Methodological quality of meta-analyses on treatments for depression: a cross-sectional study




Poster session 3


Tuesday 25 October 2016 - 10:30 to 11:00


All authors in correct order:

Wu XY1, Feng Y1, Ho RS1, Yu YF1, Wong SY1, Yip BH1, Sit RW1, Chung VC1
1 The Chinese University of Hong Kong, China
Presenting author and contact person

Presenting author:

Xin Yin Wu

Contact person:

Abstract text
Background: Well conducted meta-analyses (MAs) can provide best evidence for supporting treatment decision making. Nevertheless, trustworthiness of conclusions can be limited by lack of methodological rigor. Depression is one of the most common mental disorders. Identifying effective antidepressive interventions from high methodological quality MAs is of great help for the management of this disorder.

Objectives: To assess the methodological quality of MAs on depression treatments.

Methods: A cross-sectional study on the bibliographical and methodological characteristics of MAs on depression treatment trials was conducted. Two electronic databases (Cochrane Database of Systematic Reviews and the Database of Abstracts of Reviews of Effects) were searched for potential MAs. Methodological quality was assessed using the validated AMSTAR (A MeaSurement Tool to Assess systematic Reviews) tool by two reviewers independently.

Results: Two-hundred and sixty-four MAs were appraised, with only 18.9% being an update of a previous review. Only 25.4% took into account risk of bias among primary studies when formulating conclusions. In 88.3% of MAs, conflict of interests were not declared fully and the issue is more prevalent among MAs published more recently, or with corresponding authors from Europe or North America. Publication bias was not evaluated in 54.5% of MAs, and only 16.3% searched non-English databases. Harms were not reported in 26.8% of the MAs on pharmacological treatments.

Conclusions: Methodological quality of included MAs is low. Future MAs should strive to improve rigor by considering of risk of bias when formulating conclusions, reporting conflict of interests and treatment harm explicitly, preventing language and publication biases, and ensuring timely updates.