IIUM Repository

A comparison of methods to detect publication bias for meta-analysis of continuous data

Nik Idris, Nik Ruzni (2012) A comparison of methods to detect publication bias for meta-analysis of continuous data. Journal of Applied Sciences, 12 (13). pp. 1413-1417. ISSN 1812-5662 (O), 1812-5654 (P)

[img] PDF (A comparison of methods to detect publication bias for meta-analysis of continuous data) - Published Version
Restricted to Repository staff only

Download (399kB) | Request a copy

Abstract

Publication bias is a serious problem in meta-analysis. Various methods have been developed to detect the presence of publication bias in meta-analysis. These methods have been assessed and compared in many dichotomous studies utilizing the log-odds ratio as the measure of effect. This paper evaluates and compares the performance of three popular methods, namely the Egger’s linear regression method, the Begg and Mazumdar’s rank correlation method and the Duvall and Tweedie’s trim and fill method, on meta-analysis of continuous data. The data comprised simulated meta-analyses with different levels of primary studies in the absence and presence of induced publication bias. The performance of these methods were measured through the power and type 1 error rate for the tests. The results suggest the trim and fill method to be superior in terms of its ability to detect publication bias when it exists, even in presence of only 5% unpublished studies. However this method is not recommended for large meta-analysis as it produces high rate of false-positive results. Both linear regression and rank correlation method performed relatively well in moderate bias but should be avoided in small meta-analysis as their power is very low in this data.

Item Type: Article (Journal)
Additional Information: 4059/24449
Uncontrolled Keywords: publication bias, trim and fill method, linear regression method, rank correlation method, power of test, type 1 error
Subjects: Q Science > QA Mathematics
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: 10 Aug 2012 10:08
Last Modified: 16 Aug 2012 09:21
URI: http://irep.iium.edu.my/id/eprint/24449

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year