title: A modified regression method to test publication bias in meta-analyses with binary outcomes.
abstract: BACKGROUND
  The tendency towards publication bias is greater for observational studies than for randomized clinical trials. Several statistical methods have been developed to test the publication bias. However, almost all existing methods exhibit rather low power or have inappropriate type I error rates.
  
  
  METHODS
  We propose a modified regression method, which used a smoothed variance to estimate the precision of a study, to test for publication bias in meta-analyses of observational studies. A comprehensive simulation study is carried out, and a real-world example is considered.
  
  
  RESULTS
  The simulation results indicate that the performance of tests varies with the number of included studies, level of heterogeneity, event rates, and sample size ratio between two groups. Neither the existing tests nor the newly developed method is particularly powerful in all simulation scenarios. However, our proposed method has a more robust performance across different settings. In the presence of heterogeneity, the arcsine-Thompson test is a suitable alternative, and Peters' test can be considered as a complementary method when mild or no heterogeneity is present.
  
  
  CONCLUSIONS
  Several factors should be taken into consideration when employing asymmetry tests for publication bias. Based on our simulation results, we provide a concise table to show the appropriate use of regression methods to test for publication bias based on our simulation results.
authors.forename: Zhi-Chao
authors.initials: ZC
authors.lastname: Jin
authors.forename: Cheng
authors.initials: C
authors.lastname: Wu
authors.affiliation: Department of Health Statistics, Second Military Medical University, No, 800 Xiangyin Road, Shanghai 200433, China. azhou@u.washington.edu.
authors.forename: Xiao-Hua
authors.initials: XH
authors.lastname: Zhou
authors.forename: Jia
authors.initials: J
authors.lastname: He
mesh_terms: D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D015201:Meta-Analysis as Topic; D064887:Observational Studies as Topic; D020641:Polymorphism, Single Nucleotide; D011225:Pre-Eclampsia / Q000453:epidemiology / Q000235:genetics; D011247:Pregnancy; D011249:Pregnancy Complications, Cardiovascular; D017594:Publication Bias / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis