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Background: Evidence for a relationship between oxidative stress and osteoporotic fractures in humans is limited. Fluorescent oxidation products (FlOPs, excitation/emission wavelengths 320/420nm denoted FlOP_320; 360/420nm [FlOP_360]; and 400/475nm [FlOP_400]) are global biomarkers of oxidative stress, and reflect oxidative damage to proteins, phospholipids, and nucleic acids. We investigated the association between FlOPs and a recent osteoporotic fracture. Methods: We conducted a case-control study in a Chinese population aged 50 years or older. A recent osteoporotic fracture in the cases was confirmed by x-ray. Cases were matched with community-based non-fracture controls (1:2 ratio) for age (± 4 years) and sex. In addition, we conducted a sensitivity unmatched casecontrol study which included all fracture cases and all eligible non-fracture controls prior to matching. Plasma FlOPs were measured with a fluorescent microplate reader. We used unconditional logistic regression to analyze the association between FlOPs (per 1-SD increase in logarithmic scale) and fracture; odds ratios (OR) and 95% confidence intervals (95% CI) were reported. Results: Forty-four cases and 88 matched controls (mean age: 68.2 years) were included. After covariate adjustment (i.e., body mass index, physical activity, and smoking), higher FlOP_360 (OR = 1.85; 95% CI = 1.03 - 3.34) and FlOP_400 (OR = 13.29; 95% CI = 3.48 - 50.69) levels, but not FlOP_320 (OR = 0.56; 95% CI = 0.27 - 1.15), were associated with increased fracture risk. Subgroup analyses by fracture site and unmatched case-control study found comparable associations of FlOP_360 and FlOP_400 with hip and non-hip fractures. Conclusions: Higher FlOP_360 and FlOP_400 levels were associated with increased risk of fracture, and this association was comparable for hip and non-hip fractures. Prospective studies are warranted to confirm this finding. [ABSTRACT FROM AUTHOR]

Objective COPD is a high-cost disease and results in frequent contacts with the healthcare system. The study objective was to compare the accuracy of classification models with different covariates for classifying COPD patients into cost groups. Methods Linked health administrative databases from Saskatchewan, Canada, were used to identify a cohort of newly diagnosed COPD patients (April 1, 2007 to March 31, 2011) and their episodes of healthcare encounters for disease exacerbations. Total costs of the first and follow-up episodes were computed and patients were categorized as persistently high cost, occasionally high cost, and persistently low cost based on cumulative cost distribution ranking using the 75th percentile cutoff for high-cost status. Classification accuracy was compared for seven multinomial logistic regression models containing socio-demographic characteristics (i.e., base model), and socio-demographic and prior healthcare use characteristics (i.e., comparator models). Results Of the 1182 patients identified, 8.5% were classified as persistently high cost, 26.1% as occasionally high cost, and the remainder as persistently low cost. The persistently high-cost and occasionally high-cost patients incurred 10 times (\$12 449 vs \$1263) and seven times (\$9334 vs \$1263) more costs in their first exacerbation episode than persistently low-cost patients, respectively. Classification accuracy was 0.67 for the base model, whereas the comparator model containing socio-demographic and number of prior hospital admissions had the highest accuracy (0.72). Conclusions Costs associated with COPD exacerbation episodes are substantial. Adding prior hospitalization to socio-demographic characteristics produced the highest improvements in classification accuracy. Accurate classification models are important for identifying potential healthcare cost management strategies.

This study investigates procedures for controlling the familywise error rate (FWR) when testing hypotheses about multiple, correlated outcome variables in repeated measures (RM) designs. A content analysis of RM research articles published in 4 psychology journals revealed that 3 quarters of studies tested hypotheses about 2 or more outcome variables. Several procedures originally proposed for testing multiple outcomes in 2-group designs are extended to 2-group RM designs. The investigated procedures include 2 modified Bonferroni procedures that adjust the level of significance, alpha, for the effective number of outcomes and a permutation step-down (PSD) procedure. The FWR, any-variable power, and all-variable power are investigated in a Monte Carlo study. One modified Bonferroni procedure frequently resulted in inflated FWRs, whereas the PSD procedure controlled the FWR. The PSD procedure could be substantially more powerful than the conventional Bonferroni procedure, which does not account for dependencies among the outcome variables. However, the difference in power between the PSD procedure, which does account for these dependencies, and Hochberg's step-up procedure, which does not, were negligible. A numeric example illustrates implementation of these multiple-testing procedures. (Contains 6 tables.)

This study considers the problem of testing the difference between treatment and control groups on m [greater than or equal to] 2 measures when it is assumed a priori that the treatment group will perform better than the control group on all measures. Two procedures are investigated that do not rest on the assumptions of covariance homogeneity or multivariate normality: a likelihood ratio test based on a bootstrap critical value and a composite step-down procedure based on trimmed means. Type I error rates of both procedures are insensitive to assumption violations. Procedures that test a directional alternative hypothesis can be substantially more powerful than a procedure that tests a nondirectional hypothesis for certain configurations of the population mean vectors. The differences in average power of the investigated procedures are a function of the configuration of the population means, the magnitude of correlation among the outcome measures, and the shape of the population distribution. (Contains 4 tables.)

Standard least squares analysis of variance methods suffer from poor power under arbitrarily small departures from normality and fail to control the probability of a Type I error when standard assumptions are violated. This article describes a framework for robust estimation and testing that uses trimmed means with an approximate degrees of freedom heteroscedastic statistic for independent and correlated groups designs in order to achieve robustness to the biasing effects of nonnormality and variance heterogeneity. The authors describe a nonparametric bootstrap methodology that can provide improved Type I error control. In addition, the authors indicate how researchers can set robust confidence intervals around a robust effect size parameter estimate. In an online supplement, the authors use several examples to illustrate the application of an SAS program to implement these statistical methods. (Contains 4 tables, 1 figure, and 24 footnotes.)