Ahmed Hossain

Inference for the Type II generalized logistic distribution under progressive Type II censoring

Recently, in order to get closer agreement at the extremes, skewed distributions are playing an important role in various research studies. The generalized logistic distribution (GLD) of Type II, which is indexed by one shape parameter, is introduced here to extend the scope of this distribution in some asymmetrical studies. Several properties of this distribution in relation to other probability distributions are stated. Furthermore, the maximum-likelihood (ML) method and an approximate ML method are used to derive the point estimators of the parameters based on progressive Type II censoring. A wide range of sample sizes and progressive-censoring schemes are considered in a simulation study to see the performance of estimates of location and scale parameters of the Type II GLD. The coverages probability of the pivotal quantities (for location and scale parameters) based on asymptotic normality are shown to be unsatisfactory, especially when the effective sample size is small. To improve the coverage probabilities, we suggest the use of unconditional simulated percentage points for the construction of confidence intervals. Two numerical examples are presented to illustrate the methods of estimation discussed here.

Downregulation of microRNAs 145-3p and 145-5p is a Long-Term Predictor of Postmenopausal Breast Cancer Risk: the ORDET prospective study

Background: miRNAs have been implicated in the regulation of key metabolic, inflammatory, and malignant pathways; hence, they might be considered both predictors and players of cancer development. Methods: Using a case–control study design nested in the ORDET prospective cohort study, we addressed the possibility that specific mRNAs can serve as early predictors of breast cancer incidence in postmenopausal women. We compared leukocyte miRNA profiles of 133 incident postmenopausal breast cancer cases and profiles of 133 women who remained healthy over a follow-up period of 20 years. Results: The analysis identified 20 differentially expressed miRNAs, 15 of which were downregulated. Of the 20 miRNAs, miR145-5p and miR145-3p, each derived from another arm of the respective pre-miRNA, were consistently and significantly downregulated in all the databases that we surveyed. For example, analysis of more than 1,500 patients (the UK Metabric cohort) indicated that high abundance of miR145-3p and miR145-5p was associated with longer, and for miR145-3p also statistically significant, survival. The experimental data attributed different roles to the identified miRNAs: Although the 5p isoform was associated with invasion and metastasis, the other isoform seems related to cell proliferation. Conclusions: These observations and the prospective design of our study lend support to the hypothesis that downregulation of specific miRNAs constitutes an early event in cancer development. This finding might be used for breast cancer prevention. Impact: The identification of the miRNAs as long-term biomarkers of breast cancer may have an impact on breast cancer prevention and early detection. Cancer Epidemiol Biomarkers Prev; 23(11); 2471–81. ©2014 AACR.

Analysis of baseline, average, and longitudinally measured blood pressure data using linear mixed models

This article compares baseline, average, and longitudinal data analysis methods for identifying genetic variants in genome-wide association study using the Genetic Analysis Workshop 18 data. We apply methods that include (a) linear mixed models with baseline measures, (b) random intercept linear mixed models with mean measures outcome, and (c) random intercept linear mixed models with longitudinal measurements. In the linear mixed models, covariates are included as fixed effects, whereas relatedness among individuals is incorporated as the variance-covariance structure of the random effect for the individuals. The overall strategy of applying linear mixed models decorrelate the data is based on Aulchenko et al.s GRAMMAR. By analyzing systolic and diastolic blood pressure, which are used separately as outcomes, we compare the 3 methods in identifying a known genetic variant that is associated with blood pressure from chromosome 3 and simulated phenotype data. We also analyze the real phenotype data to illustrate the methods. We conclude that the linear mixed model with longitudinal measurements of diastolic blood pressure is the most accurate at identifying the known single-nucleotide polymorphism among the methods, but linear mixed models with baseline measures perform best with systolic blood pressure as the outcome.

