News - 2019-2020 MSc. Biostatistics Modules and Short Courses Schedule

Session

Dates

1st Week

(Course Lecturer)

2nd Week

(Course Lecturer)

Dates

Pre-Session 1

4-8Mar19

STA6100a Databases

(Dr. Kondwani Munthali)

STA6100b Intro to STATA

(Mr. Tsilizani Kaombe)

STA6100c-Intro to R package

(Dr. Emmanuel Singogo)

11-15Mar19

Pre-Session 2

18-22Mar19

STA6100h Foundational Prob. & Stat

(Ms. Halima Twabi and

Ms. Fiskani Kondowe)

STA6100d Advanced Excel

(Mr. Patrick Sawerengera)

STA6100e-Intro to MATLAB

(Mr. Patrick Chidzalo)

25-29Mar19

Pre-Session 3

1-5 Apr19

STA6100f Intro to LaTeX

(Mr. Milliward Maliyoni)

STA6100g Survey design with CSPro

(Mr. Elias Mwakilama)

STA6100i Advanced Research and Academic Writing

(Dr. Jupiter Simbeye)

8-12Apr19

Session 1

27-31May19

STA6102 Probability and Distribution Theory

(Dr. George A. Luwanda)

The aim of this module is to provide an understanding of principles of probability theory and a thorough mathematical understanding of distribution theory. It covers basic rules of probability, random variables and their distributions, variate transformation and distribution function techniques; moment generating functions, characteristic functions and Cumulants. It also covers distributions of functions of random variables; Inversion theorems, distribution of order statistics, Sampling distributions of the mean and related functions; and limiting Distributions.

STA6105 Statistical Inference

(Prof. Lawrence Kazembe)

The aim of the module is to provide students with thorough mathematical understanding of statistical inference and derivation of various parameter estimation methods. The module covers principles of estimation, testing hypotheses, point and interval estimation, properties of estimators, estimation methods and procedures on formulation of hypotheses.

3-7Jun19

Session 2

15-19Jul19

STA6103 Generalised Linear Models

(Dr. Marc Henrion)

The module aims to provide an understanding of generalised linear models and their applications by providing basic theory associated with general and generalized linear models and practicalities of fitting regression models to data, interpreting and checking their adequacy. Topics covered include introduction to linear regression and ANOVA, fitting linear models, weighted and generalized least-squares estimation and transformations techniques. It also covers interpretation of the fitted models and parameter estimates, comparison of regression models and modeling using GLIM package.

STA6104 Experimental Designs

(Assoc. Prof. Jimmy Namangale)

This module provides an understanding of experimental designs and analysis of data arising from experimental designs. The topics of study include principles of good design, general theory of block designs, completely randomised and orthogonal designs, factorial experiments and Taguchi methods.

22-26Jul19

Session 3

9-13Sep19

STA6206 Bayesian data analysis

(Dr. Marc Henrion)

The aim of this module is to provide an understanding of Bayesian statistical models and demonstrating their usefulness in applied settings. The topics of study include principles of Bayesian estimation methods, hierarchical modelling, a comparison of classical and Bayesian approaches for similar problems.

STA6203 Time-to-event data analysis

(Dr. Mavuto Mukaka)

The aim of the module is to provide an understanding of statistical methods for analysing time to event data and their applications. This will enable students to derive survival analysis functions, compare intervention groups and provide regression models of survival data. Topics include survivor function, hazard functions and censoring, Kaplan-Meier survival curve, log-rank test, Cox’s Proportional Hazards Model, parametric models, assessing model fit, time-varying covariates and competing risks.

16-20Sep19

Session 4

4-8Nov19

STA6205 Correlated and Longitudinal data analysis

(Dr. Sarah White)

The module provides an understanding of statistical models and methods for the analysis of longitudinal data with a strong emphasis on applications. Topics covered include merits and approaches to longitudinal studies, design issues like bias, efficiency and sample size calculations. It also covers topics on exploratory data analysis using graphs and correlation structures, General and generalized linear models for longitudinal data, transition models including covariance structures and handling of missing data and the dropout process and joint modelling of longitudinal and time to event data.

STA6309

Programme Monitoring & Evaluation

(Ms. Cecelia Makupe)

The module aims to introduce students to concepts, study designs, and methods for monitoring and evaluating population programs. It covers topics on definition and concepts of monitoring and evaluation, principal types and frameworks for monitoring and evaluation, evaluation plan and data source, approach to quantitative and qualitative evaluation and their strengths and limitations.

11-15Nov19

Session 5

6-10Jan20

STA6302 Clinical Trials

(Prof. Samuel Manda)

The module aims to introduce specialist methods required to execute clinical trials, variety of methods of analysis available for clinical trials and discuss the ethical issues involved in clinical trials. It covers topics such as historical background and the need for randomized controlled trials and, standard methodology. It also covers issues of clinical trials protocols, sample size calculation including methods of analysis such as intention to treat, per protocol, interim analyses, meta-analysis and application of sequential methods. It further covers trial monitoring and safety issues.

STA6303 Principles of Epidemiology 

(Prof. Adamson Muula)

The module aims to provide an understanding of various statistical models used in epidemiology. Students will familiarize themselves with different types of study designs in epidemiology, analyse data from different study designs and will further appreciate the concept of risk and its measurement. The module will cover, in detail, concepts and measures of effect in epidemiology, statistical methods of analysing data from cross-sectional, prevalence, cohort and case-control study designs. The module will also cover issues of measurement error, confounding and interaction effects.

13-17Jan20

Session 6

2-6Mar20

STA6304

Modelling Infectious Diseases

(Dr. Levis Eneya)

The module provides an introduction to concepts and methods in transmission of communicable diseases. The topics covered include introduction to the epidemiology of infections, models in infectious disease epidemiology, dynamics of infection and measuring transmissibility of infections using measures such as incidence and attack rates, secondary attack rate, reproduction rate and adaptation of indirect transmission. This module will also include a practical problem on transmissibility and epidemic potential of a chosen infectious disease.

STA6203 Discrete data analysis

(Prof. Tobias Chirwa)

The module aims at providing a thorough understanding of statistical methods for discrete data through the theory and application of log-linear models for analysing discrete multivariate and discrete response data. The module covers a discussion of contingency tables throughout, Chi-square and exact tests and other measures of association, logistic regression and data analyses of multi-way contingency tables using log-linear models.

9-13Mar20

Session 7

27Apr-12Jun20

STA6106-Nonparametric methods

(Prof. Samuel Manda)

The purpose of this module is to provide students with thorough understanding and applications of non-parametric statistical methods. It provides a compendium of some of the better-known non-parametric techniques - alternative versions to the parametric tests. The module covers one-sample non-parametric tests such as ordinary sign tests, Wilcoxon signed rank test and their normal approximation, two-sample non-parametric tests, Kolmogorov-Smirnov goodness of fit test, two sample rank tests, types of scores, non-parametric versions of the randomized complete block designs and tests of randomness and independence among others, and Nonparametric regression

STA6204-Multivariate data analysis

(Assoc. Prof. Jimmy Namangale)

The module aims to provide an understanding on the theory of multivariate statistics and its applications including specifics of the concepts of principal component, cluster and factor analysis. Topics covered include multivariate data summary and graphical displays, estimation of mean and covariance in multivariate normal distributions, reduction of dimensionality. The module also covers principal component and factor analysis. Cluster analysis including discrimination, classification and correlation are covered in detail

15-29Jun20

Session 8

10-21Aug20

STA6306-Spatial statistics

(Prof. Lawrence Kazembe & Mr. James Chirombo)

The aim of the module is to provide an understanding of statistical inference methods for analyzing spatial (geo-referenced) data and disease mapping, when the locations or spatial arrangement of observations are important in the research problem. The module covers types of spatial data and their examples, geostatistical modelling approaches, autoregressive models, spatial autocorrelation and prediction. It also covers statistical image analysis, hierarchical modelling for spatial response data and spatio-temporal and spatial survival models. Computer implementations and exercises will be done using R.

STA6316-Measure and probability theory

(Mr. Nelson Dzupire)

The module provides advanced and thorough grounding in measure and probability theory. It covers topological spaces, Boolean algebras, and σ-algebra and probability spaces, measures and measurable functions. It also includes integration, conditional expectation and convergence theorems.

24Aug-4Sep20

Session 9

19-30Oct20

STA6312-Multilevel modelling

(Mr. Tsilizani Kaombe & Dr. Emmanuel Singogo)

The aim of the module is to highlight the problems that occur when the hierarchical nature of many social surveys is ignored in classical statistical analysis, with emphasis on applied problems. It is assumed that the students have completed at least Generalised Linear Modelling module and have a primary knowledge about Bayesian methods. Topics covered include random Effects ANOVA, random intercept models, three Level Regression Models and generalized linear multilevel models, multilevel logistic regression, multilevel models for ordered categorical variables and multilevel poisson models.