Fig. 6.

Maximum additional bias of the MI estimator of ${\beta }_{\mathit{YX}}$ when continuous outcome Y is missing not at random, with missingness caused by Y and X, and the imputation model includes exposure X and a predictor of missingness but not the missing values, Z, varying the direct effect sizes ${\beta }_{\mathit{YX}}$, ${\beta }_{\mathit{RY}}$, ${\beta }_{\mathit{RX}}$, and ${\beta }_{\mathit{RZ}}$. Maximum additional bias was calculated using Eq. 4.2. The distribution of maximum additional bias in each box-plot is averaged over the values of ${\beta }_{\mathit{RY}}$ and ${\beta }_{\mathit{RX}}$