Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance
This paper examines, analytically, the biases introduced in the meta analysis estimates when the study-level variances are missing with non-random missing mechanism (MNAR). Two common approaches in handling this problem is considered, namely, the missing variances are imputed, and the studies with m...
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Universiti Teknologi Malaysia
2011
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| Online Access: | http://irep.iium.edu.my/7219/ http://irep.iium.edu.my/7219/ http://irep.iium.edu.my/7219/4/estimating_the_bias_in_meta_analysis_estimates_for_continuous_data.pdf |
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iium-72192012-02-02T23:46:19Z http://irep.iium.edu.my/7219/ Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance Nik Idris, Nik Ruzni QA Mathematics This paper examines, analytically, the biases introduced in the meta analysis estimates when the study-level variances are missing with non-random missing mechanism (MNAR). Two common approaches in handling this problem is considered, namely, the missing variances are imputed, and the studies with missing study variances are omitted from the analysis. The results suggest the variance will be underestimated if the magnitude of the study-variances that are missing are mostly larger implying false impression of precision. On the other hand, if the missing variances are mostly smaller, the variance of the e®ect size will be overestimated. Universiti Teknologi Malaysia 2011-12 Article PeerReviewed application/pdf en http://irep.iium.edu.my/7219/4/estimating_the_bias_in_meta_analysis_estimates_for_continuous_data.pdf Nik Idris, Nik Ruzni (2011) Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance. Matematika, 27 (2). pp. 121-128. ISSN 0127-8274 http://www.fs.utm.my/matematika/content/view/278/31/ |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
QA Mathematics |
| spellingShingle |
QA Mathematics Nik Idris, Nik Ruzni Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance |
| description |
This paper examines, analytically, the biases introduced in the meta analysis estimates when the study-level variances are missing with non-random missing mechanism (MNAR). Two common approaches in handling this problem is considered, namely, the missing variances are imputed, and the studies with missing study variances are omitted from the analysis. The results suggest the variance will be underestimated if the magnitude of the study-variances that are missing are mostly larger implying false impression of precision. On the other hand, if the missing variances are mostly smaller, the variance of the e®ect size will be overestimated. |
| format |
Article |
| author |
Nik Idris, Nik Ruzni |
| author_facet |
Nik Idris, Nik Ruzni |
| author_sort |
Nik Idris, Nik Ruzni |
| title |
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_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_sort |
estimating the bias in meta analysis estimates for continuous data with non-random missing study variance |
| publisher |
Universiti Teknologi Malaysia |
| publishDate |
2011 |
| url |
http://irep.iium.edu.my/7219/ http://irep.iium.edu.my/7219/ http://irep.iium.edu.my/7219/4/estimating_the_bias_in_meta_analysis_estimates_for_continuous_data.pdf |
| first_indexed |
2023-09-18T20:16:30Z |
| last_indexed |
2023-09-18T20:16:30Z |
| _version_ |
1777407823311798272 |