Short Courses and Workshops

We host short courses and workshops for researchers and students in Africa and globally. Recent courses have included:

Course overview

R is an open-source, statistical software platform that is growing in popularity due to its rapidly expanding amount of libraries containing cutting-edge statistical functions, as well as the user-friendly, built-in communication tools in RStudio. Course participants will not only be introduced to the basics of programming in R, but they will also learn how import and clean data in addition to visualising and reporting results. Specific topics that will be covered include: importing data; reshaping/tidying data; merging/joining datasets; handling dates, transforming numeric and categorical variables and summarizing data with plots and tables.

This course does NOT cover any form of hypothesis confirmation (e.g. using statistical tests, predictive or causal modelling), or methods used for “big data” -- all data will be small, in-memory datasets.

Lectures will be interwoven with practical exercises. Participants will be encouraged to follow along with exercises and programming on their own laptops.

Outcomes

Participants will learn the basics of:

  • R data types
  • R programming and style

And will acquire the skills to:

  • importing data
  • reshaping/tidying data
  • merging/joining datasets
  • handling dates and transforming numeric and categorical variables
  • summarising data with plots and tables

Target audience

The course is suitable for graduate students or professionals who are familiar with statistical analysis and have managed or interacted with datasets using other software platforms. It is assumed that course participants are familiar with programming in another language.

Presenter

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Dr Roxanne Beauclair

Dr Roxanne Beauclair is a specialist in applying biostatistical methods to epidemiological data. She holds a PhD in this field from Ghent University, and has launched her own statisticsl consultancy company, Data Yarn, based in Pretoria. She received training in Epidemiology (MPH) from the University of Cape Town in South Africa. She has been involved in an analytical capacity for several different epidemiological studies of sexual behaviour and HIV in Southern Africa. For her PhD research at Ghent University she studied how age-mixing patterns influence HIV transmission in the South African and Malawian contexts. Over the past few years she has become an R enthusiast and enjoys learning new ways to improve upon statistical programmes by creating clean, reproducible, and legible code.

Epidemiology is broadly defined as the study of the distribution and determinants of health-related states or events in specified populations and the application of this study to the prevention and control of health problems. Encompassing the methods and research designs needed to interpret evidence of health research, epidemiology is not only a discipline of the health sciences in itself, but also a much needed skill for practitioners in other branches of health research.

Epidemiology concentrates on questions like these: What are the determinants, distribution, and frequency of disease? Who gets ill and why? What are the crucial differences between those who become ill and those who do not? (Both sick and healthy people are to be studied.) How effective are interventions in treating disease, and in stopping further spread? How effective are health services in addressing health needs? What information do they need to make decisions? What is the risk assessment, and what is the threshold for action?

The 13 lecture series comprises: Introduction, basic tools; Demographic aspects; Cross-sectional designs; Construction of questionnaires; Case-control studies; Cohort designs, randomised controlled trials; Sampling; Screening and surveillance; Bias in design; Causation; Disease clusters, outbreaks; Hints on writing a research proposal; Hints on writing research papers and constructing conference presentations.

Outcomes

  • Understand and correctly apply the basic tools of epidemiology and research
  • Identify various research designs, know their uses and limitations
  • Understand the basic principles of sampling and apply that knowledge to everyday investigations
  • Understand the basics of screening, sensitivity and specificity of tests
  • Recognise bias in the design of investigations and try to avoid it
  • Understand the epidemiology of disease clustering and outbreaks and how to investigate them
  • Be aware of the principles of causation

Be able to report on their findings professionally, both in writing and orally.

Presenter

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Dr Jo Barnes was Senior Lecturer in Epidemiology and Community Health of the Faculty of Medicine and Health Sciences of the University of Stellenbosch at Tygerberg and is now semi-retired. She still presents courses in Epidemiology. She is still involved in research into the health impact and further consequences of pollution from failing sanitation in urban areas and pollution reaching rivers arising from using the water for drinking and irrigation of edible crops and livestock. She has long experience in water monitoring of the Berg and Eerste Rivers. She is a member of various water and conservation related bodies. Under her leadership her students investigated the dwellings and health status of inhabitants of low-cost housing schemes in the City and the impact of lack of sanitation on the urban environment. Other studies investigated the health status of old age pensioners in low income areas and the oral health status of children under 5 years in the Cape metropolitan area. She is an active member of the Disaster Management Forum of the Western Cape. She contributed health chapters for the disaster plans written for the district municipalities of West Coast, Cape Winelands, Uthungulu, Amajuba and the provincial disaster plan for Limpopo. She is a recipient of the Order of the Disa (Member Class) 2007 for meritorious services to the Province of the Western Cape, winner of the Women in Water, Sanitation and Forestry Award 2007 for the category Education and Awareness for awareness created on contamination of rivers, winner of the Cape Times/Caltex Environmental Award 2005 for the research work on contaminations of rivers and recipient of the Faculty of Health Sciences Award for Community Service for 2007.

