Speaker: Caleb Leedy, Graduate Students, Department of Statistics, Iowa State University

Title:  Handling Nonmonotone Missing Data with Available Complete-Case Missing Value Assumption (Cheng et al. 2022)

Abstract: Nonmonotone missing data is a common problem in scientific studies. The conventional ignorability and missing-at-random (MAR) conditions are unlikely to hold for nonmonotone missing data and data analysis can be very challenging with few complete data. In this paper, the authors introduce the available complete-case missing value (ACCMV) assumption for handling nonmonotone and missing-not-at-random (MNAR) problems. Their ACCMV assumption is applicable to data set with a small set of complete observations, and they show that the ACCMV assumption leads to nonparametric identification of the distribution for the variables of interest. They show the validity of the method with simulation studies and illustrate the applicability of the method by applying it to a diabetes data set from electronic health records.