Journal of Biometrics & Biostatistics

Functional Mapping is a popular statistical method in QTL mapping studies for longitudinal data. The threshold for declaring statistical significance of a QTL is commonly obtained through permutation tests, which can be time consuming. To improve the computational efficiency of a permutation test of mixture models used in Functional Mapping, we first quantified the correlation between QTL and longitudinal data, using a curve clustering method. Then, the QTLs which are highly correlated with the outcome were computed in the improved permutation tests. As a result, it reduces the amount of computation in permutation tests and speeds up the computation for Functional Mapping analysis. Simulation studies and real data analysis were conducted to demonstrate that the proposed approach can greatly improve the computational efficiency of QTL mapping without loss of accuracy.

Despite widespread adoption of family planning in the developing world contraceptive use is still very low in sub-Saharan Africa including Ethiopia and in other regions. The general objective of this study was identifying the socioeconomic factors of modern contraceptive methods usage among married women of reproductive ages (15-49 years old) in Hawassa city. From a total 990 sampled married women about 57.9% (573) were modern contraception methods users. Bayesian logistic regression procedure was adopted to make inference about the parameters of a logistic regression model. The purpose of this method is generating the posterior distribution of the unknown parameters given both the data and some prior density for the unknown parameters. Bayesian inference for logistic regression models is derived applying a Markov Chain Monte Carlo algorithm to simulate from the joint posterior distribution of the regression and the link parameters.

General description of the human neocortex verbal areas and exploration of the manner in which the synaptic neurolinguistic structure that develops in these areas after birth establishes our comprehension ability. Description of the manner in which the neurolinguistic subjective model of reality that develops in these areas can be made to evolve towards objective representation.

The high throughput RNA sequencing (RNA-seq) technology has become the popular method of choice for transcriptomics and the detection of differentially expressed genes. Sample size calculations for RNA-seq experimental design are an important consideration in biological research and clinical trials. Currently, the sample size formulas derived from the Wald and the likelihood ratio statistical tests with a Poisson distribution to model RNA-seq data have been developed. However, since the mean read counts in the real RNA-seq data are not equal to the variance, an extended method to calculate sample sizes based on a negative binomial distribution using an exact test statistic was proposed by Li et al. in 2013. In this study, we alternatively derive five sample size calculation methods based on the negative binomial distribution using the Wald test, the log-transformed Wald test and the log-likelihood ratio test statistics. A comparison of our five methods and an existing method was performed by calculating the sample sizes and the simulated power in different scenarios. We first calculated the sample sizes for testing a single gene using the six methods given a nominal significance level α at 0.05 and 80% power. Then, we calculated the sample sizes for testing multiple genes given a false discovery rate (FDR) at 0.05 and 0.10. The empirical power and true prognostic genes for differential gene expression analysis corresponding to the estimated sample sizes from the six methods are also estimated via the simulation studies. Using the sample size formulas derived from log-transformed and Wald-based tests, we observed smaller sample properties while maintaining the nominal power close to or higher than 80% in all the settings compared to other methods. Moreover, the Wald test based sample size calculation method is easier to compute and faster in an RNA-seq experimental design.

A study was conducted to evaluate the relative biochemical basis of susceptibility of six commercial wheat varieties grown in Khyber Pakhtunkhwa, Pakistan, against angoumois grain moth, Sitotroga cerealella (Olivier) (Lepidoptera: Gelechiidae) and construction of its life table at 28 ± 1°C, 65 ± 5 R.H.% and L:D 16:8 hours under laboratory environment. The results were evaluated on the basis of mean pest S. cerealella emergence, percent damage, and percent weight loss, male and female emerged along susceptibility index, 1000 grains weight, hardness and chemical composition of test wheat materials. Life table parameters of S. cerealella on highly susceptible and least susceptible wheat varieties were compared. On the basis of susceptibility index, variety Sirin (5.002) was recorded least susceptible and variety Pirsbak-2005 (7.832) recorded as highly susceptible. The chemical composition based on protein and carbohydrate contents (11.15%, 72.54%) revealed that the variety Sirin was recorded least susceptible, while variety Pirsabak-2005 (12.68%, 75.00%) was noted as highly susceptible. On the basis of life table, the net reproductive rate (10.9) on variety Pirsabak-2005 was higher than variety Sirin (9.4), and the intrinsic rate of increase was also higher on Pirsabak-2005 (30.7) than Sirin (15.9). With respect to doubling time being index of resistance, this time in Pirsabak-2005 was almost half (0.01 days) of the Sirin (0.02 days), which means that S. cerealella can develop more quickly on Pirsabak-2005 germplasm. In summation, relatively least susceptible wheat variety Sirin can be used to increase the level and diversify the basis of resistance to S. cerealella in the resistance breeding programs.

Often repeated measures data are summarized into pre-post-treatment measurements. Various methods exist in the literature for estimating and testing treatment effect, including ANOVA, analysis of covariance (ANCOVA), and linear mixed modeling (LMM). Under the first two methods, outcomes can either be modeled as the post treatment measurement (ANOVA-POST or ANCOVA-POST), or a change score between pre and post measurements (ANOVACHANGE, ANCOVA-CHANGE). In LMM, the outcome is modeled as a vector of responses with or without Kenward- Rogers adjustment. We consider five methods common in the literature, and discuss them in terms of supporting simulations and theoretical derivations of variance. Consistent with existing literature, our results demonstrate that each method leads to unbiased treatment effect estimates, and based on precision of estimates, 95% coverage probability, and power, ANCOVA modeling of either change scores or post-treatment score as the outcome, prove to be the most effective. We further demonstrate each method in terms of a real data example to exemplify comparisons in real clinical context.

The objective was to find out the misconceptions and perceptions associated with breast cancer in Kilifi south subcounty. A survey was conducted on a random sample of women in Kilifi South with no history of breast cancer. The Survey instrument were questionnaires which included measures of perceptions and misconceptions of breast cancer for themselves and for the average woman, perceptions of risk factors that influenced their risk and the average woman’s risk for breast cancer. Descriptive statistics, chi-square tests and spearman’s correlations were used to analyze the data. A spearman’s correlation coefficient showed that, women’s perceptions on Witchcraft as a cause for cancer were related to Cancer being a curse. The correlation was statistically significant at 5% level of significance, (R=0. 5182, S=4462.4, p=0.0003016). A Pearson’s chi-square test also confirmed this (χ2=47.407, P-value=0.000). This probably explains why the uptake of screening services provided by the government in the health centers across the counties has not been effective.