A unified approach to modeling multivariate binary data using copulas over partitions

Many seemingly disparate approaches for marginal modeling have been developed in recent years. We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the proposed copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts.

On Time Series Analysis of Public Health and Biomedical Data

A time series is a sequence of observations made over time. Examples in public health include daily ozone concentrations, weekly admissions to an emergency department or annual expenditures on health care in the United States. Time series models are used to describe the dependence of the response at each time on predictor variables including covariates and possibly previous values in the series. Time series methods are necessary to account for the correlation among repeated responses over time. This paper gives an overview of time series ideas and methods used in public health research.

Systemic analysis of microarray data sets towards identifying genes regulating root development

Nitrogen and water are essential for plant growth and development. In this study, we designed experiments to produce gene expression data of poplar roots under nitrogen starvation and water deprivation conditions. We found low concentration of nitrogen led first to increased root elongation followed by lateral root proliferation and eventually increased root biomass. To identify genes regulating root growth and development under nitrogen starvation and water deprivation, we designed a series of data analysis procedures, through which, we have successfully identified biologically important genes. Differentially Expressed Genes (DEGs) analysis identified the genes that are differentially expressed under nitrogen starvation or drought. Protein domain enrichment analysis identified enriched themes (in same domains) that are highly interactive during the treatment. Gene Ontology (GO) enrichment analysis allowed us to identify biological process changed during nitrogen starvation. Based on the above analyses, we examined the local Gene Regulatory Network (GRN) and identified a number of transcription factors. After testing, one of them is a high hierarchically ranked transcription factor that affects root growth under nitrogen starvation. It is very tedious and time-consuming to analyze gene expression data. To avoid doing analysis manually, we attempt to automate a computational pipeline that now can be used for identification of DEGs and protein domain analysis in a single run. It is implemented in scripts of Perl and R.

DIMENSION REDUCTION FOR POWER SYSTEM MODELING USING PCA METHODS CONSIDERING INCOMPLETE DATA READINGS

Principal Component Analysis (PCA) is a popular method for dimension reduction that can be used in many fields including data compression, image processing, exploratory data analysis, etc. However, traditional PCA method has several drawbacks, since the traditional PCA method is not efficient for dealing with high dimensional data and cannot be effectively applied to compute accurate enough principal components when handling relatively large portion of missing data. In this report, we propose to use EM-PCA method for dimension reduction of power system measurement with missing data, and provide a comparative study of traditional PCA and EM-PCA methods. Our extensive experimental results show that EM-PCA method is more effective and more accurate for dimension reduction of power system measurement data than traditional PCA method when dealing with large portion of missing data set.

Common Mistakes When Fitting Models to Data

Dr. Rossi discusses the common errors that are made when fitting statistical models to data. Focuses on the planning, data analysis, and interpretation phases of a statistical analysis, and highlights the errors that are commonly made by researchers of these phases. The implications of these commonly made errors are discussed along with a discussion of the methods that can be used to prevent these errors from occurring. A prescription for carrying out a correct statistical analysis will be discussed.

Cluster randomized clinical trials in orthodontics: design, analysis and reporting issues

Cluster randomized trials (CRTs) use as the unit of randomization clusters, which are usually defined as a collection of individuals sharing some common characteristics. Common examples of clusters include entire dental practices, hospitals, schools, school classes, villages, and towns. Additionally, several measurements (repeated measurements) taken on the same individual at different time points are also considered to be clusters. In dentistry, CRTs are applicable as patients may be treated as clusters containing several individual teeth. CRTs require certain methodological procedures during sample calculation, randomization, data analysis, and reporting, which are often ignored in dental research publications. In general, due to similarity of the observations within clusters, each individual within a cluster provides less information compared with an individual in a non-clustered trial. Therefore, clustered designs require larger sample sizes compared with non-clustered randomized designs, and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this article to highlight with relevant examples the important methodological characteristics of cluster randomized designs as they may be applied in orthodontics and to explain the problems that may arise if clustered observations are erroneously treated and analysed as independent (non-clustered).

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Streaming Support for Data Intensive Cloud-Based Sequence Analysis

Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of “resources-on-demand” and “pay-as-you-go”, scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client’s site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation.

The timing of significant arch dimensional changes with fixed orthodontic appliances: data from a multicenter randomised controlled trial.

