Epidemiology and Medical Datasciencea

Course goals

The following learning objectives will be discussed. Upon completing the course, students can::

a. use and explain the basic concepts used in epidemiology.
b. understand when to apply a mixed model in practice.
c. perform mixed model analyses using statistical software (R).
d. understand mechanisms giving rise to missing data.
e. describe key assumptions and apply imputation methods to deal with missing data.
f. understand the advantages, limitations and key characteristics of IPD-MA in intervention, diagnostic and prognostic research.
g. understand the relevance of between-study heterogeneity and be familiar with statistical methods for investigating and reporting this.
h. be familiar with statistical methods for summarizing relative treatment effects, and for developing and validating clinical prediction models using IPD from multiple sources

Assessment
There will be 2 exams and 2 group assignments during this course counting towards the final grade:
• exam 1: Introduction to Epidemiology and Mixed models
• group assignment 1: Case study on Mixed models
• exam 2: Missing data and IPD Meta analysis
• group assignment 2: Case study on IPD Meta analysis
To average weighted grade will be your final score.

To qualify for a repair of the final result the mark needs to be at least a 4. 

Prerequisites
INFOMDWR Data Wrangling.

Enrolment in this course is only available by using the pre-enrolment form that will be forwarded to all ADSM students in the second week of September.
 

Content

This course provides insight into the basic principles used in epidemiology, such as bias and confounding and students will learn statistical methods to address missing data and correlated data. Correlated data may occur because response variables are observed more than once per individual, or when there is a hierarchical (multilevel) structure in the data, e.g. patients within hospitals, pupils within classrooms, etcetera. Mixed models are one way of analyzing this kind of data. Possible mechanisms for data being missing, their potential impact in terms of bias, and methods to handle missing data will be discussed next. Systematic reviews and meta-analyses are methods to summarize published aggregate data, but it is increasingly common that individual participant data (IPD) are obtained from multiple primary studies, leading to “big data”. Meta-analysis involving IPD, a special application of mixed models, will therefore also be discussed. Emphasis will be on meta-analysis for interventions, though it is also possible to use meta-analysis to investigate the accuracy of diagnostic tests, to develop clinical prediction models, and to externally validate such models. Students will apply all these methods in R during computer labs and assignments and show what they have learned in two case studies.

Course form
Lectures, tutorials, practicals.

Additional information

Enrolment in this course is only available by using the pre-enrolment form that will be forwarded to all ADSM students in the second week of September.