Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures

A common drawback with meta analysis is when the variability measures, particularly the variances , are not reported, or “missing” in the individual study. Among the approaches adopted in handling this problem is through exclusion of the studies with missing variances. Alternatively, the missing s...

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Main Author:	Nik Idris, Nik Ruzni
Format:	Conference or Workshop Item
Language:	English English
Published:	2012
Subjects:	Q Science (General)
Online Access:	http://irep.iium.edu.my/25596/ http://irep.iium.edu.my/25596/1/irie_2012_1212.pdf http://irep.iium.edu.my/25596/4/cert_of_participation.pdf

id	iium-25596
recordtype	eprints
spelling	iium-255962012-12-17T08:02:47Z http://irep.iium.edu.my/25596/ Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures Nik Idris, Nik Ruzni Q Science (General) A common drawback with meta analysis is when the variability measures, particularly the variances , are not reported, or “missing” in the individual study. Among the approaches adopted in handling this problem is through exclusion of the studies with missing variances. Alternatively, the missing study-variances could be imputed. This paper examines the analytical implications of these two approaches on the overall effect estimate and the corresponding variances. The bias in these estimates are derived using the Fixed Effect model. The results show that no bias is expected in the estimate of the overall effect using both approaches. Similarly, there is no bias in the variance of the effect estimate when the missing study-variances are imputed and homogeneous study-variances are assumed across the studies. However, if the magnitude of the missing study-variances are mostly larger than those that are reported, imputation leads to under estimation of the variance of the effect estimate. This is a likely case in meta analysis. When studies with missing variances were excluded from analysis, the variances of the effect estimate are overestimated, and the magnitude of the bias in this case is relatively larger when compared to those from complete imputed data. 2012-02-21 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/25596/1/irie_2012_1212.pdf application/pdf en http://irep.iium.edu.my/25596/4/cert_of_participation.pdf Nik Idris, Nik Ruzni (2012) Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures. In: IIUM Research, Invention and Innovation Exhibition, IRIIE 2012, 21 - 22 February, 2012., Cultural Activity Centre (CAC) and KAED Gallery, IIUM.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	Q Science (General)
spellingShingle	Q Science (General) Nik Idris, Nik Ruzni Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures
description	A common drawback with meta analysis is when the variability measures, particularly the variances , are not reported, or “missing” in the individual study. Among the approaches adopted in handling this problem is through exclusion of the studies with missing variances. Alternatively, the missing study-variances could be imputed. This paper examines the analytical implications of these two approaches on the overall effect estimate and the corresponding variances. The bias in these estimates are derived using the Fixed Effect model. The results show that no bias is expected in the estimate of the overall effect using both approaches. Similarly, there is no bias in the variance of the effect estimate when the missing study-variances are imputed and homogeneous study-variances are assumed across the studies. However, if the magnitude of the missing study-variances are mostly larger than those that are reported, imputation leads to under estimation of the variance of the effect estimate. This is a likely case in meta analysis. When studies with missing variances were excluded from analysis, the variances of the effect estimate are overestimated, and the magnitude of the bias in this case is relatively larger when compared to those from complete imputed data.
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 based on fixed effect model for data with missing variability measures
title_short	Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures
title_full	Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures
title_fullStr	Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures
title_full_unstemmed	Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures
title_sort	estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures
publishDate	2012
url	http://irep.iium.edu.my/25596/ http://irep.iium.edu.my/25596/1/irie_2012_1212.pdf http://irep.iium.edu.my/25596/4/cert_of_participation.pdf
first_indexed	2023-09-18T20:38:09Z
last_indexed	2023-09-18T20:38:09Z
_version_	1777409184518635520

Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures

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