Aims - This module aims to provide an introduction to the fields of clinical trials and epidemiology, with emphasis on the statistical ideas and methodology most widely used in these areas. It is primarily intended for third and fourth year undergraduate students and taught postgraduate students registered on the degree programmes offered by the Department of Statistical Science, or jointly with other departments. It also serves as an optional module for students taking the mathematics and statistics stream of the Natural Sciences degree (with prerequisite: STAT0005).

Objectives - On successful completion of the course, a student should have an understanding of ways to measure health outcome, types of observational studies and their design issues and design features of randomised trials. In addition, they should be able to implement and interpret results from basic methods of analysis used in health studies as well as, logistic regression and methods for analysis of survival data.

Applications - This module has applications in both medicine and epidemiology. Important areas include the design and analysis of medical research studies, including randomised controlled trials.

Content - The role of medical statistics; Types of observational studies: case-control, matched case-control, cohort, cross-sectional and their analysis: introducing absolute and relative measures of risk, rates and odds; Design features of randomised trials: randomisation, blocking, stratification, minimisation, blinding, use of placebos; Survival analysis: features of survival data, hazard and survivor functions, censoring, Kaplan Meier Curves, Log rank Tests, Cox regression; Analysis of parallel group trials: basic analysis, intention to treat and per protocol analyses, missing data, use of baseline data, subgroup analyses, interpretation of results; Confounding and interaction: concepts of confounding and interaction, stratification and matched analysis; Logistic regression: odds ratios, predictions, multiple logistic regression, categorical and continuous covariates, assumptions of linearity, interactions, goodness of fit (Hosmer-Lemeshow), conditional logistic regression; Calculation of sample size for trials and observational studies; Introduction to statistical software STATA. There will be computer or paper based practical sessions on analysis of observational studies, study design, analysis of trials, confounding and interaction, survival analysis, logistic regression and sample size calculation.

Key texts - Available from ReadingLists@UCL.