Figure 2:

Schematic diagram of the pipeline used to obtain pooled estimates when applying a MI strategy. The incomplete dataset is imputed $\mathit{m}$ times, where the value of $\mathit{m}$ can be defined in order to maximize the efficiency of the MI estimator (see Appendix A); each imputed dataset is individually processed to compute separate inferences; all the inferences are pooled by Rubin’s rule [4] to get the pooled estimates ($\widehat{\mathit{Q}}$), their total variances ($\widehat{\mathit{V}\mathit{A}\mathit{R}}$) and standard errors ($\widehat{\mathit{S}\mathit{E}}$) and their confidence intervals ($\widehat{\mathit{C}\mathit{I}}$). In the figure, we use the superscript $\mathit{j}$ to index the imputations number ($\mathit{j}\in \left\{1,\dots ,\mathit{m}\right\}$) and the subscript $\mathit{i}$ to index the predictor variable in the dataset (see Table 2 for a detailed list of all the notations used throughout the paper).