Biomedical subjects

Mohamed M Shoukri

Publications and source records attributed to Mohamed M Shoukri.

5 recordsLinked to original sources

PubMed2006

Interval estimation and optimal design for the within-subject coefficient of variation for continuous and binary variables.

BACKGROUND: In this paper we propose the use of the within-subject coefficient of variation as an index of a measurement's reliability. For continuous variables and based on its maximum likelihood estimation we derive a variance-stabilizing transformation and discuss confidence interval construction within the framework of a one-way random effects model. We investigate sample size requirements for the within-subject coefficient of variation for continuous and binary variables. METHODS: We investigate the validity of the approximate normal confidence interval by Monte Carlo simulations. In designing a reliability study, a crucial issue is the balance between the number of subjects to be recruited and the number of repeated measurements per subject. We discuss efficiency of estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated by an example on Magnetic Resonance Imaging (MRI). We also discuss the issue of sample size estimation for dichotomous responses with two examples. RESULTS: For the continuous variable we found that the variance stabilizing transformation improves the asymptotic coverage probabilities on the within-subject coefficient of variation for the continuous variable. The maximum like estimation and sample size estimation based on pre-specified width of confidence interval are novel contribution to the literature for the binary variable. CONCLUSION: Using the sample size formulas, we hope to help clinical epidemiologists and practicing statisticians to efficiently design reliability studies using the within-subject coefficient of variation, whether the variable of interest is continuous or binary.

Algorithms↗

PubMed2005

Segmentation of cDNA microarray spots using markov random field modeling.

MOTIVATION: Spot segmentation is a critical step in microarray gene expression data analysis. Therefore, the performance of segmentation may substantially affect the results of subsequent stages of the analysis, such as the detection of differentially expressed genes. Several methods have been developed to segment microarray spots from the surrounding background. In this study, we have proposed a new approach based on Markov random field (MRF) modeling and tested its performance on simulated and real microarray images against a widely used segmentation method based on Mann-Whitney test adopted by QuantArray software (Boston, MA). Spot addressing was performed using QuantArray. We have also devised a simulation method to generate microarray images with realistic features. Such images can be used as gold standards for the purposes of testing and comparing different segmentation methods, and optimizing segmentation parameters. RESULTS: Experiments on simulated and 14 actual microarray image sets show that the proposed MRF-based segmentation method can detect spot areas and estimate spot intensities with higher accuracy.

Algorithms↗

PubMed2005

A temporal-spatial analysis of bovine spongiform encephalopathy in Irish cattle herds, from 1996 to 2000.

This study investigated the distribution of bovine spongiform encephalopathy (BSE) in herds of cattle in Ireland over the years 1996 through 2000, prior to the introduction of widespread active surveillance. Mappings of index herds, herd density, and standardized morbidity ratios, by county, were employed to help visualize areas of potential clustering of BSE. The hypothesis of spatial clustering was tested using a spatial scan statistic applied to the location of the herd where exposure likely occurred. Both Bernoulli and Poisson spatial models indicated marked clustering of BSE herds centred on Monaghan county, with secondary clusters detected by Bernoulli approaches and some Poisson models in Wexford and Cork. The number of cases increased with time, but clear temporal-spatial clusters were rarely detected, except in the case of a cluster in Wexford. The focussed spatial scan analyses using the location of large-scale feed suppliers provided support for the hypothesis that clustering of BSE may be associated with feed source. The results of our analyses provided strong evidence in support of the hypothesis that herds, in which animals were most likely to have been exposed to the BSE agent, cluster geographically.

Animals↗

PubMed2004

Assessment of reliability of microarray data and estimation of signal thresholds using mixture modeling.

DNA microarray is an important tool for the study of gene activities but the resultant data consisting of thousands of points are error-prone. A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. In this study, we describe an approach based on normal mixture modeling for determining optimal signal intensity thresholds to identify reliable measurements of the microarray elements and subsequently eliminate false expression ratios. We used univariate and bivariate mixture modeling to segregate the microarray data into two classes, low signal intensity and reliable signal intensity populations, and applied Bayesian decision theory to find the optimal signal thresholds. The bivariate analysis approach was found to be more accurate than the univariate approach; both approaches were superior to a conventional method when validated against a reference set of biological data that consisted of true and false gene expression data. Elimination of unreliable signal intensities in microarray data should contribute to the quality of microarray data including reproducibility and reliability of gene expression ratios.

Algorithms↗

PubMed2003

Associations between air emissions from sour gas processing plants and indices of cow retainment and survival in dairy herds in Alberta.

This paper describes the results of an investigation into the effects of air emissions from sour gas processing plants on indices of retainment or survival of adult female dairy cattle on farms in Alberta; namely, the productive lifespan of individual animals, and annual herd-level risks for culling and mortality. Using a geographical information system, 2 dispersion models--1 simple and 1 complex--were used to assess historical exposures to sour gas emissions at 1382 dairy farm sites from 1985 through to 1994. Multivariable survival models, adjusting for the dependence of survival responses within a herd over time, as well as potential confounding variables, were utilized to determine associations between sour gas exposure estimates and the time from the first calving date to either death or culling of 150210 dairy cows. Generalized linear models were used to model the relationship between herd-level risks for culling and mortality and levels of sour gas exposure. No significant (P < 0.05) associations were found with the time to culling (n = 70052). However, both dispersion model exposure estimates were significantly associated (P < 0.05) with a decreased hazard for mortality; that is, in cases where cattle had died on-farm (n = 8743). There were no significant associations (P > 0.05) between herd culling risks and the 2 dispersion model exposure estimates. There was no measurable impact of plant emissions on the annual herd risk of mortality.

Air Pollutants↗