On selection of models for continuos meta analysis data with incomplete variability measures

Nik Idris, Nik Ruzni and Sarudin, Norraida (2011) On selection of models for continuos meta analysis data with incomplete variability measures. Far East Journal of Mathematical Sciences (FJMS), 59 (2). pp. 173-187. ISSN 0972-0871 (In Press)

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Abstract

The choice between the fixed and random effects models for providing an overall meta analysis estimates may affect the accuracy of those estimates. When the study-level standard deviations (SDs) are not completely reported or are “missing” selection of a meta analysis model should be done with more caution. In this article, we examine through a simulation study, the effects of the choice of meta analysis model and the techniques of imputation of the missing SDs on the overall meta analysis estimates. The results suggest that imputation should be adopted to estimate the overall effect size, irrespective of the model used. However, the accuracy of the estimates of the corresponding standard error (SE) are influenced by the imputation techniques. For estimates based on the fixed effect model, mean imputation provides better estimates than multiple imputation, while those based on the random effects model are the more robust of the techniques imputation used.

Item Type:	Article (Journal)
Additional Information:	4059/7217
Uncontrolled Keywords:	meta analysis, fixed effect model, random effect model, missing SDs,imputation techniques.
Subjects:	Q Science > Q Science (General)
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:	12 Dec 2011 08:31
Last Modified:	12 Dec 2011 08:31
URI:	http://irep.iium.edu.my/id/eprint/7217

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