OBJECTIVES To identify the timing of significant arch dimensional increases during orthodontic alignment involving round and rectangular nickel-titanium (NiTi) wires and rectangular stainless steel (SS). A secondary aim was to compare the timing of changes occurring with conventional and self-ligating fixed appliance systems. METHODS In this non-primary publication, additional data from a multicenter randomised trial initially involving 96 patients, aged 16 years and above, were analysed. The main pre-specified outcome measures were the magnitude and timing of maxillary intercanine, interpremolar, and intermolar dimensions. Each participant underwent alignment with a standard Damon (Ormco, Orange, CA) wire sequence for a minimum of 34 weeks. Blinding of clinicians and patients was not possible; however, outcome assessors and data analysts were kept blind to the appliance type during data analysis. RESULTS Complete data were obtained from 71 subjects. Significant arch dimensional changes were observed relatively early in treatment. In particular, changes in maxillary inter-first and second premolar dimensions occurred after alignment with an 0.014in. NiTi wire (P<0.05). No statistical differences in transverse dimensions were found between rectangular NiTi and working SS wires for each transverse dimension (P>0.05). Bracket type had no significant effect on the timing of the transverse dimensional changes. CONCLUSIONS Arch dimensional changes were found to occur relatively early in treatment, irrespective of the appliance type. Nickel-titanium wires may have a more profound effect on transverse dimensions than previously believed. CLINICAL SIGNIFICANCE On the basis of this research orthodontic expansion may occur relatively early in treatment. Nickel-titanium wires may have a more profound effect on transverse dimensions than previously believed.

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^

Racial disparities in CD4 counts at initial HIV-1 diagnosis: Analysis of the Adult Spectrum of HIV disease dataset and public health implications

Introduction. The HIV/AIDS disease burden disproportionately affects minority populations, specifically African Americans. While sexual risk behaviors play a role in the observed HIV burden, other factors including gender, age, socioeconomics, and barriers to healthcare access may also be contributory. The goal of this study was to determine how far down the HIV/AIDS disease process people of different ethnicities first present for healthcare. The study specifically analyzed the differences in CD4 cell counts at the initial HIV-1 diagnosis with respect to ethnicity. The study also analyzed racial differences in HIV/AIDS risk factors. ^ Methods. This is a retrospective study using data from the Adult Spectrum of HIV Disease (ASD), collected by the City of Houston Department of Health. The ASD database contains information on newly reported HIV cases in the Harris County District Hospitals between 1989 and 2000. Each patient had an initial and a follow-up report. The extracted variables of interest from the ASD data set were CD4 counts at the initial HIV diagnosis, race, gender, age at HIV diagnosis and behavioral risk factors. One-way ANOVA was used to examine differences in baseline CD4 counts at HIV diagnosis between racial/ethnic groups. Chi square was used to analyze racial differences in risk factors. ^ Results. The analyzed study sample was 4767. The study population was 47% Black, 37% White and 16% Hispanic [p<0.05]. The mean and median CD4 counts at diagnosis were 254 and 193 cells per ml, respectively. At the initial HIV diagnosis Blacks had the highest average CD4 counts (285), followed by Whites (233) and Hispanics (212) [p<0.001 ]. These statistical differences, however, were only observed with CD4 counts above 350 [p<0.001], even when adjusted for age at diagnosis and gender [p<0.05]. Looking at risk factors, Blacks were mostly affected by intravenous drug use (IVDU) and heterosexuality, whereas Whites and Hispanics were more affected by male homosexuality [ p<0.05]. ^ Conclusion. (1) There were statistical differences in CD4 counts with respect to ethnicity, but these differences only existed for CD4 counts above 350. These differences however do not appear to have clinical significance. Antithetically, Blacks had the highest CD4 counts followed by Whites and Hispanics. (2) 50% of this study group clinically had AIDS at their initial HIV diagnosis (median=193), irrespective of ethnicity. It was not clear from data analysis if these observations were due to failure of early HIV surveillance, HIV testing policies or healthcare access. More studies need to be done to address this question. (3) Homosexuality and bisexuality were the biggest risk factors for Whites and Hispanics, whereas for Blacks were mostly affected by heterosexuality and IVDU, implying a need for different public health intervention strategies for these racial groups. ^

A comparative analysis of CHIP Perinatal policy in twelve states

The purpose of this comparative analysis of CHIP Perinatal policy (42 CFR § 457) was to provide a basis for understanding the variation in policy outputs across the twelve states that, as of June 2007, implemented the Unborn Child rule. This Department of Health and Human Services regulation expanded in 2002 the definition of “child” to include the period from conception to birth, allowing states to consider an unborn child a “targeted low-income child” and therefore eligible for SCHIP coverage. ^ Specific study aims were to (1) describe typologically the structural and contextual features of the twelve states that adopted a CHIP Perinatal policy; (2) describe and differentiate among the various designs of CHIP Perinatal policy implemented in the states; and (3) develop a conceptual model that links the structural and contextual features of the adopting states to differences in the forms the policy assumed, once it was implemented. ^ Secondary data were collected from publicly available information sources to describe characteristics of states’ political system, health system, economic system, sociodemographic context and implemented policy attributes. I posited that socio-demographic differences, political system differences and health system differences would directly account for the observed differences in policy output among the states. ^ Exploratory data analysis techniques, which included median polishing and multidimensional scaling, were employed to identify compelling patterns in the data. Scaled results across model components showed that economic system was most closely related to policy output, followed by health system. Political system and socio-demographic characteristics were shown to be weakly associated with policy output. Goodness-of-fit measures for MDS solutions implemented across states and model components, in one- and two-dimensions, were very good. ^ This comparative policy analysis of twelve states that adopted and implemented HHS Regulation 42 C.F.R. § 457 contributes to existing knowledge in three areas: CHIP Perinatal policy, public health policy and policy sciences. First, the framework allows for the identification of CHIP Perinatal program design possibilities and provides a basis for future studies that evaluate policy impact or performance. Second, studies of policy determinants are not well represented in the health policy literature. Thus, this study contributes to the development of the literature in public health policy. Finally, the conceptual framework for policy determinants developed in this study suggests new ways for policy makers and practitioners to frame policy arguments, encouraging policy change or reform. ^

