Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data |
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| Affiliation: | 1. University of Chicago, Department of Health Studies, Chicago, IL 60637, USA;2. University of California-Los Angeles, Department of Biostatistics, Los Angeles, CA 90095, USA;1. Avignon University, LMA EA 2151, 84000 Avignon, France;2. INRA, BioSP, 84000 Avignon, France;3. INRA, Statistics, GAFL, UR 1052, 84000 Avignon, France;4. Zone Atelier Plaine & Val de Sèvre, CEBC-CNRS, 79360 Villiers-en-Bois, France;1. School of Mathematical Sciences, Beijing Normal University, Beijing 100875, PR China;2. School of Mathematical Sciences, Capital Normal University, Beijing 100048, PR China;1. Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA;2. Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7614, USA;1. Erasmus University Medical Centre, Erasmus Rotterdam, the Netherlands;2. Argos Care Institution, Rotterdam, the Netherlands |
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| Abstract: | 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. This paper outlines 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 automatically 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 performance. We show that a procedure that uses a different type of ABB for each imputed data set accounts for appropriate uncertainty and provides nominal coverage. |
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