Although researchers do their best to avoid missing data, it is a common problem in medical and epidemiological studies. How large your missing data problem is and how to deal with it depends on how much data is missing and why your data are missing. This two-day course provides you with tools how to evaluate and handle missing data in medical and epidemiological studies with different missing data rates.

There are various methods 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.

Before you are going to use a method to handle missing data you must have to gain insight into the effect of missing data on your study results. Consequences of various rates of missing data for your study results will be explored and discussed during the course. In general there are three missing data mechanisms, missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Knowledge about these mechanisms is important and provides information about how well you are able to estimate and replace the missing values and how well you are able to solve the missing data problem in your study. 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 in the afternoon. During the computer exercises various ways to explore missing data problems as well as simple and more advanced missing data methods as Multiple Imputation will be trained using SPSS software. During the computer exercises you will work with real epidemiological and medical datasets.

Learning objectives

1. The participant has knowledge about the most frequently used methods to handle missing data in epidemiological and medical datasets. 
2. 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). 
3. The participant has knowledge about various data evaluation procedures to distinguish between different missing data mechanisms. 
4. The participant is able to mention the strength and limitations of the most frequently used methods to handle missing data in various missing data situations. 
5. The participant is able to use Multiple Imputation by the Multivariate Imputation by Chained Equations (MICE) procedure in SPSS and knows under which missing data situations this method can be used. 
6. 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.