Although researchers do their best to avoid missing data, it is a common problem in medical and epidemiological studies. How large the impact is of missing data on the study results and how to solve the missing data problem depends on how much data is missing and why the data are missing. This three-day course provides you with simple and advanced tools how to evaluate and handle missing data in medical and epidemiological studies.
Missing data is a 3-days course that consists of an intensive programme of partly interactive lectures, combined with computer-based practical work. Examples taken from clinical practice will be used for the computer-based work.
If a course is [Full], you can still register, and we will place you on a waiting list. We will contact you as soon as a place becomes available. You can then decide whether you still want to participate.
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 consequencesLectures:
Missing data solutionsLectures:
SPSS and R(Studio) software.
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.
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.
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/
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
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%.
Epidemiology and Data Science, Amsterdam UMC
The Netherlands Organisation for applied scientific research (TNO)