## Objectives/ aim

You are able to design a study based on a clinical scientific problem, and understand the connection between epidemiological methodology, database design and management and concepts of statistical data analysis of clinical research questions.

You are able to discuss and evaluate the concepts and methods based on specific and precisely defined questions in clinical scientific research.

You recognize the limitations of using crude epidemiological concepts such as relative risk and odds ratio, and know how to adjust these using statistical methods such as logistic regression and survival analysis.

You are able to apply these concepts, and formulate the results as well as the assumptions upon which results and evaluations are based.

You are able to apply these principles to your own study protocol.

## Content

In previous courses on epidemiology,informatics, or (bio)statistics, you may have studied these subjects more or less as – stand alone – disciplines.

In practice however, research is only possible through strong interdisciplinary collaboration. This means that researchers must be able to combine and exploit knowledge across disciplines when formulating research questions based on a clinical problem, discussing and evaluating appropriate study designs, proposing the data collection and storage procedures and discussing the epidemiological and statistical analysis procedures which are appropriate. For these reasons, we will discuss in this course the practical problem of scientific research design and methods. We will start the course with the discussion of a real-life example in clinical scientific research. From this discussion,we will evaluate through a joint discussion which problems may be encountered when carrying out research and what the appropriate options are for study design and analysis. The focus of the course is thus on the interdisciplinary aspects of applied research and how epidemiology, informatics and (bio)statistics work together to help us solve practical research questions.

## Literature

The following is a list of English text books which are suitable for study. The course book contains an extended list of Dutch course texts as well, which may be used by students instead of the texts below.

Epidemiology. An introduction. K.J. Rothman. 2002, New York, Oxford University Press, ISBN 0-19-513554-7 (Alternative English book)

Epidemiology, beyond the basics. M. Szklo, F.J. Nieto. 2004, Sudbury Massachusetts, Jones and Bartlett Publishers, ISBN 0-7637-4722-X (for optional further reading)

Sams Teach Yourself SQL in 10 Minutes. Ben Forta, Sams Publishing, 2004. ISBN 0-672-32567-5

Medical Statistics at a Glance. Aviva Petrie and Caroline Sabin, Blackwell Science, 200

## Form of tuition

Lectures, workgroups, (computer) practical sessions, self study exercises.

## Mode of assessment

Final assessment will be through a written examination, taking 3 hours.

## Entry requirements/recommended prior knowledge

Epidemiology

Research design (patient series, cross-sectional design, randomized controlled clinical trial, follow-up study, case-control research).

Frequency summary measures of disease (prevalence, incidence).

Disease risk (absolute risk, relative risk).

Information science:

Knowledge on basic principles of information sciences, specifically with respect to databases (database models, structuring databases, quality control, generating basic queries and reports, …).

Some experience of the database package Access or any comparable software database package.

Statistics:

Knowledge of basic introductory statistics concepts (hypothesis testing, p-values, confidence intervals, standard statistical tests, such as the chi-squared test, paired and unpaired testing, non-parametric testing, some familiarity with formulating the conclusions of statistical hypothesis tests.

Some experience with the statistical analysis package SPSS or any comparable similar package for statistical data analysis (SAS, STATA,…).

Preferably knowledge of analysis of variance (ANOVA) and regression analysis, but not strictly required.