Methadone, a widely prescribed medication for chronic pain and opioid addiction, is associated with respiratory depression and increased predisposition for torsades de pointes, a potentially fatal arrhythmia. Most methadone-related deaths occur during sleep. The objective of this study was to determine whether methadone's arrhythmogenic effects increase during sleep, with a focus on cardiac repolarization instability using QT variability index (QTVI), a measure shown to predict arrhythmias and mortality. Sleep study data of 24 patients on chronic methadone therapy referred to a tertiary clinic for overnight polysomnography were compared with two matched groups not on methadone: 24 patients referred for overnight polysomnography to the same clinic (clinic group), and 24 volunteers who had overnight polysomnography at home (community group). Despite similar values for heart rate, heart rate variability, corrected QT interval, QTVI, and oxygen saturation (SpO2 ) when awake, patients on methadone had larger QTVI (P = 0.015 vs. clinic, P < 0.001 vs. community) and lower SpO2 (P = 0.008 vs. clinic, P = 0.013 vs. community) during sleep, and the increase in their QTVI during sleep vs. wakefulness correlated with the decrease in SpO2 (r = -0.54, P = 0.013). QTVI positively correlated with methadone dose during sleep (r = 0.51, P = 0.012) and wakefulness (r = 0.73, P < 0.001). High-density ectopy (> 1,000 premature beats per median sleep period), a precursor for torsades de pointes, was uncommon but more frequent in patients on methadone (P = 0.039). This study demonstrates that chronic methadone use is associated with increased cardiac repolarization instability. Methadone's pro-arrhythmic impact may be mediated by sleep-related hypoxemia, which could explain the increased nocturnal mortality associated with this opioid.

The number of studies where the primary measurement is a matrix is exploding. In response to this, we propose a statistical framework for modeling populations of repeatedly observed matrix-variate measurements. The 2D structure is handled via a matrix-variate distribution with decomposable row/column-specific covariance matrices and a linear mixed effect framework is used to model the multilevel design. The proposed framework flexibly expands to accommodate many common crossed and nested designs and introduces two important concepts: the between-subject distance and intraclass correlation coefficient, both defined for matrix-variate data. The computational feasibility and performance of the approach is shown in extensive simulation studies. The method is motivated by and applied to a study that monitored physical activity of individuals diagnosed with congestive heart failure (CHF) over a 4- to 9-month period. The long-term patterns of physical activity are studied and compared in two CHF subgroups: with and without adverse clinical events. Supplementary materials for this article, that include de-identified accelerometry and clinical data, are available online.

Big Data is increasingly prevalent in science and data analysis. We provide a short tutorial for adapting to these changes and making the necessary adjustments to the academic culture to keep Biostatistics truly impactful in scientific research.

We present methods for modeling and estimation of a concurrent functional regression when the predictors and responses are two-dimensional functional datasets. The implementations use spline basis functions and model fitting is based on smoothing penalties and mixed model estimation. The proposed methods are implemented in available statistical software, allow the construction of confidence intervals for the bivariate model parameters, and can be applied to completely or sparsely sampled responses. Methods are tested to data in simulations and they show favorable results in practice. The usefulness of the methods is illustrated in an application to environmental data.

Inference methods are proposed for the bivariate mean function of a continuous stochastic process with a two-dimensional domain. Nonparametric bivariate estimation is facilitated by thresholded projection estimators. Estimators adapt to the sparsity of the bivariate function. Oracle inequality results are developed to describe the adaptive inference methods. The construction of nonparametric bivariate confidence bands is presented. Implementation results show the applicability of the methods in practice.

The purpose of the study was to identify demographic variables associated with women's body mass index (BMI) and body size perceptions and to determine if BMI affects body size perception. SizeUSA data (n = 6,811) were used for the data analysis. A multinomial regression between demographic variables, BMI, and body size perception showed that each demographic variable was a significant predictor of a women's BMI and body size perception. Results can be used to understand the BMI and body size perceptions of female consumers. Retailers can use the results to customize merchandise assortments for each store based on their target market's demographics.

