An integrated approach to test for missingness not at random
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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 are not possible when using only the original incomplete data. Our results shed new light on the properties of one such test, and provide an integrated framework for hypothesis testing and designing follow up samples in an efficient cost-effective way.
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