What's On at UWA

The aim of this course is to introduce you to basic statistics. It will cover descriptive statistics (means and standard deviations); data exploration; basic categorical data analysis; simple linear regression and basic analysis of variance (ANOVA).

Subsidised rates are available for UWA Graduate Research Students.

Please register online.

Unfortunately this event has been cancelled.

Due to unforeseen circumstances the presenter is no longer available to deliver this course.

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This course aims to provide you with an introduction to the facilities available in MS Excel from a statistical point of view. As well as an introduction to Excel, spreadsheet functions and graphics, it concentrates on performing basic statistical methods, producing charts and tables, and discusses the limitations of Excel when it comes to more complex statistical analysis.

Discount fees are available to UWA Postgraduate Research Students.

Statistical meta-analysis deals with a variety of sophisticated statistical methods to efficiently combine the results of several studies that have a common target. In this course, we will describe the basic concepts of effect size for continuous measurements as well as qualitative attributes, combination of tests and estimates of effect size, tests for homogeneity of effect sizes, fixed versus random effects model of meta-analysis, combination of Gallup polls, meta-analysis of binary data, meta-regression, and publication bias.

The aim of this course is to introduce you to basic statistics. It will cover descriptive statistics (means and standard deviations); data exploration; basic categorical data analysis; simple linear regression and basic analysis of variance (ANOVA).

The course is subsidised for UWA postgraduate research students.

Our Epidemiology and Control of Communicable Diseases unit is designed to allow students to understand the principles of infectious disease emergence and transmission using current cases and examples.

The unit reviews the traditional tools of outbreak and epidemic investigation, combined with ideas on environmental change as a major driving force behind disease emergence.

Topics covered include:

- Australian and global perspectives on emerging and re-emerging infectious diseases - links between environmental change and infectious diseases - infections from food and water sources - vector-borne illnesses, with a major emphasis on Australian examples - principles of infectious disease transmission - infection patterns in at-risk populations - infectious disease surveillance - the assessment and management of epidemics and outbreaks - bioterrorism - successes and failures in infectious disease control

Unit outline

The five-day unit will consist of a combination of lectures, practical case studies and discussions of recent regional and global outbreaks. In preparation for the teaching week, pre-reading will be sent out in mid June.

Who should attend?

Anyone with an interest in infectious diseases, microbiology and/or environmental health issues is welcome. This includes microbiologists, health department staff, doctors, nurses, students of public health, researchers, managers, ecologists and environmental scientists, and anyone working in areas relating to infectious disease prevention and control.

This unit is available for postgraduate students as a 6 point unit. It is also available for professional development. Fees apply, please visit our website for details.

The course is designed for those who have no experience with R, but have a basic understanding of statistics. Those without this experience are encouraged to attend the Introductory Statistics course first.

This course covers material ranging from Kaplan Meier curves to the analysis of censored data and Cox's proportional hazards models.

Participants must bring their own laptop with R installed.

This course covers techniques that can be used to analyse data with continuous and categorical variables. The course will begin with simple linear regression and then proceed with approaches that can be used with more than two variables such as multiple regression. ANOVA with interactions and blocking will also be covered. The course will end with techniques that address the analysis of binary or ordinal variables.

Subsidised fees are available for UWA Graduate Research Students.

This course is aimed at anyone wishing to improve their survey questionnaires. This course is useful for both people new to questionnaire design and those who have experience and would like to extend their knowledge. It will be a benefit not only for people who anticipate designing a questionnaire in the future, but for those in the role of critiquing commissioned or existing research.

UWA Postgraduate Research students receive subsidised fees.

The Nurses’ Health Studies are among the largest and longest running investigations of factors that influence women’s health. Started in 1976 and expanded in 1989, the information provided by the 238,000 dedicated nurse-participants has led to many new insights on health and disease. While the prevention of cancer is still a primary focus, the study has also produced landmark data on cardiovascular disease, diabetes and many other conditions. Most importantly, these studies have shown that diet, physical activity and other lifestyle factors can powerfully promote better health.

R is a free and extremely powerful language and software environment for statistical computing, data analysis, and graphics. The course is designed for those who have no experience with R, but have a basic understanding of statistics. The course will include: Introduction to R: How to install R on your computer; basic R commands, how to use and understand the R help pages. Data: Reading in data and data manipulation; summarising data; basic statistical analysis and fitting linear models. Graphics and output: Basic plotting commands and how to customise your plots; how to export your plots and output in a user-friendly format. Functions: Writing simple functions and flow control structures.

Professor Tilling main areas of expertise are in methods for analysing exposures and outcomes across the life-course, and in methods for dealing with missing data. She has a mathematical background, an MSc in Applied Statistics and a PhD in Epidemiology, together with more than 15 years’ experience working in academic public health and epidemiology. Prof Tilling enjoys interpreting and explaining results from more complex statistical methods to other researchers and members of the public and using all forms of the media to effectively promote public health messages.

Areas of interest: Childhood growth; Life-course analyses; Trajectories of change in disease outcomes (E.g. PSA in prostate cancer, EDS in Multiple Sclerosis; Complex inter-relationships which change over time (E.g. fat mass and physical activity, risk factors for CHD; Methodological interests: Methods to examine and correct for bias due to missing data; Structural equation models, particularly for longitudinal data; Multilevel models for growth trajectories; Fractional polynomial and linear spline models Time-varying confounding - marginal structural models and g-estimation

The course will cover material ranging from a review of simple one-way ANOVA, to more complex designs and analyses including crossed and nested factors with fixed and random effects.The emphasis throughout will be placed on applications rather than theory. The statistical package R and R Commander will be used and some familiarity with this will be assumed.