We introduce a class of dynamic regression models designed to predict the future of growth curves based on their historical dynamics. This class of models incorporates both baseline and time-dependent covariates, start with simple regression models and build up to dynamic function-on-function regressions. We compare the performance of the dynamic prediction models in a variety of signal-to-noise scenarios and provide practical solutions for model selection. We conclude that (a) prediction performance increases substantially when using the entire growth history relative to using only the last and first observation; (b) smoothing incorporated using functional regression approaches increases prediction performance; and (c) the interpretation of model parameters is substantially improved using functional regression approaches. Because many growth curve datasets exhibit missing and noisy data, we propose a bootstrap of subjects approach to account for the variability associated with the missing data imputation and smoothing. Methods are motivated by and applied to the CONTENT dataset, a study that collected monthly child growth data on 197 children from birth until month 15. R code describing the fitting approaches is provided in a supplementary file.

BACKGROUNDThe purpose of the study was to examine the effects of the Yes You Can! (YYC) curriculum on sexual knowledge and behavioral intent of program participants.METHODSParticipants included students ages 10-14 from schools in a northeast US urban area. Yes You Can! program lessons were designed to support healthy relationships. The curriculum was taught by trained instructors. The testing instrument was a 30-item questionnaire, which included sexual knowledge and intent items. Students completed the questionnaire before program implementation, immediately following intervention, and a third time at follow-up. Data were analyzed using analysis of covariance. Pretest knowledge scores were used as the covariate for the knowledge analyses. Pretest intent scores were used as the covariate for the intent analyses.RESULTSResults showed the intervention group had less intent to engage in sexual intercourse than the control group at post-test (p < .001) and at follow-up (p < .001). Similarly, the intervention group had higher knowledge scores than the control group at post-test (p < .001) and at follow-up (p < .001).CONCLUSIONSResults indicate that the YYC program had a statistically significant, positive impact on knowledge and sexual intent. These variables are important precursors to actual behavior. Future research should examine the effects of the program on changes in sexual behavior.

Abstract Background Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. Methods We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Results Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Conclusions Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

Deriving statistical models to predict one variable from one or more other variables, or predictive modeling, is an important activity in obesity and nutrition research. To determine the quality of the model, it is necessary to quantify and report the predictive validity of the derived models. Conducting validation of the predictive measures provides essential information to the research community about the model. Unfortunately, many articles fail to account for the nearly inevitable reduction in predictive ability that occurs when a model derived on one data set is applied to a new data set. Under some circumstances, the predictive validity can be reduced to nearly zero. In this overview, we explain why reductions in predictive validity occur, define the metrics commonly used to estimate the predictive validity of a model (for example, coefficient of determination (R(2)), mean squared error, sensitivity, specificity, receiver operating characteristic and concordance index) and describe methods to estimate the predictive validity (for example, cross-validation, bootstrap, and adjusted and shrunken R(2)). We emphasize that methods for estimating the expected reduction in predictive ability of a model in new samples are available and this expected reduction should always be reported when new predictive models are introduced.

A general framework for smooth regression of a functional response on one or multiple functional predictors is proposed. Using the mixed model representation of penalized regression expands the scope of function-on-function regression to many realistic scenarios. In particular, the approach can accommodate a densely or sparsely sampled functional response as well as multiple functional predictors that are observed on the same or different domains than the functional response, on a dense or sparse grid, and with or without noise. It also allows for seamless integration of continuous or categorical covariates and provides approximate confidence intervals as a by-product of the mixed model inference. The proposed methods are accompanied by easy to use and robust software implemented in the pffr function of the R package refund. Methodological developments are general, but were inspired by and applied to a diffusion tensor imaging brain tractography dataset.

BACKGROUNDCurrently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds.OBJECTIVEThe objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1.DESIGNData consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing <5% of body weight in 1 y. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds were calculated for each model. The AUC statistic quantified the ROC curve's capacity to classify participants likely to lose <5% of their body weight at the end of 1 y. The models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated.RESULTSOptimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50).CONCLUSIONSThe newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention.