Course overview

Introductory and intermediate courses in epidemiological methods teach students the concepts needed to begin a career conducting valid epidemiological research; however these courses typically only briefly cover the causal models that should underlie the design of valid epidemiological studies. We will use these models as a jumping-off point to begin rethinking what we have already learned and to go further in our understanding of basic concepts of measures of effect, confounding, misclassification and selection bias. From there we will begin to question the implications of various sources of bias in our studies and we will work through novel methods and approaches for doing more than simply speculating about these biases. We will then finish by exploring the basic statistics used in epidemiological research and we will correct misunderstandings about what these statistics can tell us.

Throughout the course we will focus on the core concepts of validity and precision and will further develop our understanding of these central concepts. We will emphasize the development of skills that every doctoral level epidemiologist should have, skills that are both practical and marketable. Note that this course is not offered for any credit. It is a course designed to help doctoral level and advanced master’s students advance their skills.

Readings

Students are expected to prepare fully for class by reading the material ahead of time. There will be several readings per day. The readings for this course are challenging.

Presenter

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Matthew Fox , DSc, MPH, is Professor in the Department of Epidemiology and Global Health at Boston University. Dr. Fox joined the Center in 2001. Before joining Boston University, he was a Peace Corps volunteer in the former Soviet Republic of Turkmenistan. His research interests include treatment outcomes in HIV-treatment programs, infectious disease epidemiology (with specific interests in HIV, pneumonia, and malaria), and epidemiological methods. Dr. Fox is currently working on ways to improve retention in HIV-care programs in South Africa from the time of testing HIV-positive through long-term treatment. Dr. Fox also does research on quantitative sensitivity analysis and recently co-authored a book on these methods, Applying Quantitative Bias Analysis to Epidemiologic Data. He currently teaches a third-level epidemiological methods class. Dr. Fox is a graduate of the Boston University School of Public Health with a Masters degree in epidemiology and biostatistics and a doctorate in epidemiology.

Course overview

Students of epidemiology are well versed in ways to reduce systematic error (bias) in the design of their studies and to describe random error in the analysis of their studies through confidence intervals and p values. However students are rarely taught methodologies for quantifying systematic error in their studies. Quantitative bias analysis (QBA) provides a methodology for assessing the impact of bias on study results by making assumptions about the bias parameters. QBA allows for assessment of both the direction and magnitude of systematic error and gives an estimate of effect (or a series of estimates of effect) that would have occurred had the bias been absent, assuming the bias parameters are correct. Such analyses allow investigators to go beyond speculation about the bias in discussion section of manuscripts and can be a powerful tool for quantifying the impact of such biases.

Based on the book co-authored by Dr. Fox*, this 3 day workshop will cover simple and multidimensional bias analysis methods that can be used to gain a better understanding of the impact of unmeasured confounding, selection bias and misclassification (measurement error) on study results. These methods can be applied to nearly any dataset, even summary data presented in the literature. Such approaches lay the foundation for more complicated methods, but by themselves, they act as if the bias parameters are known with certainty. We will then continue with probabilistic bias analysis which requires specification of probability distributions about the bias parameters and then uses Monte Carlo simulations methods to create intervals accounting for the uncertainty in the systematic error. Finally we will finish with methods for combining the systematic error to create simulation intervals that account for the total error (systematic and random) in the study results.

* Lash TL, Fox MP, Fink A. Applying Quantitative Bias Analysis to Epidemiologic Data Springer 2009.

Outcomes

Students who successfully complete this course, should be able to correctly:

  • Apply the methods to nearly any dataset, even summary data presented in the literature.
  • Utilize probabilistic bias analysis which requires specification of probability distributions about the bias parameters and then uses Monte Carlo simulations methods to create intervals accounting for the uncertainty in the systematic error.
  • Combine the systematic error to create simulation intervals that account for the total error (systematic and random) in the study results

Presenter

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Matthew Fox, DSc, MPH, is Professor in the Department of Epidemiology and Global Health at Boston University. Dr. Fox joined the Center in 2001. Before joining Boston University, he was a Peace Corps volunteer in the former Soviet Republic of Turkmenistan. His research interests include treatment outcomes in HIV-treatment programs, infectious disease epidemiology (with specific interests in HIV, pneumonia, and malaria), and epidemiological methods. Dr. Fox is currently working on ways to improve retention in HIV-care programs in South Africa from the time of testing HIV-positive through long-term treatment. Dr. Fox also does research on quantitative sensitivity analysis and recently co-authored a book on these methods, Applying Quantitative Bias Analysis to Epidemiologic Data. He currently teaches a third-level epidemiological methods class. Dr. Fox is a graduate of the Boston University School of Public Health with a Masters degree in epidemiology and biostatistics and a doctorate in epidemiology.

