Accurate Inference for Repeated Measures in High Dimensions

This paper proposes inferential methods for high-dimensional repeated measures in factorial designs. High-dimensional refers to the situation where the dimension is growing with sample size such that either one could be larger than the other. The most important contribution relates to high-accuracy of the methods in the sense that p-values, for example, are accurate up to the second-order. Second-order accuracy in sample size as well as dimension is achieved by obtaining asymptotic expansion of the distribution of the test statistics, and estimation of the parameters of the approximate distribution with second-order consistency. The methods are presented in a unified and succinct manner that it covers general factorial designs as well as any comparisons among the cell means. Expression for asymptotic powers are derived under two reasonable local alternatives. A simulation study provides evidence for a gain in accuracy and power compared to limiting distribution approximations and other competing methods for high-dimensional repeated measures analysis. The application of the methods are illustrated with a real-data from Electroencephalogram (EEG) study of alcoholic and control subjects

Asymptotics for testing hypothesis in some multivariate variance components model under non-normality

AbstractWe consider the problem of deriving the asymptotic distribution of the three commonly used multivariate test statistics, namely likelihood ratio, Lawley–Hotelling and Bartlett–Nanda–Pillai statistics, for testing hypotheses on the various effects (main, nested or interaction) in multivariate mixed models. We derive the distributions of these statistics, both in the null as well as non-null cases, as the number of levels of one of the main effects (random or fixed) goes to infinity. The robustness of these statistics against departure from normality will be assessed.Essentially, in the asymptotic spirit of this paper, both the hypothesis and error degrees of freedom tend to infinity at a fixed rate. It is intuitively appealing to consider asymptotics of this type because, for example, in random or mixed effects models, the levels of the main random factors are assumed to be a random sample from a large population of levels.For the asymptotic results of this paper to hold, we do not require any distributional assumption on the errors. That means the results can be used in real-life applications where normality assumption is not tenable.As it happens, the asymptotic distributions of the three statistics are normal. The statistics have been found to be asymptotically null robust against the departure from normality in the balanced designs. The expressions for the asymptotic means and variances are fairly simple. That makes the results an attractive alternative to the standard asymptotic results. These statements are favorably supported by the numerical results

High-Dimensional Repeated Measures

Recently, new tests for main and simple treatment effects, time effects, and treatment by time interactions in possibly high-dimensional multigroup repeated-measures designs with unequal covariance matrices have been proposed. Technical details for using more than one between-subject and more than one within-subject factor are presented in this article. Furthermore, application to electroencephalography (EEG) data of a neurological study with two whole-plot factors (diagnosis and sex) and two subplot factors (variable and region) is shown with the R package HRM (high-dimensional repeated measures)

Nonparametric Inference for Multivariate Data: The R Package npmv

We introduce the R package npmv that performs nonparametric inference for the comparison of multivariate data samples and provides the results in easy-to-understand, but statistically correct, language. Unlike in classical multivariate analysis of variance, multivariate normality is not required for the data. In fact, the different response variables may even be measured on different scales (binary, ordinal, quantitative). p values are calculated for overall tests (permutation tests and F approximations), and, using multiple testing algorithms which control the familywise error rate, significant subsets of response variables and factor levels are identified. The package may be used for low- or highdimensional data with small or with large sample sizes and many or few factor levels

Mood Management Intervention for College Smokers with Elevated Depressive Symptoms: A Pilot Study

Objective This pilot study examined smoking reduction and cessation among college smokers with elevated depressive symptomatology participating in a group-based behavioral counseling, mood management, and motivational enhancement combined intervention (CBT). Participants & Methods Fifty-eight smokers (smoked ≥ 6 days in the past 30) were randomized to six sessions of CBT (n=29) or a nutrition-focused attention-matched control group (CG, n=29). Results Relative to CG participants, significantly more CBT participants reduced smoking intensity by 50% (χ2(1, N=58)=4.86, p=.028) at end of treatment. Although CBT participants maintained smoking reductions at 3- and 6-month follow-up, group differences were no longer significant. No group differences in cessation emerged. Finally, participants in both groups evidenced increased motivation to reduce smoking at end of treatment (F(1, 44)=11.717, p=.001, ηp2=.207). Conclusions Findings demonstrate the utility of this intervention for smoking reduction and maintenance of reductions over time among a population of college students with elevated depressive symptomatology

A Roadmap for Building Data Science Capacity for Health Discovery and Innovation in Africa

