There are various methods that can be used to deal with missing data. Simple solutions are that you ignore the missing values and delete all cases with missing values from the analysis or to use a regression model to estimate the missing values. There are also more advanced methods as Multiple Imputation. Multiple Imputation with the Multivariate Imputation with Chained Equations (MICE) procedure is a promising technique that works well in various missing data situations. With Multiple Imputation several complete datasets are generated. Data analysis has to be done in each dataset and results are pooled using special calculation rules (called Rubin’s rules). These steps will be discussed during the course as well as questions of how to use different missing data methods in medical and epidemiological datasets. Furthermore it is important to check if your imputation strategy was successful (imputation diagnostics) which will also be discussed during the course.

Each course day starts with lectures in the morning followed by computer exercises. During the computer exercises various ways to explore missing data problems as well as the application of simple and more advanced missing data methods as Multiple Imputation will be trained using SPSS and R(Studio) software. During the computer exercises you will work with real epidemiological and medical datasets.

Missing data consequences
Lectures:

• Examples of Missing data in different Epidemiological and Medical research designs.
• The meaning of missing data mechanisms (MCAR, MAR, MNAR).
• Consequences and impact of missing data for statistical analyses.
• Ways to evaluate various missing data situations and mechanisms.

Missing data solutions
Lectures:

• The application of simple missing data methods.
• The theory and practice of Multiple Imputation.
• Data analysis after Multiple Imputation.
• How to evaluate imputation success by using imputation diagnostics.

Practicals

SPSS and R(Studio) software.

1. The participant is able to distinguish between different missing data mechanisms called missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR).
2. The participant can apply basic evaluation procedures to make a valid assumption about the missing data mechanism.
3. The participant understands the working of the most frequently used methods to handle missing data in epidemiological and medical datasets.
4. The participant recognizes the strengths and limitations of the most frequently used methods to handle missing data in various missing data situations.
5. The participant is able to work with SPSS to investigate missing data and to work with the best missing data methods for various missing data situations.
6. The participant is able to use Multiple Imputation by the Multivariate Imputation by Chained Equations (MICE) procedure in SPSS amd R(Studio).
7. The participant understands how multiple imputation works and how a multiple imputation model should be specified.
8. The participant understands how to handle missing questionnaire data and can comprehend the difference between handling item scores at item level and at total score level.
9. The participant understands the practical solutions to handle missing data in Multilevel (and Longitudinal) studies.
10. The participant is able to work with SPSS and R(Studio) to handle missing data in questionnaires and in Multilevel (and longitudinal) studies.

Target audience

The course is designed for PhD-students, practitioners and applied researchers working in the field of epidemiology, medicine, public health, psychology, human movement sciences. The course is designed for everybody who wants to learn about missing data, because missing data may be present in your own research and you are going to start with your data analysis, or you want to learn how to judge other articles or research grants that report missing data. It is also important to be able to judge the impact of missing data for practice-related research.

Course prerequisites

The following concepts are assumed known by participants at the start of this course:

– Knowledge of basic statistical tests as t-tests and regression analyses.

– Knowledge of some basic SPSS commands. (Knowledge of R(Studio) is not a prerequisite.

Course material

The course materials (lectures, assignments, feedback of the assignments etc) are available on Canvas, our digital learning environment. The documents will remain available on Canvas for at least one year.

In order to do the computer practicals, you will need:

1. R and R studio. R and R studio can be downloaded for free at https://cran.r-project.org/

and

2. SPSS. If you do not have SPSS installed yet, you can purchase it on Surfspot. Another option is to use IBM’s trial version: SPSS Software | IBM

Literature

Missing data in R and SPSS: https://bookdown.org/mwheymans/BookMI/

Introduction in R: https://bookdown.org/introrbook/intro2r/ 

Epidemiology Master’s students are required to do the exam in order to complete this course. For other students (not enrolled in Epidemiology Master), the exam is optional and costs €160,- per registration.

Students who complete the exam with a pass grade will receive an exam statement mentioning the academic credits (ECs).

You can register for the exam on the Exams page. Registration will close 3 weeks prior to the exam.

Please note that you need to pass the exam in order to receive credits (EC).

A certificate of participation will be granted to all students who have attended at least 80% of the classes. The number of contact hours will be stated on the certificate.

Only for Dutch medical specialists!

On the final day of the course, you need to sign the attendance list if you wish to obtain KNMG accreditation credits (PE-points).

To qualify for these credits, there is an attendance requirement of 100%.