Multiple Imputation to Correct for Nonresponse Bias: Application in Non-communicable Disease Risk Factors Survey

Abstract

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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Successive Wave Analysis to Assess Nonresponse Bias in a Statewide Random Sample Testing Study for SARS-CoV-2

“…We believe this approach can either complement or substitute for more complicated and time-consuming methods of weighting variables or constructing propensity models that require access to population characteristics from which the sample was drawn. 22 , 23 …”

“…■ We believe this approach can either complement or substitute for more complicated and time-consuming methods of weighting variables or constructing propensity models that require access to population characteristics from which the sample was drawn. 22,23 ■ To help uptake of successive wave analysis among public health researchers, journal editors and reviewers can ask that researchers provide evidence of assessing nonresponse when applicable to their study methods and context. Doing so will strengthen the evidence base produced by epidemiological studies in the field.…”

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