Postgraduate study

Source: https://www.gla.ac.uk/postgraduate/taught/advanced-statistics/?card=course&code=STATS5015 Parent: https://www.gla.ac.uk/postgraduate/taught/advanced-statistics/

Postgraduate taught

Advanced Statistics MSc

Biostatistics (Level M) STATS5015

Academic Session: 2025-26
School: School of Mathematics and Statistics
Credits: 10
Level: Level 5 (SCQF level 11)
Typically Offered: Either Semester 1 or Semester 2
Available to Visiting Students: Yes
Collaborative Online International Learning: No
Curriculum For Life: No

Short Description

To provide an appreciation of 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). To expose students to uses and misuses of Statistics in the Biomedical literature.

Timetable

20 lectures

fortnightly tutorials

2 two-hour practical sessions

Excluded Courses

STATS4006 Biostatistics

STATS3012 Statistics 3B: Biostatistics

Assessment

120 minute, end-of-course examination (100%)

Main Assessment In: August

Course Aims

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).

Intended Learning Outcomes of Course

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, theoretically derive their properties, and be able to apply them to real data.

■ describe measures for quantifying the impact of a covariate factor on disease risk, describe their theoretical basis, 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, explain their theoretical basis, and be able to apply them to real data.

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

Minimum Requirement for Award of Credits

Not applicable