Approximate MLEs of the parameters of location-scale models under type II censoring

Log-location-scale distributions are widely used parametric models that have fundamental importance in both parametric and semiparametric frameworks. The likelihood equations based on a Type II censored sample from location-scale distributions do not provide explicit solutions for the para-meters. Statistical software is widely available and is based on iterative methods (such as, Newton Raphson Algorithm, EM algorithm etc.), which require starting values near the global maximum. There are also many situations that the specialized software does not handle. This paper provides a method for determining explicit estimators for the location and scale parameters by approximating the likelihood function, where the method does not require any starting values. The performance of the proposed approximate method for the Weibull distribution and Log-Logistic distributions is compared with those based on iterative methods through the use of simulation studies for a wide range of sample size and Type II censoring schemes. Here we also examine the probability coverages of the pivotal quantities based on asymptotic normality. In addition, two examples are given.

An Improved Method on Wilcoxon Rank Sum Test for Gene Selection from Microarray Experiments

Selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. In this paper, we propose a flexible rank-based nonparametric procedure for gene selection from microarray data. In the method we propose a statistic for testing whether area under receiver operating characteristic curve (AUC) for each gene is equal to 0.5 allowing different variance for each gene. The contribution to this “single gene” statistic is the studentization of the empirical AUC, which takes into account the variances associated with each gene in the experiment. Delong et al. proposed a nonparametric procedure for calculating a consistent variance estimator of the AUC. We use their variance estimation technique to get a test statistic, and we focus on the primary step in the gene selection process, namely, the ranking of genes with respect to a statistical measure of differential expression. Two real datasets are analyzed to illustrate the methods and a simulation study is carried out to assess the relative performance of different statistical gene ranking measures. The work includes how to use the variance information to produce a list of significant targets and assess differential gene expressions under two conditions. The proposed method does not involve complicated formulas and does not require advanced programming skills. We conclude that the proposed methods offer useful analytical tools for identifying differentially expressed genes for further biological and clinical analysis.

A flexible approximate likelihood ratio test for detecting differential expression in microarray data

Identifying differentially expressed genes in microarray data has been studied extensively and several methods have been proposed. Most popular methods in the study of gene expression microarray data analysis rely on normal distribution assumption and are based on a Wald statistic. These methods may be inefficient when expression levels follow a skewed distribution. To deal with possible violations of the normality assumption, we propose a method based on Generalized Logistic Distribution of Type II (GLDII). The motivation behind this distributional assumption is to allow longer tails than normal distribution. This is important in analyzing gene expression data since extreme values are common in such experiments. The shape parameter for GLDII allows flexibility in modeling a wide range of distributions. To simplify the computational complexity involved in carrying out Likelihood Ratio (LR) tests for several thousands of genes, an Approximate LR Test (ALRT) is proposed. We also generalize the two-class ALRT method to multi-class microarray data. The performance of the ALRT method under the GLDII assumption is compared to methods based on Wald-type statistics using simulation. The results from the simulations show that our method performs quite well compared to the significance analysis of microarrays (SAM) approach using standardized Wilcoxon rank statistics and the empirical Bayes (E-B) t-statistics. Our method is also less sensitive to extreme values. We illustrate our method using two publicly available gene expression data sets.

Application of skew-normal distribution for detecting differential expression to microRNA data

Traditional statistical modeling of continuous outcome variables relies heavily on the assumption of a normal distribution. However, in some applications, such as analysis of microRNA (miRNA) data, normality may not hold. Skewed distributions play an important role in such studies and might lead to robust results in the presence of extreme outliers. We apply a skew-normal (SN) distribution, which is indexed by three parameters (location, scale and shape), in the context of miRNA studies. We developed a test statistic for comparing means of two conditions replacing the normal assumption with SN distribution. We compared the performance of the statistic with other Wald-type statistics through simulations. Two real miRNA datasets are analyzed to illustrate the methods. Our simulation findings showed that the use of a SN distribution can result in improved identification of differentially expressed miRNAs, especially with markedly skewed data and when the two groups have different variances. It also appeared that the statistic with SN assumption performs comparably with other Wald-type statistics irrespective of the sample size or distribution. Moreover, the real dataset analyses suggest that the statistic with SN assumption can be used effectively for identification of important miRNAs. Overall, the statistic with SN distribution is useful when data are asymmetric and when the samples have different variances for the two groups.

Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data

Abstract MicroRNAs (miRNAs) are short non-coding RNAs that play critical roles in numerous cellular processes through post-transcriptional functions. The aberrant role of miRNAs has been reported in a number of diseases. A robust computational method is vital to discover novel miRNAs where level of noise varies dramatically across the different miRNAs. In this paper, we propose a flexible rank-based procedure for estimating a weighted log partial area under the receiver operating characteristic (ROC) curve statistic for selecting differentially expressed miRNAs. The statistic combines results taking partial area under the curve (pAUC) and their corresponding variance. The proposed method does not involve complicated formulas and does not require advanced programming skills. Two real datasets are analyzed to illustrate the method and a simulation study is carried out to assess the performance of different miRNA ranking statistics. We conclude that the proposed method offers robust results with large samples for miRNA expression data, and the method can be used as an alternative analytical tool for identifying a list of target miRNAs for further biological and clinical investigation.

MiRNA-513a-5p inhibits progesterone receptor expression and constitutes a risk factor for breast cancer: The hOrmone and Diet in the ETiology of breast cancer prospective study

MicroRNAs (miRNAs) might be considered both predictors and players of cancer development. The aim of the present report was to investigate whether many years before the diagnosis of breast cancer miRNA expression is already disregulated. In order to test this hypothesis, we compared miRNAs extracted from leukocytes in healthy women who later developed breast cancer and in women who remain healthy during the whole 15-year follow-up time. Accordantly, we used a case-control study design nested in the hOrmone and Diet in the ETiology of breast cancer (ORDET) prospective cohort study addressing the possibility that miRNAs can serve as both early biomarkers and components of the hormonal etiological pathways leading to breast cancer development in premenopausal women. We compared leukocyte miRNA profiles of 191 incident premenopausal breast cancer cases and profiles of 191 women who remained healthy over a follow-up period of 20 years. The analysis identified 20 differentially expressed miRNAs in women candidate to develop breast cancer versus control women. The upregulated miRNAs, miR-513-a-5p, miR-513b-5p and miR-513c-5p were among the most significantly deregulated miRNAs. In multivariate analysis, miR-513a-5p upregulation was directly and statistically significant associated with breast cancer risk (OR = 1.69; 95% CI 1.08-2.64; P = 0.0293). In addition, the upregulation of miR-513-a-5p displayed the strongest direct association with serum progesterone and testosterone levels. The experimental data corroborated the inhibitory function of miR-513a-5p on progesterone receptor expression confirming that progesterone receptor is a target of miR-513a-5p. The identification of upregulated miR-513a-5p with its oncogenic potential further validates the use of miRNAs as long-term biomarker of breast cancer risk.

The school-level factors associated with internet addiction among adolescents: A Cross-Sectional study in Bangladesh

Objectives: The adolescents have increased the use of the Internet not ably over the last few years. Here we investigate the prevalence of Internet addiction among adolescents and identify the school-level factors on Internet addiction. Materials and Methods: This was a school-based cross-sectional study with 279 students aged 14 17years in the education year of 2016. The students Internet addiction was determined through the Ormans test, which consists of nine questions. Results: The prevalence of severe internet addiction among the surveyed adolescents was 2.51%, and moderate internet addiction was 64.87%. The study revealed from the multivariatelogisticmodel that the male students are 31% (OR = 0.69, confidence interval: 0.36 1.34) less likely to be moderate/severely internet addicted compared to female students. Similarly, considering the number of siblings, lonely child in a house is 3.12 times (OR = 3.12, CI: 1.14 9.29) more likely to have moderate/severe internet addiction compared to who has 3ormore siblings. The students with English medium studies (OR = 0.58) and the students who play out door games (OR = 0.56) are also negatively associated with severe internet addiction. Conclusion: The results indicate that the adolescents of grade nine students, Bangla medium studies and the students who don’t play outside games are more prone to be the Internet addicted. Besides, female students are more in risk to be Internet addicted than male students. The findings will help us for strategies to be put in the school to reduce the Internet addiction among adolescents. *Corresponding author: Ahmed Hossain, Department of Public Health, North South University, Canada, E-mail: ahmed.hossain@utoronto.ca Received Date: September 13, 2017 Accepted Date: November 30, 2017 Published Date: December 06, 2017