Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance
When study variances are not reported or ‘missing”, it is common practice in meta analysis to assume that the missing variances are missing completely at random (MCAR). In practice, however, it is possible that the variances are not missing completely at random (NMAR). In this paper, we examine, ana...
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| Format: | Proceeding Paper |
| Language: | English |
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2010
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| Online Access: | http://irep.iium.edu.my/5551/ http://irep.iium.edu.my/5551/1/skskm2010_manu.pdf |
| _version_ | 1848776523441504256 |
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| author | Nik Idris, Nik Ruzni |
| author_facet | Nik Idris, Nik Ruzni |
| author_sort | Nik Idris, Nik Ruzni |
| building | IIUM Repository |
| collection | Online Access |
| description | When study variances are not reported or ‘missing”, it is common practice in meta analysis to assume that the missing variances are missing completely at random (MCAR). In practice, however, it is possible that the variances are not missing completely at random (NMAR). In this paper, we examine, analytically, the biases introduce in the meta analysis estimates when the missing study variances occur with non-random missing mechanism (MNAR), namely, when the magnitude of the missing variances are mostly larger than those that are reported. In meta analysis, this is more likely to occur in studies which carry larger variances. We looked at two common approaches in handling this problem, namely, the missing variances are imputed using the mean imputation, and the studies with missing study-variances are omitted from the analysis. The results suggest that for the estimate of the variance of the effect size, if the magnitude of the study-variances that are missing are mostly larger than those that are reported, the variance of the effect size will be underestimated. Thus under MNAR, the mean imputation gives false impression of precision as the estimated variance of the overall effect is too small. On the other hand, if the missing variances are mostly smaller, the variance will be overestimated. |
| first_indexed | 2025-11-14T14:31:27Z |
| format | Proceeding Paper |
| id | iium-5551 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T14:31:27Z |
| publishDate | 2010 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-55512011-11-22T00:39:18Z http://irep.iium.edu.my/5551/ Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance Nik Idris, Nik Ruzni HA29 Theory and method of social science statistics When study variances are not reported or ‘missing”, it is common practice in meta analysis to assume that the missing variances are missing completely at random (MCAR). In practice, however, it is possible that the variances are not missing completely at random (NMAR). In this paper, we examine, analytically, the biases introduce in the meta analysis estimates when the missing study variances occur with non-random missing mechanism (MNAR), namely, when the magnitude of the missing variances are mostly larger than those that are reported. In meta analysis, this is more likely to occur in studies which carry larger variances. We looked at two common approaches in handling this problem, namely, the missing variances are imputed using the mean imputation, and the studies with missing study-variances are omitted from the analysis. The results suggest that for the estimate of the variance of the effect size, if the magnitude of the study-variances that are missing are mostly larger than those that are reported, the variance of the effect size will be underestimated. Thus under MNAR, the mean imputation gives false impression of precision as the estimated variance of the overall effect is too small. On the other hand, if the missing variances are mostly smaller, the variance will be overestimated. 2010-12-21 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/5551/1/skskm2010_manu.pdf Nik Idris, Nik Ruzni (2010) Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance. In: Seminar Kebangsaan Sains Komputer Dan Matematik 2010, 21-22 December 2010, Kota Kinabalu, Sabah. http://www.skskm.net |
| spellingShingle | HA29 Theory and method of social science statistics Nik Idris, Nik Ruzni Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance |
| title | Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance |
| title_full | Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance |
| title_fullStr | Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance |
| title_full_unstemmed | Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance |
| title_short | Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance |
| title_sort | estimating the bias in meta analysis estimates for continuous data with non-random missing study variance |
| topic | HA29 Theory and method of social science statistics |
| url | http://irep.iium.edu.my/5551/ http://irep.iium.edu.my/5551/ http://irep.iium.edu.my/5551/1/skskm2010_manu.pdf |