2017 Volume 46 Issue 2 Pages 87-106
Missing data problems are common in medical and epidemiologic studies. If there are systematic differences between responders and non-responders, we need to select an appropriate method in analyzing the data to handle the missing data problem. When MAR assumption is valid, methods based on observed data likelihood or multiple imputation method are often applied in practice. These methods are categorized as parametric models, which can suffer potential sensitivity to deviations of assumed model from the true model. A natural alternative option for this problem is to take semiparametric approach. However, semiparametric methods for incomplete data are less popular in practice, partly because their complexity. In this paper, we explain the methodological issues on semiparametric inference based on incomplete data, focusing on a simple pretest-posttest study scenario to give greater importance to the practical aspect of its application.
