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Activity Number: 263 - Addressing Incomplete Data in Public Health Studies: New Frontiers for Network-Based Studies and Meta-Analyses
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
Sponsor: ENAR
Abstract #313405
Title: Accounting for Small-Study Effects Using a Bivariate Trim and Fill Meta-Analysis Procedure
Author(s): Chongliang Luo* and Yong Chen
Companies: University of Pennsylvania and University of Pennsylvania
Keywords: bivariate meta-analysis; small-study effects; trim and fill; galaxy plot; funnel plot; antidepressant drug trials

In meta-analyses, small-study effects (SSE) refer to the phenomenon that smaller studies show different, often larger, treatment effects than larger studies, which may lead to incorrect, commonly optimistic estimates of treatment effects. The trim and fill procedure (T&F) is a non-parametric method to identify and adjust for SSE based on the funnel plot and is widely used in practice due to its simplicity. However, for a meta-analysis with multiple outcomes, the estimated number of studies for different outcomes may be different, leading to inconsistent conclusions. We propose a bivariate T&F procedure to account for SSE in a meta-analysis, based on a recently developed visualization tool of bivariate meta-analysis, known as the galaxy plot. The method relies on the symmetry of the galaxy plot and assumes that some studies are suppressed by a straight line. Compared to the univariate method, the proposed method yields consistent conclusion about SSE and identifies missing studies with possible linear combination of outcomes. The proposed approach is validated using simulated data and is applied to a meta-analysis of efficacy and safety of antidepressant drugs.

Authors who are presenting talks have a * after their name.

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