Biostatistics for Clinical and Public Health Research
This short course introduces clinical and public health researchers to biostatistics as applied to public health and management studies. Biostatistics is the science of describing, summarising and analysing health-related data. It is essential to understand biostatistics in order to design, conduct and interpret health-related research. The basic principles and methods used in biostatistics are covered in this short course, including the technical qualifications necessary for analysing and interpreting data on a descriptive and bivariate level.
Topics include: classification of health data; summarising data using simple statistical methods and graphical presentation; sampling distributions, quantifying uncertainty in results from a sample; statistical distributions; comparing two/more groups/methods using confidence intervals and hypothesis tests (p-values); assessing the association between an outcome and an exposure using the chi-squared test; risk comparisons (RR & OR); prediction of an event or identifying risk factors for an event of interest where the event is measured on a continuous scale or a binary scale (yes/no); sample size calculations.
Attendees will be very briefly introduced to the computerised statistical package, SPSS. However, if you wish to become proficient in using SPSS, it is recommended you register for the Introduction to Data Analysis: SPSS Without Tears course. The SPSS short course theory will be complemented by the use of applied examples and exercises to enhance understanding and facilitate development of practical skills. Completion of this Biostatistics for Clinical and Public Health Research short course (or equivalent training) is required prior to registering for the SPSS short course.
● Discuss classification of data, various epidemiological studies, and method of data collection for evaluating research question in the concerned Epidemiological Studies
● Summarise data (using graphical display and descriptive statistics) that has been collected from the concerned populations
● Discuss the probability distributions for variables collected in public health studies (Application Level) and evaluate the sampling variability (uncertainty) in data analysis results
● Discuss the statistical significance of a hypothesis about the study population based on sample data (normality assumption holds) and evaluate the difference between two groups
● Formulate boundary values for parameters in the study population and evaluate the precision in its estimates based on sample data (normality assumption holds)
● Evaluate the difference between more than two groups based on sample data where data in each group follows the normal distribution
● Evaluate the difference between two or more groups when normality assumption does not hold or data is ordinal or discrete
● Explain the relationship between an outcome (continuous scale) and exposures and identify the truly independent exposures for the outcome of interest
● Evaluate the risk that an event (binary) will occur given a particular exposure, compared to the risk of the event occurring in the absence of that exposure
● Explain the relationship between a binary event and exposures and identify the truly independent exposures for the event of interest
● Evaluate the association between event (categorical) and exposure where either or both can have multiple categories
● Power analysis and sample size calculation
Who should attend?
This course is targeted at medical doctors, nurses, physiotherapists, clinicians, epidemiologists, psychologists, pharmacists, biochemists and public health researchers who are interested in data analysis, report/journal article writing, report/journal article reviewing and supervising researchers in the above areas.
While the information contained herein was correct at the date of publication, Monash University reserves the right to alter procedures, fees and regulations should the need arise.