Sensitivity Analysis in Multiple Imputation for Missing Data

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Sensitivity Analysis in Multiple Imputation for Missing Data  


ABSTRACT
Multiple imputation, a popular strategy for dealing with missing values, usually assumes that the data are
missing at random (MAR). That is, for a variable Y, the probability that an observation is missing depends
only on the observed values of other variables, not on the unobserved values of Y. It is important to examine
the sensitivity of inferences to departures from the MAR assumption, because this assumption cannot be
verified using the data.
The pattern-mixture model approach to sensitivity analysis models the distribution of a response as the
mixture of a distribution of the observed responses and a distribution of the missing responses. Missing
values can then be imputed under a plausible scenario for which the missing data are missing not at random
(MNAR). If this scenario leads to a conclusion different from that of inference under MAR, then the MAR
assumption is questionable.
This paper reviews the concepts of multiple imputation and explains how you can apply the pattern-mixture
model approach in the MI procedure by using the MNAR statement, which is new in SAS/STAT® 13.1.
You can specify a subset of the observations to derive the imputation model, which is used for pattern
imputation based on control groups in clinical trials. You can also adjust imputed values by using specified
shift and scale parameters for a set of selected observations, which are used for sensitivity analysis with a
tipping-point approach.
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关键词：Sensitivity Imputation Multiple Analysis missing SAS数据分析方法