MASTER OF SCIENCE DEGREE IN BIOSTATISTICS AND EPIDEMIOLOGY (MSBE)
PROGRAMME OVERVIEW
The major aim of the program is to provide students with an in-depth understanding of the design, analysis and interpretation of different epidemiological studies.
Study designs to be covered include cross-sectional, case-control, cohort and intervention studies.
The HIV/AIDS pandemic has also necessitated the mathematical statistics approaches in order to curb its unprecedented spread, through the design of intervention evaluation of new therapies for its treatment and prevention.
ENTRY REQUIREMENTS
Normal Entry: A good Honours degree in Mathematics, Applied mathematics,
Statistics, Operations Research (or their equivalence), Medicine, Epidemiology and any other related programs, with upper second class or better. Applicants with a lower class and relevant experience may be considered. Such applicants may have to pass either an entrance examination or interview to determine their suitability for the programme.
CAREER OPPORTUNITIES AND FURTHER EDUCATION
4.1 Employability: Research scientists in health institutions. The disciplines of epidemiology and biostatistics are essential to achieving the goals of public health. Both fields consider population health concerns, and both fields have the ultimate goal of promoting overall health and disease prevention in a specific community, academic research and in the private medical and health care fields. Data analysts, software developers, risk analysts in health institutions, epidemiologists in medical laboratories and lecturers in tertiary institutions.
4.2 Further Studies: Doctoral studies in Statistical Modelling , Epidemiology and/or Biostatistics as well as in any disciplinary programmes related to the modules offered in this programme.
PROGRAMME STRUCTURE
| N.B. *Denotes core modules which are not Minimum Body of Knowledge and Skills
**Denotes core modules which are Minimum Body of Knowledge and Skills |
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Level 1 Semester I
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| ** MSBE8301 Fundamentals of Biostatistics 18 | |||||
| ** MSBE8302 Research Methods in Epidemiology 18 | |||||
| ** MSBE8303 Principles of Epidemiology 18
* MSBE8307 Applied Multivariate Analysis 18 |
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| * MSBE8309 Further Time Series Analysis 18 | |||||
| Level 1 Semester II | |||||
| **MSBE8304 Survival Analysis in Epidemiology 18 | |||||
| **MSBE8305 Experimental Design and Analysis of
Clinical Studies. 18 |
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| * MSBE8306 Generalized Linear Models 18 | |||||
| * MSBE8308 Infectious Disease Modelling 18
* MSBE8312 Health Insurance 18 Level Two Semester I |
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| **MSBE8370 Dissertation 180 | |||||
SYNOPSES
MSBE8301 Fundamentals of Biostatistics
Review of basic statistics and its applications; univariate and bivariate descriptive statistics, probability and statistical distributions, statistical inference- confidence intervals and hypothesis testing, confidence intervals for case-control and cohort studies, sample size calculation, correlation and regression analysis, introduction to survival analysis, standardization of rates, use of statistical software such as Excel, Epidata, EpiInfo and Stata for data management and analysis.
MSBE8302 Research Methods in Epidemiology
Review of statistical methodology from epidemiological viewpoint, including cohort and case-control designs, relative odds (odds ratio) and relative risks. Computational statistics using appropriate software packages. Sampling techniques, data collection strategies, questionnaire construction, sampling, health surveys.
MSBE8303 Principles of Epidemiology
Measures of disease frequency. Observational studies, Case control studies, Cross sectional surveys, outline data-based studies, individual level, Ecological studies. Logistic Regression; interpretation of parameter estimates; probabilities and odds; comparison of classical discriminant analysis and logistic regression. intervention studies, Clinical trials, Field trials, Individual and aggregated levels, sample size determination for each study design, biases and their control in study design, confounding and methods of control and effect modification.
MSBE8304 Survival Analysis in Epidemiology
Examples of Survival Data, Analysis of dichotomous data, Introduction to Survival Analysis, Censoring and Truncation, nonparametric estimation methods, Hypothesis Testing for survival data. Refinements to Proportional Hazards method, Regression Diagnostics and inference. Models Preliminaries – survival analysis in Stata. Shapes of hazard and survival functions, Estimation of the (integrated) hazard and survivor functions: Kaplan-Meier product-limit and life-table methods, Estimation: (i) continuous time (ii) discrete time models, Unobserved heterogeneity, Competing risk models.
