MICEVerfahren

MICE-Verfahren (Multiple Imputation by Chained Equations) is a statistical method used for handling missing data in datasets. It is part of the broader category of multiple imputation techniques, which aim to produce unbiased estimates by replacing missing values with multiple sets of plausible data points.

The procedure involves iteratively imputing missing data for each variable conditioned on other variables in the dataset. Starting with initial estimates for missing values, the MICE algorithm cycles through each variable with missing data, using regression models or other predictive models to generate new imputations based on the observed data. This process continues through several iterations until the imputations stabilize.

MICE offers several advantages, including its flexibility to handle various types of data (continuous, binary, categorical) and the ability to incorporate complex relationships among variables. It is widely used in fields such as biostatistics, social sciences, and economics to address issues of incomplete data, thereby improving the validity and reliability of statistical analyses.

The generated multiple imputed datasets are analyzed separately, and results are combined using appropriate pooling rules, such as Rubin's rules, to obtain overall estimates that account for the uncertainty introduced by missing values.

Overall, MICE is a robust and versatile method for managing missing data, facilitating more accurate and valid statistical inference in a wide range of research contexts.