Evidence-Based Medicine
Copyright ©The Author(s) 2015. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Meta-Anal. Dec 26, 2015; 3(6): 225-231
Published online Dec 26, 2015. doi: 10.13105/wjma.v3.i6.225
Towards better meta-analyses in assisted reproductive technology: Fixed, random or multivariate models?
Philippe Lehert
Philippe Lehert, Faculty of Medicine, the University of Melbourne, Southbank 3006, Victoria, Australia
Philippe Lehert, Faculty of Economics, UCL Louvain University, B-7000 Mons, Belgium
Author contributions: This author is the exclusive author of this whole research.
Conflict-of-interest statement: The author declares no competing interests.
Data sharing statement: None.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Dr. Philippe Lehert, PhD, Professor of Statistics, Faculty of Medicine, the University of Melbourne, 801/250 St Kilda Rd, Southbank 3006, Victoria, Australia. philippe.lehert@gmail.com
Telephone: +61-3-96999411 Fax: +61-3-96999411
Received: May 15, 2015
Peer-review started: May 20, 2015
First decision: July 26, 2015
Revised: September 27, 2015
Accepted: October 16, 2015
Article in press: October 19, 2015
Published online: December 26, 2015
Processing time: 223 Days and 6.9 Hours
Abstract

AIM: To study the validity of the fixed, random, and multivariate meta-analytical models applied in meta-analyses in artificial reproduction technique.

METHODS: Based on common characteristics of in vitro fertilization (IVF) meta-analyses, we simulated a large number of data to compare results issued from the fixed model (FM) with the random model (RM). For multiple endpoints meta-analysis (MA), we compared the univariate RM with the multivariate model (MM). Finally, we illustrate our findings in re-analyzing a recent MA.

RESULTS: In our review, although a homogeneous effect was excluded in 89% of the MAs (11%), FM was utilized in 41 studies (82%). From simulations, a concordance of 59% ± 6% was found between the two tests, with up to 65% of falsely significant results with FM. The Q-test on studies characterized by substantial heterogeneity falsely accepted homogeneity in 46% of studies. Comparing separate univariate RM and MM on multiple endpoints studies, MM reduces the between endpoint discrepancy (BED) of 68%, and increases the power of 57% ± 8%. In the example dealing with the controversial effect of luteneizing hormone supplementation to follicle stimulating hormone during ovarian stimulation in IVF cycles, MM reduced BED by 66%, and consistent effects were found for all the endpoints, irrespective of partial reporting.

CONCLUSION: The FM generally may produce falsely significant differences. The RM should always be used. For multiple endpoints, the MM constitutes the best option.

Keywords: Meta-analysis; Random model; Fixed model; Assisted reproductive techniques; In vitro fertilization

Core tip: The numerous meta-analyses (MA) published in assisted reproduction technology (ART) are often characterized by conflicting results. This paper provides evidence that the choice of the meta-analytical model constitutes a major concern. We first identified a general profile of characteristics of the ART studies, compare different models by simulation and resolve a practical case. MA based on the fixed model produce severe biases and falsely significant differences. Better results derive from the random model. For partially reported multiple endpoints, the multivariate model takes advantage of the between-endpoint inter-correlation and provides consistent estimates, better precision, and higher power.