arXiv subjects

Ayman Alzaatreh

Publications and source records attributed to Ayman Alzaatreh.

4 recordsLinked to original sources

arXiv2025

Computation of statistical power and sample size for in vivo research models

Sample size calculation is crucial in biomedical in vivo research investigations mainly for two reasons: to design the most resource-efficient studies and to safeguard ethical issues when alive animals are subjects of testing. In this context, power analysis has been widely applied to compute the sample size by predetermining the desired statistical power and the significance level. To verify whether the assumption of a null hypothesis is true, repeated measures analysis of variance (ANOVA) is used to test the differences between multiple experimental groups and control group(s). In this article, we focus on the a priori power analysis, for testing multiple parameters and calculating the power of experimental designs, which is suitable to compute the sample size of trial groups in repeated measures ANOVA. We first describe repeated measures ANOVA and the statistical power from a practical aspect of biomedical research. Furthermore, we apply the G*Power software to conduct the a priori power analysis using examples of repeated measures ANOVA with three groups and five time points. We aim not to use the typical technically adapted statistical language. This will enable experimentalists to confidently formulate power calculation and sample size calculation easier and more accurately.

stat.AP↗

arXiv2024

Multivariate Analysis of Gut Microbiota Composition and Prevalence of Gastric Cancer

The global surge in the cases of gastric cancer has prompted an investigation into the potential of gut microbiota as a predictive marker for the disease. The alterations in gut diversity are suspected to be associated with an elevated risk of gastric cancer. This paper delves into finding the correlation between gut microbiota and gastric cancer, focusing on patients who have undergone total and subtotal gastrectomy. Utilizing data mining and statistical learning methods, an analysis was conducted on 16S-RNA sequenced genes obtained from 96 participants with the aim of identifying specific genera of gut microbiota associated with gastric cancer. The study reveals several prominent bacterial genera that could potentially serve as biomarkers assessing the risk of gastric cancer. These findings offer a pathway for early risk assessment and precautionary measures in the diagnosis of gastric cancer. The intricate mechanisms through which these gut microbiotas influence gastric cancer progression warrant further investigation. This research significantly aims to contribute to the growing understanding of the gut-cancer axis and its implications in disease prediction and prevention.

q-bio.GN↗

arXiv2024

How to Measure Evidence and Its Strength: Bayes Factors or Relative Belief Ratios?

Both the Bayes factor and the relative belief ratio satisfy the principle of evidence and so can be seen to be valid measures of statistical evidence. Certainly Bayes factors are regularly employed. The question then is: which of these measures of evidence is more appropriate? It is argued here that there are questions concerning the validity of a current commonly used definition of the Bayes factor based on a mixture prior and, when all is considered, the relative belief ratio has better properties as a measure of evidence. It is further shown that, when a natural restriction on the mixture prior is imposed, the Bayes factor equals the relative belief ratio obtained without using the mixture prior. Even with this restriction, this still leaves open the question of how the strength of evidence is to be measured. It is argued here that the current practice of using the size of the Bayes factor to measure strength is not correct and a solution to this issue is presented. Several general criticisms of these measures of evidence are also discussed and addressed.

math.ST↗

arXiv2020

On the discrete analog of gamma-Lomax distribution: properties and applications

This article represents how certain types of blockades in any industrial (heavy industries) production, in particular, industrial strikes can be modeled with the proposed discrete probabilistic distribution as a baseline distribution. We considered the number of outbreaks of strikes in the coal mining industry, the vehicle manufacturing industry, and the transpose industry in the UK obtained from Consul (1989). We fitted those data sets with the proposed discrete gamma-Lomax distribution and compared the fit with the discrete generalized Pareto distribution (Consul, 1989). For this purpose, we explore the basic properties of the discrete gamma-Lomax distribution including but not limited to: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; consider maximum likelihood estimation under the normal set up as well as under the censored data set scenario. It is observed that the newly proposed model can be useful to describe strikes arising from various types of industries.

math.ST↗