Statistical and methodological challenges for disaster preparedness and medical needs assessment in Rio Grande Valley of Texas

In recent years, disaster preparedness through assessment of medical and special needs persons (MSNP) has taken a center place in public eye in effect of frequent natural disasters such as hurricanes, storm surge or tsunami due to climate change and increased human activity on our planet. Statistical methods complex survey design and analysis have equally gained significance as a consequence. However, there exist many challenges still, to infer such assessments over the target population for policy level advocacy and implementation. ^ Objective. This study discusses the use of some of the statistical methods for disaster preparedness and medical needs assessment to facilitate local and state governments for its policy level decision making and logistic support to avoid any loss of life and property in future calamities. ^ Methods. In order to obtain precise and unbiased estimates for Medical Special Needs Persons (MSNP) and disaster preparedness for evacuation in Rio Grande Valley (RGV) of Texas, a stratified and cluster-randomized multi-stage sampling design was implemented. US School of Public Health, Brownsville surveyed 3088 households in three counties namely Cameron, Hidalgo, and Willacy. Multiple statistical methods were implemented and estimates were obtained taking into count probability of selection and clustering effects. Statistical methods for data analysis discussed were Multivariate Linear Regression (MLR), Survey Linear Regression (Svy-Reg), Generalized Estimation Equation (GEE) and Multilevel Mixed Models (MLM) all with and without sampling weights. ^ Results. Estimated population for RGV was 1,146,796. There were 51.5% female, 90% Hispanic, 73% married, 56% unemployed and 37% with their personal transport. 40% people attained education up to elementary school, another 42% reaching high school and only 18% went to college. Median household income is less than $15,000/year. MSNP estimated to be 44,196 (3.98%) [95% CI: 39,029; 51,123]. All statistical models are in concordance with MSNP estimates ranging from 44,000 to 48,000. MSNP estimates for statistical methods are: MLR (47,707; 95% CI: 42,462; 52,999), MLR with weights (45,882; 95% CI: 39,792; 51,972), Bootstrap Regression (47,730; 95% CI: 41,629; 53,785), GEE (47,649; 95% CI: 41,629; 53,670), GEE with weights (45,076; 95% CI: 39,029; 51,123), Svy-Reg (44,196; 95% CI: 40,004; 48,390) and MLM (46,513; 95% CI: 39,869; 53,157). ^ Conclusion. RGV is a flood zone, most susceptible to hurricanes and other natural disasters. People in the region are mostly Hispanic, under-educated with least income levels in the U.S. In case of any disaster people in large are incapacitated with only 37% have their personal transport to take care of MSNP. Local and state government’s intervention in terms of planning, preparation and support for evacuation is necessary in any such disaster to avoid loss of precious human life. ^ Key words: Complex Surveys, statistical methods, multilevel models, cluster randomized, sampling weights, raking, survey regression, generalized estimation equations (GEE), random effects, Intracluster correlation coefficient (ICC).^

Performance recovery from exercise: A review from a statistical point of view

Background. Research into methods for recovery from fatigue due to exercise is a popular topic among sport medicine, kinesiology and physical therapy. However, both the quantity and quality of studies and a clear solution of recovery are lacking. An analysis of the statistical methods in the existing literature of performance recovery can enhance the quality of research and provide some guidance for future studies. Methods: A literature review was performed using SCOPUS, SPORTDiscus, MEDLINE, CINAHL, Cochrane Library and Science Citation Index Expanded databases to extract the studies related to performance recovery from exercise of human beings. Original studies and their statistical analysis for recovery methods including Active Recovery, Cryotherapy/Contrast Therapy, Massage Therapy, Diet/Ergogenics, and Rehydration were examined. Results: The review produces a Research Design and Statistical Method Analysis Summary. Conclusion: Research design and statistical methods can be improved by using the guideline from the Research Design and Statistical Method Analysis Summary. This summary table lists the potential issues and suggested solutions, such as, sample size calculation, sports specific and research design issues consideration, population and measure markers selection, statistical methods for different analytical requirements, equality of variance and normality of data, post hoc analyses and effect size calculation.^