MAS61002 Medical Statistics
|Both semesters, 2022/23||15 Credits|
|Lecturer:||Dr Kevin Walters||Reading List|
|Aims||Outcomes||Teaching Methods||Assessment||Full Syllabus|
This module comprises sections on Clinical Trials and Survival Data Analysis. The special ethical and regulatory constraints involved in experimentation on human subjects mean that Clinical Trials have developed their own distinct methodology. Students will, however, recognise many fundamentals from mainstream statistical theory. The course aims to discuss the ethical issues involved and to introduce the specialist methods required. Prediction of survival times or comparisons of survival patterns between different treatments are examples of paramount importance in medical statistics. The aim of this course is to provide a flavour of the statistical methodology developed specifically for such problems, especially with regard to the handling of censored data (e.g., patients still alive at the close of the study). Demonstrating implementation of the statistical analyses in the R package is an important part of the course.
Prerequisites: MAS223 (Statistical Inference and Modelling)
Not with: MAS361 (Medical Statistics)
No other modules have this module as a prerequisite.
Outline syllabusClinical Trials:
- Basic concepts and designs: controlled and uncontrolled clinical trials; historical controls; protocol; placebo; randomisation; blind and double blind trials; ethical issues; protocol deviations.
- Size of trials.
- Multiplicity and meta-analysis: interim analyses; multi-centre trials; combining trials.
- Cross-over trials.
- Binary response data: logistic regression modelling; McNemar's test, relative risks, odds ratios.
- Basic concepts: survivor function; hazard function; censoring.
- Single sample methods: lifetables; Kaplan-Meier survival curve; parametric models.
- Two sample methods: log-rank test; parametric comparisons.
- Regression models: inclusion of covariates; Cox's proportional hazards model; parametric and accelerated failure time regression models.
- illustrate applications of statistics within the medical field;
- introduce students to some of the distinctive statistical methodologies developed to tackle problems specifically related to clinical trials and the analysis of survival data;
- demonstrate how appropriate statistical methods for analysing clinical trial data can be implemented using the software package R;
- enhance students’ broader understanding of statistical methodology and develop their professional skills as applied statisticians.
- explain how a clinical trial is designed and conducted, so that the effectiveness of a new medical treatment can be assessed fairly and ethically;
- use appropriate statistical methods to analyse data from clinical trials, including ‘survival’ data;
- assist with the design of a clinical trial, in particular regarding the choice of sample size, and the use of strategies to reduce variation and bias;
- appraise the application of statistical methodology in a substantial medical case study, and communicate the key issues to a non-expert.
There will be formal lectures (in Semester 1 only), which will involve the explanation of theoretical concepts and their application to worked examples. The motivation, rationale, advantages and disadvantages of the various methods taught will be discussed as appropriate, with examples given of communicating issues to a lay audience. Detailed lecture notes will be provided, which students will be expected to study in their own time to assimilate the material. Lectures will include practical demonstrations of analysis using R . Students will work through set exercises in both theory and R implementation, and submit homework for marking, although this will not be part of the formal assessment. Students will undertake a project in Semester 2, which will involve investigating the application of methods and concepts covered in the module in a substantial case study, and will be required to communicate their findings in a written report, at a level so that the key findings/issues can be understood by a non-expert reader.
20 lectures, no tutorials
One formal 2 hour written examination (70%). All questions compulsory. One project (30%).
- Basic concepts and designs:
controlled and uncontrolled clinical trials; historical controls; protocol; placebo; randomisation; blind and double blind trials; ethical issues; protocol deviations.
- Size of trials
- Multiplicity and meta-analysis:
interim analyses; multi-centre trials; combining trials.
- Cross-over trials:
- Binary response data:
logistic regression modelling; McNemar's test, relative risks, odds ratios.
- Basic concepts:
survivor function; hazard function; censoring.
- Single sample methods:
lifetables; Kaplan-Meier survival curve; parametric models.
- Two sample methods:
log-rank test; parametric comparisons.
- Regression models:
inclusion of covariates; Cox's proportional hazards model; parametric and accelerated failure time regression models.
|A||Everitt and Rabe-Heskith||Analyzing Medical Data Using S-Plus||610.285 (E)||Blackwells||Amazon|
|A||Matthews||An Introduction to Randomized Controlled Clinical Trials||615.50724 (M)||Blackwells||Amazon|
|B||Altman||Practical Statistics for Medical Research||519.023 (A)||Blackwells||Amazon|
|B||Campbell||Statistics at Square Two||519.023 (C)||Blackwells||Amazon|
|B||Collett||Modelling Survival Data in Medical Research||610.727 (C)||Blackwells||Amazon|
(A = essential, B = recommended, C = background.)
Most books on reading lists should also be available from the Blackwells shop at Jessop West.