Groenwold et al. (1) offer advice on ways to handle missing outcome data in randomized experiments and observational studies. We agree that, regardless of the amount of effort invested in collecting complete data, it is difficult to avoid some missing values. However, we are concerned about the conclusion, stated in the abstract and the discussion, that "complete case analysis with covariate adjustment can and should be used as the analysis of choice more often" (1, pp. 210 and 217). In support of this conclusion, Groenwold et al. presented a simulation study that considered a very basic scenario: binary treatment with a constant treatment effect, 1 fully observed covariate, and 1 partially missing outcome, either binary or continuous, in which the parameters of the missingness mechanism were distinct from those that governed the distribution of the outcomes (2). In the simulations, complete case analysis with covariate adjustment (CCA-CA) yielded results similar to those from the multiple imputation method implemented using multivariate imputation by chained equations (3).
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