Missing data: consequences and solutions

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.
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.