A comparison of methods to detect publication bias for meta-analysis of continuous data
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 evaluat...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Asian Network for Scientific Information
2012
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/24449/ http://irep.iium.edu.my/24449/ http://irep.iium.edu.my/24449/ http://irep.iium.edu.my/24449/1/published.version.pdf |
| id |
iium-24449 |
|---|---|
| recordtype |
eprints |
| spelling |
iium-244492012-08-16T01:21:14Z http://irep.iium.edu.my/24449/ A comparison of methods to detect publication bias for meta-analysis of continuous data Nik Idris, Nik Ruzni QA Mathematics 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method to be superior in terms of its ability to detect publication bias when it exists, even in presence of only 5% unpublished studies.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. Asian Network for Scientific Information 2012-07-27 Article PeerReviewed application/pdf en http://irep.iium.edu.my/24449/1/published.version.pdf 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) http://scialert.net/abstract/?doi=jas.2012.1413.1417 10.3923/jas.2012.1413.1417 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
QA Mathematics |
| spellingShingle |
QA Mathematics Nik Idris, Nik Ruzni A comparison of methods to detect publication bias for meta-analysis of continuous data |
| description |
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. |
| format |
Article |
| author |
Nik Idris, Nik Ruzni |
| author_facet |
Nik Idris, Nik Ruzni |
| author_sort |
Nik Idris, Nik Ruzni |
| title |
A comparison of methods to detect publication bias for meta-analysis of continuous data |
| title_short |
A comparison of methods to detect publication bias for meta-analysis of continuous data |
| title_full |
A comparison of methods to detect publication bias for meta-analysis of continuous data |
| title_fullStr |
A comparison of methods to detect publication bias for meta-analysis of continuous data |
| title_full_unstemmed |
A comparison of methods to detect publication bias for meta-analysis of continuous data |
| title_sort |
comparison of methods to detect publication bias for meta-analysis of continuous data |
| publisher |
Asian Network for Scientific Information |
| publishDate |
2012 |
| url |
http://irep.iium.edu.my/24449/ http://irep.iium.edu.my/24449/ http://irep.iium.edu.my/24449/ http://irep.iium.edu.my/24449/1/published.version.pdf |
| first_indexed |
2023-09-18T20:36:39Z |
| last_indexed |
2023-09-18T20:36:39Z |
| _version_ |
1777409090589294592 |