Technological advances now make it possible to generate diverse, complex and varying sizes of data in a wide range of applications from business to engineering to medicine. In the health sciences, in particular, data are being produced at an unprecedented rate across the full spectrum of scientific inquiry spanning basic biology, clinical medicine, public health and health care systems. Leveraging these data can accelerate scientific advances, health discovery and innovations. However, data are just the raw material required to generate new knowledge, not knowledge on its own, as a pile of bricks would not be mistaken for a building. In order to solve complex scientific problems, appropriate methods, tools and technologies must be integrated with domain knowledge expertise to generate and analyze big data. This integrated interdisciplinary approach is what has become to be widely known as data science. Although the discipline of data science has been rapidly evolving over the past couple of decades in resource-rich countries, the situation is bleak in resource-limited settings such as most countries in Africa primarily due to lack of well-trained data scientists. In this paper, we highlight a roadmap for building capacity in health data science in Africa to help spur health discovery and innovation, and propose a sustainable potential solution consisting of three key activities: a graduate-level training, faculty development, and stakeholder engagement. We also outline potential challenges and mitigating strategies

The nonparametric Behrens‐Fisher problem with dependent replicates

Purely nonparametric methods are developed for general two-sample problems in which each experimental unit may have an individual number of possibly correlated replicates. In particular, equality of the variances, or higher moments, of the distributions of the data is not assumed, even under the null hypothesis of no treatment effect. Thus, a solution for the so-called nonparametric Behrens-Fisher problem is proposed for such models. The methods are valid for metric, count, ordered categorical, and even dichotomous data in a unified way. Point estimators of the treatment effects as well as their asymptotic distributions will be studied in detail. For small sample sizes, the distributions of the proposed test statistics are approximated using Satterthwaite-Welch-type t-approximations. Extensive simulation studies show favorable performance of the new methods, in particular, in small sample size situations. A real data set illustrates the application of the proposed methods

Analysis of Smoking Patterns and Contexts Among College Student Smokers

Many who smoke in college do so infrequently and smoking conditions are not well-understood. We examined smoking patterns among college fraternity and sorority members (N=207) from a Midwestern university in three successive fall semesters in 2006–2008. Participants completed calendar-assisted retrospective assessments of 30-day smoking at up to 5 assessment points over 96 days. Overall smoking rates declined over the course of each semester and higher smoking on weekends was observed, with more variability among daily smokers. The most frequent categories of events to cue recall of smoking were socializing, work, and school. Findings can be used to target prevention efforts

Strategies to Recruit and Retain College Smokers in Cessation Trials

Techniques to recruit and retain college fraternity and sorority members who reported past 30-day smoking into a cessation trial are described. Recruitment efforts included relationship-building, raffles, and screening survey administration during existing meetings. Surveys were administered to 76% (n = 3,276) of members in 30 chapters, 79% of eligible members agreed to participate, and 76% of those completed assessments and were enrolled in the trial (n = 452). The retention rate was 73%. Retention efforts included cash incentives, flexible scheduling, multiple reminders, chapter incentives, and use of chapter members as study personnel. Retention was not related to demographic, behavioral, or group characteristics. The strategies of partnership, convenience, and flexibility appear effective and may prove useful to investigators recruiting similar samples

Motivational Interviewing for Smoking Cessation in College Students: A Group Randomized Controlled Trial

Objective—To examine the efficacy of four individually-delivered Motivational Interviewing counseling sessions for smoking cessation versus a matched intensity comparison condition. Method—From 2006–2009, students attending college in the Midwest smoking at least 1 of 30 days were recruited regardless of their interest in quitting. 30 fraternities and sororities were randomized, resulting in 452 participants. Results—No significant differences were found for 30-day cessation between treatment and comparison at end of treatment (31.4% vs 28%, OR=1.20, 95% CI .72,1.99) or at follow-up (20.4% vs 24.6%, OR=.78, 95% CI .50,1.22). Predictors of cessation at follow-up, regardless of condition, included more sessions attended (OR 1.2, 95% CI 1.1,1.8) and more cigarettes smoked in 30 days at baseline (OR 4.7, 95% CI 2.5,8.9). The odds of making at least one quit attempt were significantly greater for those in the smoking group at end of treatment (OR 1.75, 95% CI 1.11,2.74) and followup (OR 1.66, 95% CI 1.11,2.47). Modeling showed reduction in days smoked for both groups. At end of treatment, more frequent smokers in the treatment condition had greater reductions in days smoked. Conclusion—Motivational Interviewing for smoking cessation is effective for increasing cessation attempts and reducing days smoked in the short run