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Abstract: A major objective in microarray experiments is to identify a panel of genes that are associated with a disease outcome or trait. Many statistical methods have been proposed for gene selection within the last fifteen years. While the comparison of some of these methods has been done, most of them concentrated on finding gene signatures based on two groups. This study evaluates four gene selection methods when the outcome of interested is continuous in nature. We provide a comparative review of four methods: the Statistical Analysis of Microarrays (SAM), the Linear Models for Microarray Analysis (LIMMA), the Lassoed Principal Components (LPC), and the Quantitative Trait Analysis (QTA). Comparison is based on the power to identify differentially expressed genes, the predictive ability of the genelists for a continuous outcome (G2 checkpoint function), and the prognostic properties of the genelists for distant metastasis-free survival. A simulated dataset and a publicly available melanoma cell lines dataset are used for simulations and validation, respectively. A primary melanoma dataset is used for assessment of prognosis. No common genes were found among the genelists from the four methods. While the SAM was generally the best in terms of power, the QTA genelist performed the best in the prediction of the G2 checkpoint function. Identification of genelists depends on the choice of the gene selection method. The QTA method would be preferred over the other approaches in predicting a quantitative outcome in melanoma research. We recommend the development of more robust statistical methods for differential gene expression analysis. Keywords: Differential gene expression, Melanoma cell lines, Prediction, Power, Quantitative trait. |
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Abstract: The generalized linear model is an important class of models that has wide variety of applications mainly because of its inherent flexibility and generality. The present paper provides an important application of GLM in order to examine different constituents of bile acid in the development of gallstones as well as carcinoma among the gallbladder patients. These constituents may be broadly categorized as primary and secondary bile acids. The paper, in fact, considers two particular cases of GLM based on normal and gamma modelling assumptions and provides the complete Bayes analysis using independent but vague priors for the concerned model parameters. It then analyzes a real data set taken from SS Hospital, Banaras Hindu University, with primary (secondary) bile acids as response variables and secondary (primary) bile acids as the predictors. The authenticity of the assumed models for the given data set is also examined based on predictive simulation ideas. Keywords: Generalized linear model, vague priors, posterior distribution, biliary acids, gallbladder diseases, predictive simulation, Bayes information criterion. Download Full Article |
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Abstract: The aim of this study was to assess the relationship between self-image in eating disorders and age, duration and severity of the disorder, comorbidity, depressiveness and self-evaluation of eating problems. The results of the Offer self-image questionnaire for adolescents (QSIA) were compared in four groups: anorexia nervosa restrictive subtype (ANR, n: 47), anorexia nervosa binge/purge subtype (ANBP, n: 16), bulimia nervosa (BUL, n: 34) and eating disorders NOS (EDNOS, n: 19). The control group was age matched female pupils (NOR, n = 76). The Kruskal-Wallis test revealed significant differences between the age of patients from the ANR (16.34, SD 1.58) and BUL (17.56, SD 0.96) groups (p = .008). The self-image of schoolgirls from the NOR group was on most scales significantly better than the self-image of girls from clinical groups. On four scales differences between the (better) self-image in the ANR group and that in the BUL group were observed. Next, a cluster analysis using a generalised k-means algorithm with v-fold cross validation of QSIA questionnaire results was conducted in the group of clinical eating disorders (ANR, ANBP, and BUL). Three clusters were obtained. The first was characterized by very good self-image (above the averagefor the general population), the second by poor self-image and the third by negative self-image. Severity of depressiveness measured using the Beck Depression Inventory turned out to be the only factor which differentiated the clusters of self-image in eating disorders. Keywords: Anorexia, bulimia, QSIA, DATA MINING, cluster analysis.Download Full Article |
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Abstract: The aim of this study is to examine the association among the probabilities of Type I errorobtained by Unadjusted All Pairwise Comparisons (UAPC) and Bonferroni-adjustment approaches, the sample size and the frequency of occurrence of an event (prevalence, proportion) in hypothesis testing of difference among the proportions in studies. In the simulation experiment planned for this purpose, 4 groups were formed and the proportions in each group were chosen between 0.10 and 0.90 so that they will be equal at each experiment. Furthermore, the sample sizes were chosen from 20 to 1000. In accordance with these scenarios, the probabilities of Type I error were calculated by both of approaches. In each approach, a significant S-curve relationship was found between the probability of Type I error and sample size. However, a significant quadratic relationship was found between the probabilities of Type I error and the proportions in each group. Nonlinear functional relations were put forward in order to estimate the observed Type I errorrates obtained by the two different approaches where sample size and the proportion in each group are known. Furthermore, it was founded that Bonferroni-adjustment approach cannot always protect Type I error level. It was observed that the probability of Type I error estimated by the functional relation on Type I error rate for UAPC approach is lower than the values calculated using the formula in the literature. Keywords: Proportion comparison, type I error, bonferroniadjustment, unadjusted all pairwise comparisons.Download Full Article |
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Abstract: The Millennium Development Goal 4 (MDG 4) of United Nations had set the target of reducing high rates of under-five and infant mortality (IMR) by two thirds to be reached by 2015 using 1990 as the benchmark year. By the availability of time series data on IMR from United Nations Inter-agency Group for Child Mortality Estimation (UN IGME, 2012), led by UNICEF, WHO, the World Bank and United Nations, it has become possible to track the rate of progress towards this goal. Using the UN IGME 2012 data for all the South Asian Countries, I have considered three specific issues in this article. (1) How does the South Asian Countries fair in reducing the IMR towards this MDG target? Although the time series data exhibit declining trends for all the countries in South Asia, to what extent such trends are attributed by their average annual progress trajectory over the period for which data are available? (2) Whether deterministic or stochastic trend can attribute the IMR decline in South Asian countries and what alternative time series models be used to forecast the decline in Infant Mortality? Can we find a serviceable representative model for the entire region? (3) In case, a satisfactory representative model for the entire region exists, how do we assess the forecast accuracy for this model and quantify the propagation of forecast error? Keywords: Infant mortality, ARIMA model, random walk, MDGs, demographic forecast, unit root test.Download Full Article |