Skip to main content
Journal cover image

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach.

Publication ,  Journal Article
Chen, Y; Hong, C; Ning, Y; Su, X
Published in: Stat Med
January 15, 2016

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach and has several attractive features compared with the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

January 15, 2016

Volume

35

Issue

1

Start / End Page

21 / 40

Location

England

Related Subject Headings

  • Urinary Bladder Neoplasms
  • Statistics & Probability
  • Software
  • Models, Statistical
  • Meta-Analysis as Topic
  • Melanoma
  • Linear Models
  • Likelihood Functions
  • Humans
  • Early Diagnosis
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Chen, Y., Hong, C., Ning, Y., & Su, X. (2016). Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach. Stat Med, 35(1), 21–40. https://doi.org/10.1002/sim.6620
Chen, Yong, Chuan Hong, Yang Ning, and Xiao Su. “Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach.Stat Med 35, no. 1 (January 15, 2016): 21–40. https://doi.org/10.1002/sim.6620.
Chen Y, Hong C, Ning Y, Su X. Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach. Stat Med. 2016 Jan 15;35(1):21–40.
Chen, Yong, et al. “Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach.Stat Med, vol. 35, no. 1, Jan. 2016, pp. 21–40. Pubmed, doi:10.1002/sim.6620.
Chen Y, Hong C, Ning Y, Su X. Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach. Stat Med. 2016 Jan 15;35(1):21–40.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

January 15, 2016

Volume

35

Issue

1

Start / End Page

21 / 40

Location

England

Related Subject Headings

  • Urinary Bladder Neoplasms
  • Statistics & Probability
  • Software
  • Models, Statistical
  • Meta-Analysis as Topic
  • Melanoma
  • Linear Models
  • Likelihood Functions
  • Humans
  • Early Diagnosis