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Eighteen years ago, Professor Xinming Tu (one of the journal's biostatistical editors) and I were coauthors of a paper[1] that involved missing data in chemometrics. One of the reviewer's comments included the following:  The statement, ‘The naive approach of ignoring the missing data and using only the observed portion could provide very misleading conclusions’ is nonsense to me (and I think the authors should also recognize it as nonsense in the real world). Similarly, what does it mean, ‘When analyzing such missing data, ...’; if the data are missing, you can't analyze them.  If you find nothing nonsensical in this reviewer's comments, then the current article is worth a few minutes of your time. Statistical analysis has the same inductive nature as detective work: inferring unknowns from whatever one knows and observes, including the evidence that something is missing. Few qualified detectives would ignore suspicious absences in drawing their overall conclusions. Similarly, understanding the complications and consequences of having missing data is essential to reaching statistically meaningful and scientifically defensible conclusions.

You want me to analyze data I don't have? Are you insane?