Eleven Years Experience of Teaching Medical Statistics to Mathematics Undergraduates

1. Eleven Years Experience of Teaching Medical Statistics to Mathematics Undergraduates Chris Roberts Biostatistics Group School of Health Sciences University of Manchester

2. Motivation (Biostats Group) Recruit PhD students. Promote medical statistics as a career option for maths students. Develop links between the Biostatistics Group and the Statistics Group in the School of Mathematics. Motivation (School of Maths) Make the BSc in Maths/Maths and Statistics more attractive for applicants with an interest in statistics. Provide an additional 3rd year options. Why Teach Medical Statistics to Mathematics Students

3. Statistics Course Units in the Mathematics BSc

4. Module Curriculum (2001) • Randomised controlled trials: Introduction to RCT, Bias concealment Randomisation;Statistical and ethical issues concerning randomised experimentation on patients; Sample size and Power; Treatment allocation methods; Equivalence and Non-inferiority Trials; Intention-to-treat; Subgroups analyses; Cross-over Trials; Meta-analysis. (18 hrs) Roberts • Epidemiological studies: Causal inference concerning risks from observational studies; Confounding; Cohort and case-control designs; Methods of analysis; Incident rate ratios; Relative risks and odds-ratios; Stratification; Application of generalised linear models. (9 hrs McNamee) • Measurement Error: Problems arising from measurement errors in diagnostic testing, screening, and epidemiological studies; Statistical methods related to measurement error. (6 hrs Dunn) • Complex survey sampling: Sampling frames and sampling fractions; Methods of random sampling (simple, systematic, stratified and clustered); Implications of design based methods of statistical analysis. (3 hrs Pickles)

5. Module Curriculum (2002) Randomised controlled trials: Introduction to RCT, Bias concealment Randomisation;Statistical and ethical issues concerning randomised experimentation on patients; Sample size and Power; Treatment allocation methods; Adjustment for Baseline; Equivalence and Non-inferiority Trials; Intention-to-treat and CACE Estimation; Subgroups analyses; Cross-over Trials; Meta-analysis. (24hrs ) Roberts Epidemiological studies: Causal inference concerning risks from observational studies; Confounding; Cohort and case-control designs; Methods of analysis; Incident rate ratios; Relative risks and odds-ratios; Stratification; Application of generalised linear models. (12hrs) McNamee

6. Biostatistics MSc / MSc in Statistics Biostatistics Pathway Three or 8 modules on the Biostatistics MSc / MSc in Statistics (Biostatistics Pathway) taught by Biostatistics Group • Introduction to Clinical Trials • Epidemiology • Advanced Topics in biostatistics Medical Statistics lecture are now part of the Introduction to Clinical Trials

7. Current Syllabus (2011) • Introduction to RCT, Bias concealment Randomisation; Statistical and ethical issues concerning randomised experimentation on patients; • Basic methods of analysis including revision of statistical inference. • Sample size and Power. • Treatment allocation methods. • Adjustment for Baseline. • Equivalence and Non-inferiority Trials. • Intention-to-treat and CACE Estimation. • Subgroups analyses. • Cross-over Trials. • Meta-analysis. (22 hours lectures – 11 hours example class)

8. Number of Students

9. Features of the Medical Statistics Modules • Interpretation. • Discussion of practical aspect of trials. • No survival analysis. • Discussion of inappropriate methods. • Intention-to-treat introduced by considering CACE estimation.

10. Inappropriate Methods Covered Analysis with baseline data • Within treatment group tests of change. • Test to check randomisation. Equivalence and non-inferiority • Use of test of the null hypothesis of no difference. Sub-group analyses • Separate test of sub-groups. • Multiple testing. Crossover trials • Use of paired t-test. • Tests of carry-over effect. Treatment protocol violations • Per-protocol and As Treated Estimates

11. ITT and CACE Estimation = CACE effect / Average Treatment Effect of the Treated

12. The Students Mathematic students very different to medical students and medical researcher • Non-communicative. • Unwilling to ask or answer question. Statistic teaching focused on the mathematics of statistics. • Limited understanding of statistical inference. • Lack confidence regarding interpretation of result. • Limited exposure to design issues. Students anxious about essay style writing. Low level of computing skills. • Lack experience of using statistical software. • Course work almost always hand-written.

13. Teaching Format Most courses on the Maths BSc follow a standard format • Each course consisted of 2 lectures per week + Practical class. • Chalk or Handouts. • Assessment • 80% end of course exam • 20% course work or in-course test To make the course attractive to the typical Maths student we chose to follow this format.

14. Compromises Course expected to have a minimum of 15 students it was therefore important to make the course attractive to the typical mathematics student. • Include some algebraic deviation in course. • Only limited amount of critical appraisal as some students uncomfortable with prose style writing. • Examination and course work includes some algebraic work and well as hand-calculator calculations. • No critical appraisal in the examination – limited amount in course work. • No computing practical classes. • Use edited statistical output in handouts, exercises and examination.

15. Course Work Assessment Article from BMJ • Low medical technical content. • Simple statistical methods. Typical Tasks • Manual check calculations in paper (e.g. t-test / test of proportions/ sample size) • Comment of results - critical appraisal. • Derivation of methods • Maximum sample size for the difference of two proportions of a given magnitude. • Power/sample size for unequal allocation. • Fisher’s exact test. • Confidence interval for the rate ratio using the delta method. • Missing data issues.

16. Course work papers • Quinn. Suturing versus conservative management of lacerations of the hand: randomised controlled trial BMJ. 2002 August 10; 325(7359): 299. • Quist-Paulsen. Randomised controlled trial of smoking cessation intervention after admission for coronary heart disease BMJ. 2003 doi: 10.1136. • Heal et al. Does single application of topical chloramphenicol to high risk sutured wounds reduce incidence of wound infection after minor surgery? Prospective randomised placebo controlled double blind trial BMJ. 2009; doi: 10.1136 • Hickson et al. Use of probiotic Lactobacillus preparation to prevent diarrhoea associated with antibiotics: randomised double blind placebo controlled trial BMJ. 2007; doi: 10.1136 • Melchart et al. Acupuncture in patients with tension-type headache: randomised controlled trial BMJ. 2005; doi: 10.1136

17. Reading List Course Text Matthews JNS (2000) An Introduction to Randomised Controlled Trials. Arnold London ISBN 0-340-76143-1. Background Reading Campbell MJ & Machin D (1999) Medical Statistics: A commonsense approach. John Wiley London

18. Motivation (Biostats Group) Recruit PhD students. Promote medical statistics as a career option for maths students. Develop links between the Biostatistics Group and the Statistics Group in the School of Mathematics. Motivation (School of Maths) Make the BSc in Maths/Maths and Statistics more attractive for applicants with an interest in statistics. Provide an additional 3rd year options. Why Teach Medical Statistics to Mathematics Students