A0831
Title: Optimized recovery sampling to test for missing not at random
Authors: Robin Mitra - UCL (United Kingdom) [presenting]
Abstract: Missing data can lead to inefficiencies and biases in analyses, in particular when data are missing, not at random (MNAR). It is thus vital to understand and correctly identify the missing data mechanism. Recovering missing values through a follow-up sample allows researchers to conduct hypothesis tests for MNAR, which is not possible when using only the original incomplete data. Investigating how the properties of these tests are affected by the follow-up sample design is not explored in the literature. The results provide comprehensive insight into the properties of one such test based on the commonly used selection model framework. Conditions are determined for recovery samples that allow the test to be applied appropriately and effectively, i.e. with known type I error rates and optimized with respect to power. An integrated framework is thus provided for testing the presence of MNAR and designing follow-up samples in an efficient, cost-effective way. The methodology's performance is evaluated through simulation studies and on a real data sample.
