Journal of Epidemiology and Global Health

Volume 10, Issue 1, March 2020, Pages 36 - 41

Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys

Authors
Hamid Heidarian Miri1, *, Jafar Hassanzadeh2, Saeedeh Hajebi Khaniki1, Rahim Akrami3, Ehsan Baradaran Sirjani4
1Department of Epidemiology and Biostatistics, Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
2Department of Epidemiology, Research Center for Health Sciences, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
3School of Medicine, Sabzevar University of Medical Sciences, Sabzevar, Iran
4Department of Statistics, Islamic Azad University of Mashhad, Mashhad, Iran
*Corresponding author. Email: HeidarianH@mums.ac.ir
Corresponding Author
Hamid Heidarian Miri
Received 21 April 2019, Accepted 3 December 2019, Available Online 8 January 2020.
DOI
https://doi.org/10.2991/jegh.k.191207.001How to use a DOI?
Keywords
Multiple imputation, nonresponse, STEPS surveys
Abstract

Background: This study was aimed to evaluate five Multiple Imputation (MI) methods in the context of STEP-wise Approach to Surveillance (STEPS) surveys.

Methods: We selected a complete subsample of STEPS survey data set and devised an experimental design consisted of 45 states (3 × 3 × 5), which differed by rate of simulated missing data, variable transformation, and MI method. In each state, the process of simulation of missing data and then MI were repeated 50 times. Evaluation was based on Relative Bias (RB) as well as five other measurements that were averaged over 50 repetitions.

Results: In estimation of mean, Predictive Mean Matching (PMM) and Multiple Imputation by Chained Equation (MICE) could compensate for the nonresponse bias. Ln and Box–Cox (BC) transformation should be applied when the nonresponse rate reaches 40% and 60%, respectively. In estimation of proportion, PMM, MICE, bootstrap expectation maximization algorithm (BEM), and linear regression accompanied by BC transformation could correct for the nonresponse bias. Our findings show that even with 60% of nonresponse rate some of the MI methods could satisfactorily result in estimates with negligible RB.

Conclusion: Decision on MI method and variable transformation should be taken with caution. It is not possible to regard one method as totally the worst or the best and each method could outperform the others if it is applied in its right situation. Even in a certain situation, one method could be the best in terms of validity but the other method could be the best in terms of precision.

Copyright
© 2020 Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Epidemiology and Global Health
Volume-Issue
10 - 1
Pages
36 - 41
Publication Date
2020/01
ISSN (Online)
2210-6014
ISSN (Print)
2210-6006
DOI
https://doi.org/10.2991/jegh.k.191207.001How to use a DOI?
Copyright
© 2020 Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Hamid Heidarian Miri
AU  - Jafar Hassanzadeh
AU  - Saeedeh Hajebi Khaniki
AU  - Rahim Akrami
AU  - Ehsan Baradaran Sirjani
PY  - 2020
DA  - 2020/01
TI  - Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys
JO  - Journal of Epidemiology and Global Health
SP  - 36
EP  - 41
VL  - 10
IS  - 1
SN  - 2210-6014
UR  - https://doi.org/10.2991/jegh.k.191207.001
DO  - https://doi.org/10.2991/jegh.k.191207.001
ID  - Miri2020
ER  -