Course overview

In recent years there has been an increasing interest in the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First, when focus is on a survival outcome and we wish to account for the effect of endogenous time-dependents covariates measured with error (e.g., biomarkers), and second, when focus is on the longitudinal outcome and we wish to correct for non-random dropout.

This course is aimed for applied researchers and graduate students and will provide a comprehensive introduction into this modeling framework. In particular, we will explain when these models should be used in practice, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data. Emphasis will be given on applications and the use of the R packages JM and JMbayes.

This course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood, and regression models. In addition, basic knowledge of R would be beneficial but is not required.

Presenter

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Dr Dimitris Rizopoulos is Professor of Biostatistics at the Erasmus University Medical Center. He received an MSc in Statistics (2003) from the Athens University of Economics and Business, and a PhD in Biostatistics (2008) from the Katholieke Universiteit Leuven. Dr. Rizopoulos wrote his dissertation, as well as a number of methodological and applied articles on various aspects on models for survival and longitudinal data analysis, and he is the author of a recent book on the topic of joint models for longitudinal and time-to-event data. He has also written two freely available packages to fit this type of models in R under maximum likelihood (i.e., package JM) and the Bayesian approach using JAGS, WinBUGS or OpenBUGS (i.e., package JMbayes). He currently serves as an Associate Editor for Biometrics and Biostatistics, and he has been a guest editor of a special issue on joint models in Statistical Methods in Medical Research. More details on his research are found at http://www.drizopoulos.com .

Course overview

In the last two decades the Bayesian approach has become increasingly popular in virtually all application areas. The approach is especially known for its capability to tackle complex statistical modeling tasks. The aim of this course is to introduce the participants smoothly into Bayesian statistical methods, from basic concepts to hierarchical models, model building and model testing. Numerous biostatistical examples (e.g. meta-analyses, longitudinal studies including growth curve modelling, analysis of clinical trials, etc.) illustrate the theoretical concepts. The course is scheduled into classroom teaching and computer exercises, and uses the software packages WinBUGS, OpenBUGS, JAGS and but also their interfaces with R making use of R2WinBUGS, R2OpenBUGS and RJAGS. The course is based on a recently published Wiley book of Lesaffre and Lawson, entitled Bayesian Biostatistics. Each participant will receive a copy of this book, included in the course fee.

The course assumes a good knowledge of regression techniques (linear, logistic, etc.) and some knowledge of models for correlated data. Experience with R is beneficial though not essential; however programming skills are required for the course.

Presenter

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Emmanuel Lesaffre is Professor of Biostatistics at L-Biostat, K.U.Leuven, Leuven, Belgium. His research interests include Bayesian methods, longitudinal data analysis, statistical modelling, analysis of dental data, interval censored data, misclassification issues and clinical trials. He has written more than 350 papers in peer-reviewed statistical and medical journals. He is the founding chair of the Statistical Modelling Society, past-president of the International Society for Clinical Biostatistics and fellow of ISI and ASA.

Course overview

Many statistical practitioners make use of the Bayesian approach because it allows analyses on highly structured data. An important class of models involves the analysis of follow-up studies, i.e. longitudinal-, survival studies or a combination of the two. We will illustrate the Bayesian approach for the analysis of such data, by means of examples, and focus on the analysis of longitudinal studies. For instance, Bayesian implementations will be illustrated on (generalized and non-linear) linear mixed models with non-standard distributions for the random parts, growth curve models, pharmaco-kinetic models, multivariate mixed models, joint mixed models of several random variables, longitudinal models with smooth subject-specific evolutions, longitudinal models with informative measurement times, etc. Finally, we will look at joint modeling of the survival and longitudinal process. Examples will be analysed using WinBUGS/OpenBUGS/JAGS and R-versions of them, but also dedicated R-software.

The 5-day course will consist of theoretical sessions each morning, and practical sessions each afternoon, when participants will work on their laptops with tutor assistance, and (optionally) in small groups. A provisional outline programme is available on request.

The course is designed for applied statisticians and epidemiologists with a solid statistical background. Required skills and knowledge are: programming in R or SAS®, statistical inference, (linear, logistic, Cox) regression models and basic knowledge of Bayesian methodology. It will be advantageous to have practical experience in modelling longitudinal and survival studies.

Course material will be made available in hardcopy as well as electronically, to each participant. Some recommended reading for the course:

  • Bayesian Biostatistics, E. Lesaffre and A. Lawson (2012), John Wiley and Sons
  • Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis, M. Daniels and J. Hogan (2008), CRC Chapman and Hall, Boca Raton

Presenter

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Emmanuel Lesaffre is Professor of Biostatistics at L-Biostat, K.U.Leuven, Leuven, Belgium. His research interests include Bayesian methods, longitudinal data analysis, statistical modelling, analysis of dental data, interval censored data, misclassification issues and clinical trials. He has written more than 350 papers in peer-reviewed statistical and medical journals. He is the founding chair of the Statistical Modelling Society, past-president of the International Society for Clinical Biostatistics and fellow of ISI and ASA.

