1. Introduction to study designs and epidemiological measures
Definition and functions of epidemiology, key indicators (incidence, prevalence, relative rate, relative risk, risk difference) and concepts (bias, confounding, interaction, causality); introduction to the most important clinical study disigns: case-control studies, cross-sectional studies, cohort studies, experimental studies.
2. Principles of biostatistics
This covers descriptive data analysis and the concepts of statistical interference (random variability, confidence intervals, dealing with probability, P values).
3. Introduction to the statistical software Stata and data capture software REDCap
Part 1 includes an introduction to Stata, reading in, modifying, and managing data sets, data control and management, preparing graphics, carrying out descriptive analyses and simple statistical tests. Part 2 is an introduction to the electronic data capture system REDCap, and allows the setup of a simple survey or study with the most important question types. This course includes a personal license for Stata (www.stata.com).
4. Regression models in clinical research and epidemiology
Using Stata, this course covers practical applications of the most commonly used regression models, i.e. linear, logistic, Cox proportional hazards, and Poisson regression.
5. Evaluation of diagnostic and screening tests
Dimensions and indicators of diagnostic accuracy (sensitivity, specificity, diagnostic odds ratio, likelihood ratio, accuracy, ROC curves), statistical and clinical epidemiological definitions of normal values, the architecture of diagnostic research designs.
6. Prognostic studies and modelling
Types of prognostic studies and factors (quality of care, biomarkers, multivariable models), and study types and limitations in the conduct and analysis of studies of prognostic factors. Development of multivariate prognostic models, and internal and external validation; clinical and public health impact of the use of prognostic models in connection with personalized/stratified medicine; critical evaluation of published prognostic studies.
7. Evaluation of medical intervention: randomized clinical studies
Definition and types of randomized studies and their advantages and disadvantages; development of a study protocol (setting, inclusion and exclusion criteria, selection of the control group [placebo, usual care, etc.], randomization, definition of endpoints); sources and prevention of bias (concealment, blinding, intention-to-treat analysis); regulatory aspects and good clinical practice; critical evaluation of published randomized clinical studies.
8. Systematic reviews and meta-analyses
The advantages and disadvantages of narrative and systematic reviews, principles and procedures for conducting a meta-analysis(random effects, fixed effects, meta-regression), limitations of systematic reviews and meta-analyses of randomized and observational studies, publication bias, critical evaluation of published reviews and meta-analyses.
In the elective modules, participants’ own interests may be used to deepen and broaden the content of the basic modules. ISPM will offer elective courses in advanced statistical and epidemiological methods, health technology assessment, writing research proposals, and publishing scientific articles, while courses at other Swiss and foreign universities also may be included for credit.