In addition to plotting, there is another subtlety I am interested in. That concerns the generation of spline effects themselves. Based on your code, restricted cubic splines are created after the imputation is done. I wonder if you included spline effects when you are imputing. If not, I am also concerned upon how this may have an impact upon your analytical results, as the majority of research paperes (e.g., Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified - ScienceDirect) have shown that omission of nonlinear and interaction terms in the imputation stage and simply creat them on-the-spot in the analytical stage will cause biased-toward-zero results (i.e., the regression coefficients are biased toward zero). But, there are also research papers showing that creating them in an ad hoc manner may be a better choice in certain circumstances (e.g., Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects - ScienceDirect). I personally have not yet retrieved any research paper specifically showing how spline terms, as a special and rather sophisticated kind of nonlinear term, should be dealt with in missing data. After all, creating them on-the-spot is rather easy and certain researchers (me included) are prone to adopt it had it been proved to be a viable approach.
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