Online Program Home
  My Program

Abstract Details

Activity Number: 127 - Statistical Applications in Epidemiology
Type: Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #322956
Title: Quantifying Publication Bias in Meta-Analysis
Author(s): Lifeng Lin* and Haitao Chu
Companies: University of Minnesota and University of Minnesota
Keywords: Heterogeneity ; Meta-analysis ; Publication bias ; Skewness ; Standardized deviate ; Statistical power
Abstract:

Publication bias (PB) is a serious problem in meta-analysis (MA), affecting the validity and generalization of conclusions. Current approaches to assessing PB can be distinguished into two classes: selection models and funnel-plot-based methods. Selection models use weight functions to adjust the overall effect estimate and are usually treated as sensitivity analyses. Funnel-plot-based methods include visual examination of a funnel plot, regression and rank tests, and the nonparametric trim and fill method. Although these approaches have been widely used, measures for quantifying PB are seldom studied. Such measures can be used as a characteristic of a MA; also, they permit comparisons of PBs between different MAs. Egger's regression intercept may be considered as a candidate measure, but it lacks an intuitive interpretation. We introduce a new measure, the skewness of the standardized deviates, to quantify PB. This measure describes the asymmetry of the collected studies' distribution. In addition, a new test for PB is derived based on the skewness. Large sample properties of the new measure are studied, and its performance is illustrated using simulations and three case studies.


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

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association