Using an Approximate Bayesian Bootstrap to Multiply Impute Nonignorable Missing Data
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Thomas R Belin, The University of California-Los Angeles  *Juned Siddique, The University of Chicago 

Keywords: Not Missing at Random, NMAR, Multiple Imputation, Hot-Deck

An Approximate Bayesian Bootstrap (ABB) offers advantages in incorporating appropriate uncertainty when imputing missing data, but most implementations of the ABB have lacked the ability to handle nonignorable missing data where the probability of missingness depends on unobserved values. We outline a strategy for using an ABB to multiply impute nonignorable missing data. The method allows the user to draw inferences and perform sensitivity analyses when the missing data mechanism cannot be assumed to be ignorable. Results from imputing missing values in a longitudinal depression treatment trial as well as a simulation study are presented to demonstrate the method's effectiveness.

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