Clinical Epidemiology focuses on the application of epidemiologic research principles to questions relating to clinical diagnosis, prognosis and the efficacy of therapeutic interventions.

Unit outline

This five-day unit considers:

- design, conduct and analysis of clinical trials

- non-experimental study designs in the evaluation of clinical outcomes

- systematic reviews and meta-analysis

- evaluation of diagnostic tests

- economic analysis

- ethical issues in clinical research

Within an evidence-based practice framework, emphasis will be placed on the critical appraisal of the scientific research evidence that underpins clinical practice (from primary studies to clinical practice guidelines).

Overall, the course aims to improve the participant's knowledge of clinical research methods and to develop a critical approach to the incorporation of research into clinical care decisions. In addition to formal lectures and 'hands-on' tutorials, the course will promote discussion and debate about clinical research and the sharing of clinical research experiences.

One of the outcomes of the unit will be an increased level of competence in preparing a competitive research grant proposal. As such participants will be required to work within a multidisciplinary research team to develop a (hypothetical) clinical research proposal, from the refinement of the research question, to study design, data collection, statistical analysis, ethical issues and budget.

This five-day unit will consist of a combination of lectures, small-group tutorials, practical demonstrations, discussion and ‘hands-on’ experience in research proposal development.

In preparation for the teaching week, you will be sent pre-reading on 11 November 2013.

Who should attend?

Health professionals and health researchers interested in gaining a better understanding of clinical research methods and critical appraisal of the scientific literature related to clinical practice.

This unit is available for postgraduate students as a 6 point unit. It is also available for professional development. Fees apply, please visit our website for details.

This is an intensive five-day unit on the theory and practice of analysis of large sets of linked administrative health data at an introductory to intermediate level.

Rapid growth in data linkage projects has led to a shortfall in analyst skills. Some researchers understand epidemiological principles, but are unfamiliar with the specialised computing skills needed to analyse linked data files. Others have a strong grasp of computing concepts, but lack an adequate theoretical base to design high quality applications to answer research questions. This unit endeavours to fill a gap in research training opportunities to cater to these two areas of need.

Unit outline

Professor Holman provides a theoretical grounding in the classroom on each topic, followed by a training session on the corresponding computing solutions. Students use fictitious but realistic linked data files in the hands-on exercises. A lecturer will be available in the computing laboratory session each afternoon and conducts an end-of-day tutorial for those who need additional assistance.

In preparation for the teaching week you will be sent pre-reading on 30 November 2013.

Learning objectives

The unit acquaints health researchers, clinical practitioners and managers with the theory and skills needed to analyse linked health data at the introductory to intermediate level. Upon completion the participant will:

- possess an overview of the theory of data linkage methods and features of comprehensive data linkage systems, sufficient to understand the sources and limitations of linked health data sets

- understand the principles of epidemiologic measurement and research methods for the conceptualisation and construction of numerators and denominators used in the analysis of disease trends and health care utilisation and outcomes

- understand sources of error in epidemiologic measurement, the difference between confounding and effect modification, and use of regression models in risk adjustment in health services research

- be able to perform statistical analyses on linked longitudinal health data

- be able to conceptualise and perform the manipulation of large linked data files

- be able to write syntax to prepare linked data files for analysis, derive exposure and outcome variables, relate numerators and denominators and produce results from statistical procedures

Unit prerequisites

Basic familiarity with computing syntax used in programs such as SPSS, SAS or Stata and methods of basic statistical analysis of fixed-format data files.

There are no formal prerequisites in epidemiology for the course. However, it is recommend that participants who have not previously completed an introductory course in epidemiology, familiarise themselves with the basic principles and terms used in that discipline. A working knowledge of statistical concepts, including regression models, used in data analysis in the medical and social sciences is assumed.

This unit is available for postgraduate students as a 6 point unit. It is also available for professional development. Fees apply, please visit our website for details.

The aim of this course is to introduce you to basic statistics. It will cover descriptive statistics (means and standard deviations); data exploration; basic categorical data analysis; simple linear regression and basic analysis of variance (ANOVA). The statistical package SPSS will be used to illustrate the ideas demonstrated. The course will be held in a computer laboratory allowing participants to immediately apply the material covered through a series of practical examples.

The aim of this course is to introduce you to basic statistics. It will cover descriptive statistics (means and standard deviations); data exploration; basic categorical data analysis; simple linear regression and basic analysis of variance (ANOVA). The statistical package SPSS will be used to illustrate the ideas demonstrated. The course will be held in a computer laboratory allowing participants to immediately apply the material covered through a series of practical examples.

R is a free and extremely powerful language and software environment for statistical computing, data analysis, and graphics. The course is designed for those who have no experience with R, but have a basic understanding of statistics. The course will include: Introduction to R: How to install R on your computer; basic R commands, how to use and understand the R help pages. Data: Reading in data and data manipulation; summarising data; basic statistical analysis and fitting linear models. Graphics and output: Basic plotting commands and how to customise your plots; how to export your plots and output in a user-friendly format. Functions: Writing simple functions and flow control structures.

The course is designed for people with knowledge of basic statistics who want to learn more about regression and analysis of variance (ANOVA).

This course covers techniques that can be used to analyse data with continuous and categorical variables. The course will begin with simple linear regression and then proceed with approaches that can be used with more than two variables such as multiple regression. ANOVA with interactions and blocking will also be covered. The course will end with techniques that address the analysis of binary or ordinal variables.