Robust tests for multivariate repeated measures with small samples.

Multivariate repeated measures data naturally arise in clinical trials and other fields such as biomedical science, public health, agriculture, social science and so on. For data of this type, the classical approach is to conduct multivariate analysis of variance (MANOVA) based on Wilks' Lambda and other multivariate statistics, which require the assumptions of multivariate normality and homogeneity of within-cell covariance matrices. However, data being analyzed nowadays show marked departure from multivariate normality and homogeneity.

Estimation of multivariate treatment effects in contaminated clinical trials.

The paper addresses estimating and testing treatment effects with multivariate outcomes in clinical trials where imperfect diagnostic devices are used to assign subjects to treatment groups. The paper focuses on the pre-post design and proposes two novel methods for estimating and testing treatment effects. In addition, methods for sample size and power calculations are developed.

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.

Multivariate nonparametric methods in two-way balanced designs: performances and limitations in small samples.

Investigations of multivariate population are pretty common in applied researches, and the two-way crossed factorial design is a common design used at the exploratory phase in industrial applications. When assumptions such as multivariate normality and covariance homogeneity are violated, the conventional wisdom is to resort to nonparametric tests for hypotheses testing. In this paper we compare the performances, and in particular the power, of some nonparametric and semi-parametric methods that have been developed in recent years.

The nonparametric Behrens-Fisher problem in partially complete clustered data.

In randomized trials or observational studies involving clustered units, the assumption of independence within clusters is not practical. Existing parametric or semiparametric methods assume specific dependence structures within a cluster. Furthermore, parametric model assumptions may not even be realistic when data are measured in a nonmetric scale as commonly happens, for example, in quality-of-life outcomes.

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.

Randomized Trial of Interventions to Improve Childhood Asthma in Homes with Wood-burning Stoves.

Background: Household air pollution due to biomass combustion for residential heating adversely affects vulnerable populations. Randomized controlled trials to improve indoor air quality in homes of children with asthma are limited, and no such studies have been conducted in homes using wood for heating.

Objectives: Our aims were to test the hypothesis that household-level interventions, specifically improved-technology wood-burning appliances or air-filtration devices, would improve health measures, in particular Pediatric Asthma Quality of Life Questionnaire (PAQLQ) scores, relative to placebo, among children living with asthma in homes with wood-burning stoves.

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).

Task-induced Changes in Idiopathic Infantile Nystagmus Vary with Gaze.

Purpose: Investigations of infantile nystagmus syndrome (INS) at center or at the null position have reported that INS worsens when visual demand is combined with internal states, e.g. stress.

High-Dimensional Multivariate Repeated Measures Analysis with Unequal Covariance Matrices.

In this paper, test statistics for repeated measures design are introduced when the dimension is large. By large dimension is meant the number of repeated measures and the total sample size grow together but either one could be larger than the other. Asymptotic distribution of the statistics are derived for the equal as well as unequal covariance cases in the balanced as well as unbalanced cases.

Inference for low- and high-dimensional multigroup repeated measures designs with unequal covariance matrices.

We propose tests for main and simple treatment effects, time effects, as well as treatment by time interactions in possibly high-dimensional multigroup repeated measures designs. The proposed inference procedures extend the work by Brunner et al. (2012) from two to several treatment groups and remain valid for unbalanced data and under unequal covariance matrices.

Health risk perceptions predict smoking-related outcomes in Greek college students.

Health risk perception in smoking behavior was prospectively evaluated in a cluster-randomized trial for smoking cessation in Greek college students. Perceived Vulnerability (PV), Precaution Effectiveness, Optimistic Bias, and smoking behavior measures (quit attempts and cessation) were assessed in college-aged Greek student smokers at baseline, end of treatment (3 months), and follow-up (6 months). Using generalized estimating equations, baseline risk perception variables and change in risk perception variables between baseline and end of treatment were examined as predictors of the dichotomous smoking outcome variables.

Accurate mean comparisons for paired samples with missing data: an application to a smoking-cessation trial.

In this paper, we consider mean comparisons for paired samples in which a certain portion of the observations are missing. This type of data commonly arises in medical researches where the outcomes are assessed at two time points after the application of treatments. New methods for statistical inference are proposed by making finiteness correction based on asymptotic expansions of some intuitive statistics.

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 And Methods: Fifty-eight smokers (smoked 6 days in the past 30) were randomized to 6 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% (χ(2)[1, N = 58] = 4.

Gene set analysis for longitudinal gene expression data.

Background: Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. GSA performs statistical tests for independent microarray samples at the level of gene sets rather than individual genes. Nowadays, an increasing number of microarray studies are conducted to explore the dynamic changes of gene expression in a variety of species and biological scenarios.

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 five assessment points over 96 days.

Enforcing an outdoor smoking ban on a college campus: effects of a multicomponent approach.

Objectives: Data on effective strategies to enforce policies banning outdoor smoking are sparse. This study tested the effects of an enforcement package implemented on a college campus.

Participants: Thirty-nine observers recorded compliance of 709 outside smokers.

Some contributions to the analysis of multivariate data.

In this paper, we provide an overview of recently developed methods for the analysis of multivariate data that do not necessarily emanate from a normal universe. Multivariate data occur naturally in the life sciences and in other research fields. When drawing inference, it is generally recommended to take the multivariate nature of the data into account, and not merely analyze each variable separately.

Examination of a brief Smoking Consequences Questionnaire for college students.

This study examined the factor structure of a brief version of the Smoking Consequences Questionnaire-Adult (SCQ-A) among 315 college freshman and sophomore smokers. A comparison of results from two confirmatory factor analyses demonstrated that a nine-factor model provided superior fit to a four-factor model. Furthermore, results revealed a lack of factorial invariance of factor loadings for daily and nondaily smokers, and of latent mean structures for smoking category and gender.