by a covariance structure that asymptotically satisfies an independent increments structure, a foundation that simplifies group sequential methods. The simulation study reveals that four of the five test statistics evaluated provide satisfactory performance with as few as 10 clusters allocated to each of two interventions., Limitations: The applicability of our results to effect estimation following a group sequential cluster randomization trial is not investigated, although a theoretical foundation which may be used for this purpose is presented., Conclusions: Standard group sequential methods can be applied to cluster randomization trials when interim analyses are warranted.