Model specification and bootstrapping for multiply imputed data: An application to count models for the frequency of alcohol use
W. Scott Comulada
Department of Psychiatry and Biobehavioral Sciences
University of California, Los Angeles
Los Angeles, CA
[email protected]
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Abstract. Stata's mi commands provide powerful tools to conduct multiple
imputation in the presence of ignorable missing data. In this article, I
present Stata code to extend the capabilities of the mi
commands to address two areas of statistical inference where results are
not easily aggregated across imputed datasets. First, mi
commands are restricted to covariate selection. I show how to address
model fit to correctly specify a model. Second, the mi commands
readily aggregate model-based standard errors. I show how standard
errors can be bootstrapped for situations where model assumptions may
not be met. I illustrate model specification and bootstrapping on
frequency counts for the number of times that alcohol was consumed in
data with missing observations from a behavioral intervention.
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W. Scott Comulada
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multiple imputation, missing data, model specification, bootstrap
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