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

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  • Hamid Heidarian Miri
  • Jafar Hassanzadeh
  • Abdolreza Rajaeefard
  • Majid Mirmohammadkhani
  • Kambiz Ahmadi Angali

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.

Suggested Citation

  • Hamid Heidarian Miri & Jafar Hassanzadeh & Abdolreza Rajaeefard & Majid Mirmohammadkhani & Kambiz Ahmadi Angali, 2016. "Multiple Imputation to Correct for Nonresponse Bias: Application in Non-communicable Disease Risk Factors Survey," Global Journal of Health Science, Canadian Center of Science and Education, vol. 8(1), pages 133-133, January.
    • Handle: RePEc:ibn:gjhsjl:v:8:y:2016:i:1:p:133
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    References listed on IDEAS

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    1. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
    2. Ting Lin, 2010. "A comparison of multiple imputation with EM algorithm and MCMC method for quality of life missing data," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(2), pages 277-287, February.
    3. Royston, Patrick & White, Ian R., 2011. "Multiple Imputation by Chained Equations (MICE): Implementation in Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i04).
    4. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
    5. Adam Davey & Michael J. Shanahan & Joseph L. Schafer, 2001. "Correcting for Selective Nonresponse in the National Longitudinal Survey of Youth Using Multiple Imputation," Journal of Human Resources, University of Wisconsin Press, vol. 36(3), pages 500-519.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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