Generating missing values for simulation purposes: a multivariate amputation procedure

Missing data form a ubiquitous problem in scientific research, especially since most statistical analyses require complete data. To evaluate the performance of methods dealing with missing data, researchers perform simulation studies. An important aspect of these studies is the generation of missing values in a simulated, complete data set: the amputation procedure. We investigated the methodological validity and statistical nature of both the current amputation practice and a newly developed and implemented multivariate amputation procedure. We studied the performance of these methods and found that the current way of practice may not be appropriate for the generation of intuitive and reliable missing data problems. That is to say, important missing data characteristics such as the missingness percentage and the impact on statistical estimates influence each other. On the other hand, we demonstrate that the multivariate amputation procedure generates reliable amputations and allows for a proper regulation of missing data problems. The procedure has additional features to generate any missing data scenario precisely as intended. Hence, the multivariate amputation procedure is an efficient method to accurately evaluate missing data methodology.