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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Main Author:	Nik Idris, Nik Ruzni
Format:	Conference or Workshop Item
Language:	English
Published:	2010
Subjects:	HA29 Theory and method of social science statistics
Online Access:	http://irep.iium.edu.my/5551/ http://irep.iium.edu.my/5551/ http://irep.iium.edu.my/5551/1/skskm2010_manu.pdf

id	iium-5551
recordtype	eprints
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 Conference or Workshop Item 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
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	HA29 Theory and method of social science statistics
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
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.
format	Conference or Workshop Item
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
publishDate	2010
url	http://irep.iium.edu.my/5551/ http://irep.iium.edu.my/5551/ http://irep.iium.edu.my/5551/1/skskm2010_manu.pdf
first_indexed	2023-09-18T20:14:12Z
last_indexed	2023-09-18T20:14:12Z
_version_	1777407677917298688

Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance

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