PhD

Applied statistics, Applied statistical methods for prediction modelling of upper limb functional recovery after stroke. Doctoral thesis, Keele University. UK †

prediction modeling [LECTURER]

Assistant researcher at KEELE UNIVERSITY

prediction modeling [assist dean/ Assistant Professor]

Teaching Lecturer in Department of Statistics and Informatics Technologies, NTU, IRAQ

Early prediction of upper limb functioning after stroke using clinical bedside assessments: a prospective longitudinal study

Journal scientfic research

publisher Nature Springer

Early and accurate prediction of recovery is needed to assist treatment planning and inform patient selection in clinical trials. This study aimed to develop a prediction algorithm using a set of simple early clinical bedside measures to predict upper limb capacity at 3-months post-stroke. A secondary analysis of Stroke Arm Longitudinal Study at Gothenburg University (SALGOT) included 94 adults (mean age 68 years) with upper limb impairment admitted to stroke unit). Cluster analysis was used to define the endpoint outcome strata according to the 3-months Action Research Arm Test (ARAT) scores. Modelling was carried out in a training (70%) and testing set (30%) using traditional logistic regression, random forest models. The final algorithm included 3 simple bedside tests performed 3-days post stroke: ability to grasp, to produce any measurable grip strength and abduct/elevate shoulder. An 86–94% model

Evaluating Associations between Physical Activity and Growth, Academic Attainment, and Socioeconomic Factors in Primary School Children - A Prospective Cohort Study

publisher Nature Springer

Aim: Previous research has shown that physical activity is positively associated with growth and academic attainment in primary school children. The aim of this study was to determine if this association is repeated and to identify differences in personal, social, and environmental factors that contribute to physical activity and academic attainment. Methods: Physical activity status was determined using the PAQ-C and measurements of mass and height were recorded and BMI calculated. Academic attainment was measured using nationally standardised end of year tests. Participants completed the Newcastle Food School Study questionnaire. Parents of participants provided information on their education, family income, profession and completed the ALPHA Environment Questionnaire. A Chi-square test of homogeneity and Independent Samples T Tests were used to determine if differences exist between children who were more or less active. Based upon these results, significant predictors were selected and included in a logistic regression model in to analyse their ability to predict educational attainment. Results: The mean of the mass children who were more active followed the growth expected trajectory, whereas those who were less active demonstrated a loss in mass at the January measurement. Children who were more active were 27.72 and 12.59 times more likely to achieve average or above performance in literacy and reading than less active children. In mathematics, children whose parents worked in professional occupations, were 28.38 times more likely to achieve average or above than those with manual occupations. There were no significant differences between children in personal, social and environmental factors. Conclusion: This study confirms previous findings which reported that there does appear to be an association between physical activity and body mass and academic performance in primary school children, with lower levels of reported physical activity being associated with negative effects.

Improving predictor selection for injury modelling methods in male footballers

publisher Nature Springer

Abstract Objectives This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football. Methods Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team’s 2015/2016 season. Results The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of ‘no-play’ and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury. Conclusion For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.