Facilitative linguistic input directly connected to children’s interest and focus of attention has become a recommended component of interventions for young children with autism spectrum disorder (ASD). This longitudinal correlational study used two assessment time points and examined the association between parental undemanding topic-continuing talk related to the child’s attentional focus (i.e., follow-in comments) and later receptive language for 37 parent–child dyads with their young (mean = 21 months, range 15–24 months) children with autism symptomology. The frequency of parental follow-in comments positively predicted later receptive language after considering children’s joint attention skills and previous receptive language abilities.

BACKGROUND:Changes in Quality of Life (QOL) measures over time with treatment of obesity have not previously been described for youth. We describe the changes from baseline through two follow up visits in youth QOL (assessed by the Pediatric Quality Life Inventory, PedsQL4.0), teen depression (assessed by the Patient Health Questionnaire, PHQ9A), Body Mass Index (BMI) and BMI z-score. We also report caregiver proxy ratings of youth QOL.METHODS:A sample of 267 pairs of youth and caregiver participants were recruited at their first visit to an outpatient weight-treatment clinic that provides care integrated between a physician, dietician, and mental health provider; of the 267, 113 attended a visit two (V2) follow-up appointment, and 48 attended visit three (V3). We investigated multiple factors longitudinally experienced by youth who are overweight and their caregivers across up to three different integrated care visits. We determined relationships at baseline in QOL, PHQ9A, and BMI z-score, as well as changes in variables over time using linear mixed models with time as a covariate.RESULTS:Overall across three visits the results indicate that youth had slight declines in relative BMI, significant increases in their QOL and improvements in depression.CONCLUSIONS:We encourage clinicians and researchers to track youth longitudinally throughout treatment to investigate not only youth's BMI changes, but also psychosocial changes including QOL.

In this paper we study a bivariate smoothing approach for estimating multiple functional parameters for functional data with a two-dimensional domain.We present a penalized regression framework for smoothing with the purpose to: (a) facilitate the estimation of the smooth overall bivariate mean function of two-dimensional functional data, (b) enable the estimation of the functional effect of a scalar covariate, (c) accommodate completely or incompletely sampled data, (d) implement the fitting approach using available statistical software, and (e) construct pointwise approximate confidence intervals for multiple bivariate functional parameters.Implementation results from simulation studies show that these methods perform very well in practice.We illustrate the usefulness of the bivariate smoothing approach to several real datasets, including applications to electricity demand.

The women's plus-size apparel category is a growing market segment in the USA as more than two-thirds (64%) of American women are considered either overweight or obese (Flegal et al., 2010 Flegal, K. M. 2010. Prevalence and trends in obesity among US adults, 1999–2008. Journal of the American Medical Association, 303(3): 235–241. Available from: http://jama.ama-assn.org/cgi/content/full/303/3/235 [Accessed 6 July 2010][Crossref], [PubMed], [Web of Science ®] , [Google Scholar]. Prevalence and trends in obesity among US adults, 1999–2008. Journal of the American Medical Association, 303 (3), 235–241. Available from: http://jama.ama-assn.org/cgi/content/full/303/3/235 [Accessed 6 July 2010]). Pisut and Connell (2007 Pisut, G. and Connell, L. J. 2007. Fit preferences of female consumers in the USA. Journal of Fashion Marketing and Management, 11(3): 366–379. [Crossref] , [Google Scholar]. Fit preferences of female consumers in the USA. Journal of Fashion Marketing and Management, 11 (3), 366–379) and Simmons et al. (2004 Simmons, K. P., Istook, C. L. and Devarajan, P. 2004. Female figure identification technique (FFIT) for apparel, part II: development of shape sorting software. Journal of Textile and Apparel Technology and Management, 4(1): 1–16. [Google Scholar]. Female figure identification technique (FFIT) for apparel, part II: development of shape sorting software. Journal of Textile and Apparel Technology and Management, 4 (1)) reported that women today reflect a more pear-shaped silhouette than in previous decades. Kurt Salmon Associates (2000 Kurt Salmon Associates. Annual consumer outlook survey. Paper presented at the American Apparel and Footwear Association apparel research committee. Orlando, FL. [Google Scholar]. Annual consumer outlook survey. Paper presented at the American Apparel and Footwear Association apparel research committee, Orlando, FL) reported that more than half the women have trouble finding well-fitting clothes. Consumers' figure types play a significant role in affecting sizing measurements of apparel (Njagi, R.K. and Zwane, P.E., 2011. Variation in measurements across different brands of same style ladies' pants in Swaziland. International Journal of Fashion Design, Technology, and Education, 4 (1), 51–57). Apparel patterns are made for hourglass-shaped women and are graded from an average size, assuming that women's measurements increase proportionally as size increases. Based on SizeUSA, a national dataset, women were classified into each size category based on their bust, waist and hip measurements, and their measurements were compared to American Society of Testing and Materials (ASTM) standards. Results showed a significant difference for most size categories. Within each size category, fewer participants satisfied all three measurements of bust, waist and hips. From these measurements, women's hip shape was determined and evaluated for sizes 14W–32W. Analysis of hip shape revealed that different hip shapes exists within a given apparel size. Research methods and implications are discussed. Keywords: womenplus-size clothingUSAapparel sizinghip shape

