We should link the cases of missing values met in Conservation of Cultural Properties with the missing data mechanisms namely: a) Missing Completely at Random (MCAR) b) Missing at Random (MAR) and c) Non-Ignorable (NI) or Missing Not at Random (MNAR).
In addition, we performed an analysis with an alternative assumption about the missing data process, namely, that there is no misclassification of stages for patients without surgery.
For point b), we examined results from the available studies for clues that suggest there may be missing studies (namely publication bias) or missing data from the included studies (namely selective reporting bias).
Multilevel analysis holds two important features that are relevant to our data, namely the handling of missing data, as well as the possibility to work with dependent data.
Several situations were considered according to the type of missing data one might encounter in practice: namely MCAR, MAR or MNAR type of missing data.
We had some missing data on three of the variables included in the analyses, namely stage at diagnosis, histopathological grade, and ER status.
We were also missing data on two risk factors that ATP III recommends to assign LDL-cholesterol risk category, namely cigarette smoking and family history of premature coronary heart disease.
1 Essentially, we agree with his sentiments, however we need to clarify the central points of his discussion, namely: 1) the objective of the study; and 2) the handling of missing data in our statistical analyses.
Strategies for dealing with missing data in longitudinal studies vary, so we adopted two recommended techniques for analysing incomplete datasets, namely, mixed models repeated measures (MMRM) and multiple imputation (MI; for a review see [ 24]).
Blanks indicate missing data.