 

Participant feedback

The course was described as oriented towards an applied audience with a good knowledge of various regression models. It was emphasised that Bayesian concepts would (because of the 2-day format) be introduced only briefly; hence a prior course on the Bayesian approach would be helpful. It was also stated that knowledge of R would be useful for the course, but no prior knowledge of WinBUGS would be assumed, although this also would be helpful, as would some knowledge of classical repeated measurements analysis and classical survival analysis. Two books were recommended as background reading: Lesaffre & Lawson, Bayesian Biostatistics, and Daniels & Logan: Missing Data in Longitudinal Studies.

Target audience

Participants will understand software engineering perspectives and apply these to their project work. They should leave with improvements to the project they bring, as well as with a plan for future development.

Objectives

Participants will understand software engineering perspectives and apply these to their project work. They should leave with improvements to the project they bring, as well as with a plan for future development.

Expected outcomes

Participants will aquire skills to :

  • Think about the overall architecture of scientific programming workflows, and make design choices that improve scientific output reproduce-ability, maintainability, and flexibility
  • Apply foundational software engineering principles for requirements elicitation, software architecture design and quality assurance
  • Use various software engineering tools--e.g. version control, automated testing--to improve their productivity, work quality, and ability to collaborate
  • Evaluate the benefits and costs of re-using existing libraries, and make their code usable by other scientists
  • Evaluate the data component of a scientific project, and make informed choices about input and output products and formats for that work
  • Think about parallelization and how a scientific project might best leverage high performance computing resources
  • Use the Amazon Web Services Cloud environment for high performance computing work;

Presenters

Carl Pearson is a Research Fellow at the London School of Hygience and Tropical Medicine (UK). Tom Hladish is a Research Scientist at the University of Florida in the Department of Biology and the Emerging Pathogens Institute (USA).Arlin Stoltzfus is computational evolutionary biologist and bioinformatician at the Institute for Bioscience and Biotechnology Research (IBBR) and Office of Data and Informatics, Material Measurement Laboratory, National Institute of Standards and Technology (USA).

All of the presenters have extensive experience in developing software for scientific research and communication. Carl and Tom have been working with participants in the SEAMS workshops for several years, and Arlin regularly leads local hack-a-thons.

The teaching assistants for the workshop are active junior scientists and past SEAMS workshop participants.

Clinic on Meaningful Modeling of Epidemiological Data (MMED)

MMED is a 2­-week modeling clinic that emphasizes the use of data in understanding infectious disease dynamics.

The Clinic will bring together graduate students, postdoctoral students, and researchers from around the world, with the goal of engaging the participants in epidemiological modeling projects that use real data to grapple with practical questions in a meaningful way.

The Clinic will consist of a series of discussions and tutorials that will guide participants through the process of data-based, dynamical models of disease spread. Participants are encouraged to bring data sets and questions to the Clinic, and to start collaborative projects with each other or the organizers. Various statistical and dynamical modeling paradigms will be discussed. Computer exercises and group projects will reinforce and extend the various concepts covered. Participants are encouraged to continue research projects begun during the Clinic when they return to their home institutions and will be required to complete a written report on group projects. The Clinic is targeted towards students and researchers that have prior knowledge of mathematical epidemiology but have not yet established themselves in the field. All participants will be required to present on previous or ongoing research that is related to the Clinic's themes. Visit http://www.ici3d.org/mmed/ for more workshop details.

Clinic on Dynamical Approaches to Infectious Disease Data (DAIDD)

This 1-week intensive modeling clinic will provide an introduction to dynamical models used in the study of infectious disease dynamics.

The Clinic will bring together postgraduate students and researchers from around the world, and instruction will focus on how the complex dynamics of pathogen transmission influence study design and data collection for addressing problems in infectious disease research. The Clinic will consist of a series of interactive lectures and tutorials that will guide participants through the uses of dynamical modeling in epidemiology. Various modeling paradigms will be discussed, with a particular focus on the development of compartmental models, and participants will be given guidance regarding the appropriate use of models to address their own research questions. Working closely with their peers and with Clinic faculty, each participant will develop a research plan that describes a roadmap for integration of dynamic modeling with data collection and/or analysis in a study system of their choosing. The research plan can be used as a framework for grant or dissertation proposals when participants return to their home institutions. This workshop is not intended for those with substantial prior experience in dynamical modeling. Visit http://www.ici3d.org/daidd/ for more workshop details.

Visit this page again for information on more upcoming courses.

DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis