Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys
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 di...
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| Format: | Article |
| Language: | English |
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Springer
2020-01-01
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| Series: | Journal of Epidemiology and Global Health |
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| Online Access: | https://www.atlantis-press.com/article/125931649/view |
| _version_ | 1818038460874752000 |
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| author | Hamid Heidarian Miri Jafar Hassanzadeh Saeedeh Hajebi Khaniki Rahim Akrami Ehsan Baradaran Sirjani |
| author_facet | Hamid Heidarian Miri Jafar Hassanzadeh Saeedeh Hajebi Khaniki Rahim Akrami Ehsan Baradaran Sirjani |
| author_sort | Hamid Heidarian Miri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T07:43:06Z |
| format | Article |
| id | doaj.art-26d5cb758c8246239d33d7fc4ffb1fdc |
| institution | Directory Open Access Journal |
| issn | 2210-6014 |
| language | English |
| last_indexed | 2024-12-10T07:43:06Z |
| publishDate | 2020-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of Epidemiology and Global Health |
| spelling | doaj.art-26d5cb758c8246239d33d7fc4ffb1fdc2022-12-22T01:57:15ZengSpringerJournal of Epidemiology and Global Health2210-60142020-01-0110110.2991/jegh.k.191207.001Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS SurveysHamid Heidarian MiriJafar HassanzadehSaeedeh Hajebi KhanikiRahim AkramiEhsan Baradaran SirjaniBackground: 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.https://www.atlantis-press.com/article/125931649/viewMultiple imputationnonresponseSTEPS surveys |
| spellingShingle | Hamid Heidarian Miri Jafar Hassanzadeh Saeedeh Hajebi Khaniki Rahim Akrami Ehsan Baradaran Sirjani Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys Journal of Epidemiology and Global Health Multiple imputation nonresponse STEPS surveys |
| title | Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys |
| title_full | Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys |
| title_fullStr | Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys |
| title_full_unstemmed | Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys |
| title_short | Accuracy of Five Multiple Imputation Methods in Estimating Prevalence of Type 2 Diabetes based on STEPS Surveys |
| title_sort | accuracy of five multiple imputation methods in estimating prevalence of type 2 diabetes based on steps surveys |
| topic | Multiple imputation nonresponse STEPS surveys |
| url | https://www.atlantis-press.com/article/125931649/view |
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