Journal of Biometrics & Biostatistics

In this study, significant risk factors of Helicobacter pylori infection in Lahore are investigated through a case-control study by using descriptive and analytical approaches. A sample of 362 subjects was selected from the Gastroenterology Departments and OPDs of different hospitals of Lahore. About 25 risk factors with sub categories were included in the study. For bivariate analysis, the chi-square, phi/v statistics and Kendall’s tau-b are used. From descriptive analysis, it was found that the persons who eat from restaurants have more risk of infection as compare to persons who eat homemade food. By the descriptive analysis, it was also observed that risk of Helicobacter pylori infection increases with an increase in the number of family members per house and in the number of persons living per room. Furthermore, similar results were observed in the bivariate analysis. In the analysis, the five risk factors including age, food eat, food liked, dental complains and number of persons living per room are found to be positively significant having the odds ratios and 95% confidence intervals of odds ratios (1.025; 1.003-1.047), (9.596; 4.767-19.314), (3.500; 1.509-8.119), (3.204; 1.685-6.094) and (2.772; 1.496-5.139), respectively. While the three risk factors including usage of tea, educational level and sewerage system are found to be negatively significant having odds ratios and 95% confidence intervals for the odds ratios (0.221; 0.119-0.411), (0.216; 0.115-0.404) and (0.401; 0.218-0.738), respectively, which indicates that these three risk factors are protective factors against Helicobacter pylori infection. According to this study, the subjects who eat from restaurants have higher risk of Helicobacter pylori infection as compared to all other risk factors.

Aim of study: Leukemia has different subtypes, which present unique clinical and molecular characteristics. MLL (Mixed Lineage Leukemia) is one of the new different subtypes than AML and ALL. Materials and Methods: Genomic characterization is the main key understanding the differences of MLL by analysis of differential gene expression, methylation patterns and mutational spectra that were compared and analyzed between MLL and AML types (n=197). Results: According to the genomic characterization of MLL, differentially expressed 114 genes were selected and 37 of them targeted genes having more than 2 fold expression change, including HOXA9, CFH, DDX4, MSH4, MSMB, TWIST1, ZSWIM2, POU6F2. To measure the aberrant methylation is the second genomic characterization of this research because the rearrangements of MLL gene leading to aberrant methylation. The methylation data were compared between cancer and control, so high methylated genes have been detected between MLL and AML types. The methylation loci were categorized into two groups: ≥ 10 fold difference and ≥ 5 and ≤ 10 fold difference. Some of the genes high methylated more than one location such as; RAET1E, HSD17B2, RNASE11, DGK1, POU6F2, NAGS, PIK3C2G, GADL1, and KRT13. In addition to that, analysis of somatic mutation gives us that CFH has the highest point mutation 9,92%. Conclusion: Overall, the MLL genomic characterization shows that it is different than AML and exhibits a unique molecular and biological phenotype and point to new possible targetable genes for future treatment of MLL leukemia are two important values.

One of the major concerns among developing countries in recent decades is the effect of declining food security with ever-growing population. Hence, the importance of adopting cost effective farming methods has led to the development of various statistical methods to alleviate food insecurity. Among these methods, CCD has gained significant attention to its application in agriculture. In this paper, the response surface methodology (RSM) was applied in order to determine the effects of the factors potassium (K), nitrogen (N) and phosphorus (P) on the yield of potato tuber. The predicted values for the yield of potato tuber by the response functions were in a very close agreement with experimental data (R2=90%). The second-order model was developed by solving the parameters of the regression equation using the method of least squares. The optimal combinations of the factors potassium (K), nitrogen (N) and phosphorus (P) with yield as the response of interest were determined by analyzing the 3D response surface plots and using the method of steepest ascent. Using ridge analysis method which corresponds to the method of steepest ascent, the optimal yield of potato tuber was estimated to be 29.26 t ha-1 which is much higher than the current national target of 14 t ha-1 with optimum factor levels being K=35.36 kg K2o ha-1, N=78.71 Kg N ha-1 and P=160.69 Kg P=160.69 kg P2o5ha-1, respectively. Nitrogen and phosphorous had a significant positive linear effects on the potato tuber yield. Based on the results, it can be concluded that the response surface methodology is a suitable approach for determining the optimal conditions of the selected fertilizer types.

Correlation measures the strength of association between two variables, which plays an important role in various fields, such as Health Science, Economics, Finance, Engineering, Environmental science among others. Several tests for testing the population correlation coefficient are proposed in a literature by various researchers at different time points. This paper evaluates the performance of some of the prominent test statistics for testing the population correlation coefficient based on empirical size and power of the tests. Some bivariate distributions, such as normal, lognormal, gamma and chi-square are considered to compare the performance of the test statistics. We believe that the findings of this paper will make an important contribution to select some good test statistics to find the relationship between two variables.

Quantile regression (QR) has received increasing attention in recent years and applied to wide areas such as investment, finance, economics, medicine and engineering. Compared with conventional mean regression, QR can characterize the entire conditional distribution of the outcome variable, may be more robust to outliers and misspecification of error distribution, and provides more comprehensive statistical modeling than traditional mean regression. QR models could not only be used to detect heterogeneous effects of covariates at different quantiles of the outcome, but also offer more robust and complete estimates compared to the mean regression, when the normality assumption violated or outliers and long tails exist. These advantages make QR attractive and are extended to apply for different types of data, including independent data, time-to-event data and longitudinal data. Consequently, we present a brief review of QR and its related models and methods for different types of data in various application areas.

In this article, we derive a new and unique method of estimating quantile and quantile density function, which is based on moments of fractional order statistics. A comparison of the proposed estimators is made with existing popular nonparametric quantile and quantile density estimators, in terms of mean squared error (MSE) for censored and uncensored data. Recommendations for the choice of quantile and/or quantile density estimators are given.