Journal of Data Science

Abstract: Much of the statistical literature regarding categorical data focuses on the odds ratio, yet in many epidemiological and clinical trial settings, the relative risk is the quantity of interest. Recently, Spiegelman and Hertz mark illustrated modeling and SAS programming for modeling relative risk in contrast to the logistic model’s odds ratio. The focus of their work is on a single relative risk, i.e., for one binary response variable. Herein, we outline two methods for estimating relative risks for two correlated binary outcomes. The first method is weighted least squares estimation for categor ical data modeling. The second method is based on generalized estimating equations. The two methods are readily implemented using common statis tical packages, such as SAS. The methods are illustrated using clinical trial data examining the relative risks of nausea and vomiting for two different drugs commonly used to provide general anesthesia.

Abstract: We introduce and study a new four-parameter lifetime model named the exponentiated generalized extended exponential distribution. The proposed model has the advantage of including as special cases the exponential and exponentiated exponential distributions, among others, and its hazard function can take the classic shapes: bathtub, inverted bathtub, increasing, decreasing and constant, among others. We derive some mathematical properties of the new model such as a representation for the density function as a double mixture of Erlang densities, explicit expressions for the quantile function, ordinary and incomplete moments, mean deviations, Bonferroni and Lorenz curves, generating function, R´enyi entropy, density of order statistics and reliability. We use the maximum likelihood method to estimate the model parameters. Two applications to real data illustrate the flexibility of the proposed model.

Abstract: Stochastic modeling and analysis of international key compar isons (interlaboratory comparisons) pose several fundamental questions for statistical methodology. A key comparison (KC) is specifically designed to derive the key comparison reference value and to assess conformance of cal ibrations by participating national metrology laboratories at a few, “key”, settings for a particular measurement process. An approach to the statis tical study of key comparisons data is proposed using a model taken from meta-analysis. This model leads to a class of weighted means estimators for the consensus value and to a method of assessing the uncertainty of the resulting estimates.

Although the two-parameter Beta distribution is the standard distribution for

Abstract: A family of distribution is proposed by using Kumaraswamy-G ( Kw − G ) distribution as the base line distribution in the generalized Marshall-Olkin (GMO) construction. By expanding the probability density function and the survival function as infinite series the proposed family is seen as infinite mixtures of the Kw − G distribution. Series expansions of the density function for order statistics are also obtained. Moments, moment generating function, Rényi entropy, quantile function, random sample generation, asymptotes, shapes and stochastic orderings are also investigated. Maximum likelihood estimation, their large sample standard error, confidence intervals and method of moment are presented. Three real life illustrations of comparative data modeling applications with some of the important sub mode

Factor analysis (FA) is the most commonly used pattern recognition methodology in social and health research. A technique that may help to better retrieve true information from FA is the rotation of the information axes. The purpose of this study was to evaluate whether the selection of rotation type affects the repeatability of the patterns derived from FA, under various scenarios of random error introduced, based on simulated data from the Standard Normal distribution. It was observed that when applying promax non - orthogonal rotation, the results were more repeatable as compared to the orthogonal rotation, irrespective of the level of random error introduced in the model.

Abstract: In the absence of definitive trials on the safety and efficacy of drugs, a systematic and careful synthesis of available data may provide critical information to help decision making by policy makers, medical professionals, patients and other stakeholders. However, uncritical and unbalanced use of pooled data to inform decision about important healthcare issues may have consequences that adversely impact public health, stifle innovation, and con found medical science. In this paper, we highlight current methodological issues and discuss advantages and disadvantages of alternative meta-analytic techniques. It is argued that results from pooled data analysis would have maximal reliability and usefulness in decision making if used in a holistic framework that includes presentation of data in light of all available knowledge and effective collaboration among academia, industry, regulatory bodies and other stakeholders.

Abstract: Childhood obesity is a major health concern. The associated health risks dramatically reduce lifespan and increase healthcare costs. The goal was to develop methodology to identify as early in life as possible whether or not a child would become obese at age five. This diagnostic tool would facilitate clinical monitoring to prevent and or minimize obesity. Obesity is measured by Body Mass Index (BMI), but an improved metric, the ratio of weight to height (or length) (WOH), is proposed from this re search for detecting early obesity. Results of this research demonstrate that WOH performs better than BMI for early detection of obesity in individuals using a longitudinal decision analysis (LDA), which is essentially an indi viduals type control chart analysis about a trend line. Utilizing LDA, the odds of obesity of a child at age five is indicated before the second birth day with 95% sensitivity and 97% specificity. Further, obesity at age five is indicated with 75% specificity before two months and with 84% specificity before three months of age. These results warrant expanding this study to larger cohorts of normal, overweight, and obese children at age five from different healthcare facilities to test the applicability of this novel diagnostic tool.

Abstract: Information identities derived from entropy and relative entropy can be useful in statistical inference. For discrete data analyses, a recent study by the authors showed that the fundamental likelihood structure with categorical variables can be expressed in different yet equivalent information decompositions in terms of relative entropy. This clarifies an essential difference between the classical analysis of variance and the analysis of discrete data, revealing a fallacy in the analysis of hierarchical loglinear models. The discussion here is focused on the likelihood information of a three-way contingency table, without loss of generality. A classical three-way categorical data example is examined to illustrate the findings.