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Title: Improving the power of a test for detecting ``missing not at random'' Authors:  Jack Noonan - Cardiff University (United Kingdom) [presenting]
Robin Mitra - University of Southampton (United Kingdom)
Stefanie Biedermann - The Open University (United Kingdom)
Abstract: Missing data is known to be an inherent and pervasive problem in the process of data collection. The effects are wide-ranging and the loss of data can lead to inefficiencies and introduce bias into analyses. The specific problem of data missing not at random (MNAR) is known to be one of the most complex and challenging problems to handle in this area and testing its prevalence is of great importance. The presence of MNAR missingness can only be tested using a follow-up sample of the missing observations and therefore recovering a proportion of missing values in an efficient way could be crucial in saving the experimenter's costs and time and may result in new treatments/technology reaching the public faster. We develop a strategy to allow researchers to be in a position to be well informed about whether MNAR is a credible issue. Within a multiple regression setting, we demonstrate a proof of concept example and provide recommendations for how the follow-up sample of missing observations should be designed.