Multiple imputation under the generalized lambda distribution

Although the normality assumption has been regarded as a mathematical convenience for inferential purposes due to its nice distributional properties, there has been a growing interest regarding generalized classes of distributions that span a much broader spectrum in terms of symmetry and peakedness behavior. In this respect, the generalized lambda distribution (GLD) represents a viable choice. In this article, we conduct multiple imputation for univariate continuous data under the GLD to explore the extent to which this procedure works properly; and we make comparisons with normal imputation models via widely accepted accuracy and precision measures using simulated data that exhibit different distributional features as characterized by competing specifications of the third and fourth moments. Furthermore, we present an application using a clinical trials data from psychiatric research. Multiple imputation under the GLD that cover most of the feasible area in the skewness-elongation plane appears to have substantial potential of capturing real missing-data trends that can be encountered in biopharmaceutical practice.