OBJECTIVE:This pilot study examined the feasibility of (1) conducting interdisciplinary fall risk screens at a communitywide adult fall prevention event and (2) collecting preliminary follow-up data from people screened at the event about balance confidence and home and activity modifications made after receiving educational information at the event. METHOD:We conducted a pilot study with pre- and posttesting (4-mo follow-up) with 35 community-dwelling adults ≥55 yr old. RESULTS:Approximately half the participants were at risk for falls. Most participants who anticipated making environmental or activity changes to reduce fall risk initiated changes (n = 8/11; 72.7%) during the 4-mo follow-up period. We found no significant difference in participants' balance confidence between baseline (median = 62.81) and follow-up (median = 64.06) as measured by the Activities-specific Balance Confidence scale. CONCLUSION:Conducting interdisciplinary fall risk screens at an adult fall prevention event is feasible and can facilitate environmental and behavior changes to reduce fall risk.

We propose and analyse fully data-driven methods for inference about the mean function of a Gaussian process from a sample of independent trajectories of the process, observed at random time points and corrupted by additive random error. Our methods are based on thresholded least squares estimators relative to an approximating function basis. The variable threshold levels are determined from the data and the resulting estimates adapt to the unknown sparsity of the mean function relative to the approximating basis. These results are obtained via novel oracle inequalities, which are further used to derive the rates of convergence of our mean estimates. In addition, we construct confidence balls that adapt to the unknown regularity of the mean and covariance function of the stochastic process. They are easy to compute since they do not require explicit estimation of the covariance operator of the process. A simulation study shows that the new method performs very well in practice and is robust against large variations that may be introduced by the random-error terms.

PURPOSE: Mallampati score is often used as a tool to predict the likelihood of obstructive sleep apnea (OSA). However studies have revealed cranio-facial differences between Caucasians and African-Americans. This study attempts to look at the effectiveness of modified mallampati score in predicting OSA in African-American (AA) population.

This paper proposes and analyzes fully data driven methods for inference about the mean function of a stochastic process from a sample of independent trajectories of the process, observed at discrete time points and corrupted by additive random error. The proposed method uses thresholded least squares estimators relative to an approximating function basis. The variable threshold levels are estimated from the data and the basis is chosen via cross-validation from a library of bases. The resulting estimates adapt to the unknown sparsity of the mean function relative to the selected approximating basis, both in terms of the mean squared error and supremum norm. These results are based on novel oracle inequalities. In addition, uniform confidence bands for the mean function of the process are constructed. The bands also adapt to the unknown regularity of the mean function, are easy to compute, and do not require explicit estimation of the covariance operator of the process. The simulation study that complements the theoretical results shows that the new method performs very well in practice, and is robust against large variations introduced by the random error terms.