Multiple imputation by chained equations (MICE) is a method for handling missing data in statistical analysis. This technique involves creating multiple imputed datasets to account for the uncertainty in missing values, leading to more accurate standard errors and estimates. MICE is flexible and can handle various data types, including continuous and binary variables, as well as complex data structures like bounds and survey skip patterns. It assumes that data are missing at random (MAR), meaning the probability of missingness depends only on observed values. The MICE process involves several steps: first, a simple imputation (like mean imputation) is performed, then missing values are set back to missing, and regression models are used to predict and impute missing values based on observed data. This process is repeated for each variable with missing data, cycling through variables until convergence. The number of imputed datasets is typically between 5 and 10, though this can vary based on data characteristics and computational resources. Setting up a MICE procedure requires selecting appropriate variables to include in the imputation model, considering both the variables used in analysis and those that may predict missingness. Auxiliary variables can improve imputation accuracy. Researchers must also decide whether to impute individual items or summary scores and whether to use raw or standardized scores. Software packages like IVEware, WinMICE, Stata, R, and SPSS offer MICE capabilities, with each having specific features and implementations. After imputation, data are analyzed using standard methods, and results are combined to account for imputation uncertainty. Analyzing multiply imputed data involves running analyses on each dataset and combining results to obtain final estimates. Despite its advantages, MICE has limitations, such as the need for careful model specification and the potential for complex data structures to require specialized approaches. Ongoing research aims to improve imputation diagnostics and expand the applicability of MICE to various data types and analyses. Overall, MICE provides a principled and flexible method for addressing missing data, making it a valuable tool for researchers in various fields.Multiple imputation by chained equations (MICE) is a method for handling missing data in statistical analysis. This technique involves creating multiple imputed datasets to account for the uncertainty in missing values, leading to more accurate standard errors and estimates. MICE is flexible and can handle various data types, including continuous and binary variables, as well as complex data structures like bounds and survey skip patterns. It assumes that data are missing at random (MAR), meaning the probability of missingness depends only on observed values. The MICE process involves several steps: first, a simple imputation (like mean imputation) is performed, then missing values are set back to missing, and regression models are used to predict and impute missing values based on observed data. This process is repeated for each variable with missing data, cycling through variables until convergence. The number of imputed datasets is typically between 5 and 10, though this can vary based on data characteristics and computational resources. Setting up a MICE procedure requires selecting appropriate variables to include in the imputation model, considering both the variables used in analysis and those that may predict missingness. Auxiliary variables can improve imputation accuracy. Researchers must also decide whether to impute individual items or summary scores and whether to use raw or standardized scores. Software packages like IVEware, WinMICE, Stata, R, and SPSS offer MICE capabilities, with each having specific features and implementations. After imputation, data are analyzed using standard methods, and results are combined to account for imputation uncertainty. Analyzing multiply imputed data involves running analyses on each dataset and combining results to obtain final estimates. Despite its advantages, MICE has limitations, such as the need for careful model specification and the potential for complex data structures to require specialized approaches. Ongoing research aims to improve imputation diagnostics and expand the applicability of MICE to various data types and analyses. Overall, MICE provides a principled and flexible method for addressing missing data, making it a valuable tool for researchers in various fields.