The chained equation process can be broken down into six general steps:
Step 1: A simple imputation, such as imputing the mean, is performed for every missing value in the dataset. These mean imputations can be thought of as “place holders.”
Step 2: The “place holder” mean imputations for one variable (“var”) are set back to missing.
Step 3: The observed values from the variable “var” in Step 2 are regressed on the other variables in the imputation model, which may or may not consist of all of the variables in the dataset. In other words, “var” is the dependent variable in a regression model and all the other variables are independent variables in the regression model. These regression models operate under the same assumptions that one would make when performing (e.g.,) linear, logistic, or Poison regression models outside of the context of imputing missing data.
Step 4: The missing values for “var” are then replaced with predictions (imputations) from the regression model. When “var” is subsequently used as an independent variable in the regression models for other variables, both the observed and these imputed values will be used.
Step 5: Steps 2–4 are then repeated for each variable that has missing data. The cycling through each of the variables constitutes one iteration or “cycle.” At the end of one cycle all of the missing values have been replaced with predictions from regressions that reflect the relationships observed in the data.
Step 6: Steps 2 through 4 are repeated for a number of cycles, with the imputations being updated at each cycle. The number of cycles to be performed can be specified by the researcher. At the end of these cycles the final imputations are retained, resulting in one imputed dataset. Generally, ten cycles are performed (Raghunathan et al., 2002); however, research is needed to identify the optimal number of cycles when imputing data under different conditions. The idea is that by the end of the cycles the distribution of the parameters governing the imputations (e.g., the coefficients in the regression models) should have converged in the sense of becoming stable. This will, for example, avoid dependence on the order in which the variables are imputed. In practice, researchers can check the convergence by, for example, comparing the regression models at subsequent cycles, as discussed in He et al. (2009). Different MICE software packages vary somewhat in their exact implementation of this algorithm (e.g., in the order in which the variables are imputed), but the general strategy is the same.
I am interesting to know why mice use mean imputation (step 1) as first imputation , If I change mean imputation from step(1) to another imputations like (classification, neural-network , ... or another method for imputation) is that affect the accuracy of MICE imputation to higher accuracy.