Multiple Imputation to Correct for Nonresponse Bias: Application in
Non-communicable Disease Risk Factors Survey
#MMPMID26234966
Heidarian Miri H
; Hassanzadeh J
; Rajaeefard A
; Mirmohammadkhani M
; Ahmadi Angali K
Glob J Health Sci
2015[May]; 8
(1
): 133-42
PMID26234966
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BACKGROUND: This study was carried out to use multiple imputation (MI) in order
to correct for the potential nonresponse bias in measurements related to variable
fasting blood glucose (FBS) in non-communicable disease risk factors survey
conducted in Iran in 2007. METHODS: Five multiple imputation methods as bootstrap
expectation maximization, multivariate normal regression, univariate linear
regression, MI by chained equation, and predictive mean matching were applied to
impute variable fasting blood sugar. To make FBS consistent with normality
assumption natural logarithm (Ln) and Box-Cox (BC) transformations were used
prior to imputation. Measurements from which we intended to remove nonresponse
bias included mean of FBS and percentage of those with high FBS. RESULTS: For
mean of FBS results didn't considerably change after applying MI methods.
Regarding the prevalence of high blood sugar all methods on original scale tended
to increase the estimates except for predictive mean matching that along with all
methods on BC or Ln transformed data didn't change the results. CONCLUSIONS:
FBS-related measurements didn't change after applying different MI methods. It
seems that nonresponse bias was not an important challenge regarding these
measurements. However use of MI methods resulted in more efficient estimations.
Further studies are encouraged on accuracy of MI methods in these settings.
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