Date of Completion
Missing Data; Multiple Imputation; Power Calculation; Rates of missing information; Sample Size
Field of Study
Doctor of Philosophy
While Multiple Imputation (MI) has become one of the most broadly used methods for handling incomplete data, many questions remain unanswered regarding statistical inference when MI is used with incomplete data. One such question is how to calculate statistical power. Although it is widely acknowledged that MI improves estimation efficiency, reduces estimation bias, and partially restores power loss, there is a gap in the literature as far as quantifying the power gained from using MI over complete case analysis (CCA). Furthermore, the rates of missing information are well developed for traditional MI, but not for newly-adjusted MI. This thesis presents methodologies and simulation studies to calculate statistical power when MI is used, to compare the performance of MI with that of CCA, and to examine under which conditions MI can better restore statistical power. We also provide formulas to compute the rates of missing information for an adjusted two-stage MI, and apply them to evaluate the impact of an extra information source.
Zha, Ruochen, "Advances in the Analysis of Incomplete Data Using Multiple Imputations" (2018). Doctoral Dissertations. 1944.