Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study
<p><strong>Background</strong></p><p>The purpose of this simulation study is to assess the performance of multiple imputation compared to complete case analysis when assumptions of missing data mechanisms are violated.</p><p><strong>Methods</strong&...
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| Format: | Article |
| Language: | English |
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Milano University Press
2016-03-01
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| Series: | Epidemiology, Biostatistics and Public Health |
| Online Access: | http://ebph.it/article/view/11598 |
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| author | Sander MJ van Kuijk Wolfgang Viechtbauer Louis L Peeters Luc Smits |
| author_facet | Sander MJ van Kuijk Wolfgang Viechtbauer Louis L Peeters Luc Smits |
| author_sort | Sander MJ van Kuijk |
| collection | DOAJ |
| description | <p><strong>Background</strong></p><p>The purpose of this simulation study is to assess the performance of multiple imputation compared to complete case analysis when assumptions of missing data mechanisms are violated.</p><p><strong>Methods</strong></p><p>The authors performed a stochastic simulation study to assess the performance of Complete Case (CC) analysis and Multiple Imputation (MI) with different missing data mechanisms (missing completely at random (MCAR), at random (MAR), and not at random (MNAR)). The study focused on the point estimation of regression coefficients and standard errors.</p><p><strong>Results</strong></p><p>When data were MAR conditional on Y, CC analysis resulted in biased regression coefficients; they were all underestimated in our scenarios. In these scenarios, analysis after MI gave correct estimates. Yet, in case of MNAR MI yielded biased regression coefficients, while CC analysis performed well.<br /><strong>Conclusion</strong></p>The authors demonstrated that MI was only superior to CC analysis in case of MCAR or MAR. In some scenarios CC may be superior over MI. Often it is not feasible to identify the reason why data in a given dataset are missing. Therefore, emphasis should be put on reporting the extent of missing values, the method used to address them, and the assumptions that were made about the mechanism that caused missing data. |
| format | Article |
| id | doaj-art-0734afceec1342f29aa429577b72778d |
| institution | OA Journals |
| issn | 2282-0930 |
| language | English |
| publishDate | 2016-03-01 |
| publisher | Milano University Press |
| record_format | Article |
| series | Epidemiology, Biostatistics and Public Health |
| spelling | doaj-art-0734afceec1342f29aa429577b72778d2025-08-20T01:59:12ZengMilano University PressEpidemiology, Biostatistics and Public Health2282-09302016-03-0113110.2427/1159810616Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation studySander MJ van Kuijk0Wolfgang Viechtbauer1Louis L Peeters2Luc Smits3Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, and department of Epidemiology, Maastricht University, Maastricht, the Netherlands.Department of Statistics and Methodology, Maastricht University, Maastricht, the Netherlands.Department of Obstetrics & Gynaecology, University Medical Centre Utrecht, Utrecht, the Netherlands.Department of Epidemiology, Maastricht University, Maastricht, the Netherlands.<p><strong>Background</strong></p><p>The purpose of this simulation study is to assess the performance of multiple imputation compared to complete case analysis when assumptions of missing data mechanisms are violated.</p><p><strong>Methods</strong></p><p>The authors performed a stochastic simulation study to assess the performance of Complete Case (CC) analysis and Multiple Imputation (MI) with different missing data mechanisms (missing completely at random (MCAR), at random (MAR), and not at random (MNAR)). The study focused on the point estimation of regression coefficients and standard errors.</p><p><strong>Results</strong></p><p>When data were MAR conditional on Y, CC analysis resulted in biased regression coefficients; they were all underestimated in our scenarios. In these scenarios, analysis after MI gave correct estimates. Yet, in case of MNAR MI yielded biased regression coefficients, while CC analysis performed well.<br /><strong>Conclusion</strong></p>The authors demonstrated that MI was only superior to CC analysis in case of MCAR or MAR. In some scenarios CC may be superior over MI. Often it is not feasible to identify the reason why data in a given dataset are missing. Therefore, emphasis should be put on reporting the extent of missing values, the method used to address them, and the assumptions that were made about the mechanism that caused missing data.http://ebph.it/article/view/11598 |
| spellingShingle | Sander MJ van Kuijk Wolfgang Viechtbauer Louis L Peeters Luc Smits Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study Epidemiology, Biostatistics and Public Health |
| title | Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study |
| title_full | Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study |
| title_fullStr | Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study |
| title_full_unstemmed | Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study |
| title_short | Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study |
| title_sort | bias in regression coefficient estimates when assumptions for handling missing data are violated a simulation study |
| url | http://ebph.it/article/view/11598 |
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