MSBE8305 Experimental Design and Analysis of Clinical Studies
Theoretical, Methodological and Measurement Issues in the Clinical Research Model. Single-Subject Designs: AB and ABAB designs, Withdrawal or reversal designs, tailor-made designs for particular practice situations. Theoretical, Methodological and Measurement Issues in Program Evaluation, Evaluation Utilization and Reporting. Practice effectiveness studies in social work, Identifying and operationalizing goals, objectives, and interventions. Evaluation, Experimental and non-experimental designs. Data Analysis: Qualitative and Computer analysis using SPSS-X. Clinical versus statistical significance.
MSBE8306 Generalized Linear Models
Theory and applications – regression and ANOVA to non-normal data. Logistic, log-linear models, and gamma regression models. Review of the GLM for Normal Data: Linear regression, fixed- and mixed-model ANOVA, ANCOVA, Extending the GLM: Non-normal error structure, The exponential class, Linear and non-linear link functions, Theory of Estimation and Model Fitting: Likelihood functions and maximum likelihood, Iteratively reweighted least squares, Theory of Statistical Inference: Analysis of deviance, Likelihood ratio tests, Wald tests, Confidence regions, Classical normal-based models, Extending the exponential class, over-dispersed models, Quasi-likelihood models, Generalized estimating equations, polytomous response models.
MSBE8307 Applied Multivariate Analysis
P-Values, Nonparametric Hypothesis Tests and their properties (ARE’s), Nonparametric Confidence Intervals, Fundamentals of Data Manipulation, Standardization, statistical distance, Matrix, Sum of Squares; Covariance Structure, Multivariate Analysis of Variance, Violating Standard Parametric Assumptions. Power of statistical tests. Path Analysis, Direct and Indirect effects, partial correlation, coefficients; causal modelling, Covariates and repeated measures in experimental designs. Concomitant or covariates in ANOVA, parallel regression lines. Two Group Discriminant Analysis Classification, Multiple Group Discriminant Analysis, Dimensionality of discriminant functions, Quadratic discriminant, Stepwise discriminant analysis.
MSBE8308 Infectious Disease Modelling
Background: examples of problems and issues, types of models. Threshold behavior. Implications for disease control. Data: evidence for the limitations of mass-action model. Multi-group epidemiological models. Implications for disease control. Stochastic invasion theorems, final epidemic size distributions. Importance of population size; limitations of deterministic approaches. Bartlett’s dynamics of childhood diseases. Small Community Models and Statistics. Stochastic models for disease outbreaks. Parameter estimation. Evolutionary and Spatial issues, Network models, Within-host models. Emergence of drug resistance. Case Studies.
MSBE8309 Further Time Series
The Seemingly Unrelated Regression and Multivariate Regression Models. Generalized Method of Moments, “Large Sample Properties of Generalized Method of Moments Estimators”, Nonlinear Systems of Equations, Univariate Time Series Lagged Variables, Simultaneous Equations Models, Multiple Time Series, Panel Data Models, Models with Discrete and Limited Dependent Variables, Resampling and Simulation Techniques: Monte Carlo, Bootstrap, Gibbs Sampler, and Simulated Method of Moments.
MSBE8312 Health Insurance
Basic principles of Health Insurance and Managed Care, History Health Insurance and Managed Care, key trends in Health Insurance and Managed Care, definition of insurance, Organized Delivery System, Risk, Capitation, and Other Financial Issues in Managed Care, Risk adjustment, Health Insurance models – Traditional (Fee-for-Service) Indemnity, “Managed” Indemnity, Preferred Provider Organization (PPO), Health Maintenance Organization (HMO), Taxonomy for determining the type of Health Insurance Plan. Consumer-Directed Health Plans (CDHPs). Case studies on national and global trends as well as practises in health insurance.
Study Session 1: An introduction to epidemiology and implication of chronic non-communicable diseases
Study Session 2: Demographic, epidemiological and nutrition transition
Study Session 3: Social Determinants for non-communicable diseases
Session 1 explains what chronic diseases are, and the diseases that fall into this category. We also start to develop an overview of why they are a concern. We also highlight the burden associated with chronic disease globally and in developing countries, dispelling assumptions that certain parts of the world are completely unaffected by chronic diseases. Basic concepts in chronic disease epidemiology are also defined.
Session 2 discusses demographic, epidemiological and nutrition transition and how these relate to the development of chronic diseases.
Session 3 looks at how social determinants of health are associated with non communicable diseases
MSBE8370 Dissertation
The student undertakes a project based on a thorough study of some mathematical and/or statistical aspects of the theory with reference to applications. The project will be supervised by a departmental lecturing member of staff and should therefore be of high standard.