Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys.
Multiple imputation
STEPS surveys
nonresponse
Journal
Journal of epidemiology and global health
ISSN: 2210-6014
Titre abrégé: J Epidemiol Glob Health
Pays: Switzerland
ID NLM: 101592084
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
21
04
2019
accepted:
03
12
2019
entrez:
17
3
2020
pubmed:
17
3
2020
medline:
7
4
2021
Statut:
ppublish
Résumé
This study was aimed to evaluate five Multiple Imputation (MI) methods in the context of STEP-wise Approach to Surveillance (STEPS) surveys. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 32175708
pii: j10/1/36
doi: 10.2991/jegh.k.191207.001
pmc: PMC7310803
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
36-41Informations de copyright
© 2020 Atlantis Press International B.V.
Déclaration de conflit d'intérêts
The authors declare they have no conflicts of interest.
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