Missing variability in meta-analysis : is imputing always good?

Nik Idris, Nik Ruzni and Abdullah, Mimi Hafizah (2006) Missing variability in meta-analysis : is imputing always good? In: International Conference on Science & Technology: Application in Industry & Education (2006), 8-9 December, 2006, Penang, Malaysia.

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Abstract

This paper examines the implications of the present approaches in handling missing variability in meta analysis on the overall standard error (SE) of the estimate. The approaches are (1) exclusion of the studies with missing standard deviations (SDs) and (2) imputation of the missing SDs. The data was simulated with the SDs assumed to be missing according three scenarios, namely, missing completely at random (MCAR), missing at random (MAR) and not missing at random (NMAR). The study demonstrates that imputation is preferable over excluding the studies with missing standard deviations if the the missing variability occurs completely at random, or if the mechanism for missing variability depends on the size of the studies. However if studies with larger variability measures tend not to report the standard deviations, then imputation will lead to a bias in the standard error of the estimates. As the later case is impossible to ascertain, it is thus recommended that an analysis based upon studies with full available data and imputed data be carried out, and comparison between the two results are made.

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	4059/5555
Uncontrolled Keywords:	meta analysis, standard deviation
Subjects:	H Social Sciences > HA Statistics
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Science > Department of Computational and Theoretical Sciences
Depositing User:	Dr Nik Ruzni Nik Idris
Date Deposited:	16 Aug 2012 15:21
Last Modified:	16 Aug 2012 15:21
URI:	http://irep.iium.edu.my/id/eprint/5555

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