This course covers statistical methods and concepts relevant to problems in medicine, especially in clinical trials and epidemiological studies, and discusses the principal ethical issues that arise. It also covers survival analysis as a means of modelling measurements made over an interval of time.

20 lectures

fortnightly tutorials

2 two-hour practical sessions

STATS3012 Statistics 3B: Biostatistics

STATS5015 Biostatistics (Level M)

Courses prescribed in the Honours- or Master's-level programme to which the student has been admitted.

90 minute, end of course examination (100%)

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

To train students in the application of statistical methods and concepts to problems in medicine, especially in clinical trials and epidemiological studies, and to discuss the principal ethical issues that arise

To introduce survival analysis as a means of modelling measurements made over an interval of time, such as the survival time of a patient (time from treatment to death).

By the end of this course students will be able to:

■ describe a range of biostatistical study designs, describe their key features, and determine the appropriateness of each one for real epidemiological investigations.

■ describe a range of summary statistics and simple statistical models used to quantify biostatistical data, and be able to apply these to real data.

■ describe measures for quantifying the impact of a covariate factor on disease risk, and compute and interpret them in real epidemiological studies.

■ describe the key features of survival data, the Kaplan Meier estimator and the proportional hazards model, and be able to apply them to real data.

■ calculate appropriate sample sizes for clinical trials and interpret them in the context of real clinical trials.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.