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Bibliography

Source: vignettes/bibliography.qmd

Key references

This page collects the primary methodological references for models implemented in bayesma. References are grouped by topic.

Foundational meta-analysis

  • DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 7(3), 177–188.

  • Hedges, L. V., & Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3(4), 486–504.

  • Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558.

Bayesian meta-analysis

  • Higgins, J. P. T., Spiegelhalter, D. J., & Murrell, P. (2009). A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society: Series A, 172(1), 137–159.

  • Röver, C. (2020). Bayesian random-effects meta-analysis using the bayesmeta R package. Journal of Statistical Software, 93(6).

  • Sutton, A. J., & Abrams, K. R. (2001). Bayesian methods in meta-analysis and evidence synthesis. Statistical Methods in Medical Research, 10(4), 277–303.

Stan and computational methods

  • Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., … & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1).

  • Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P.-C. (2021). Rank-normalization, folding, and localization: An improved R̂\hat{R} for assessing convergence of MCMC. Bayesian Analysis, 16(2), 667–718.

  • Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413–1432.

Small-sample adjustments

  • Hartung, J. (1999). An alternative method for meta-analysis. Biometrical Journal, 41(8), 901–916.

  • Knapp, G., & Hartung, J. (2003). Improved tests for a random effects meta-regression with a single covariate. Statistics in Medicine, 22(17), 2693–2710.

  • Sidik, K., & Jonkman, J. N. (2006). Robust variance estimation for random effects meta-analysis. Computational Statistics & Data Analysis, 50(12), 3681–3701.

Publication bias

PET-PEESE

  • Stanley, T. D., & Doucouliagos, H. (2014). Meta-regression approximations to reduce publication selection bias. Research Synthesis Methods, 5(1), 60–78.

Egger’s test

  • Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634.

  • Shi, L., Lin, L., & Chu, H. (2020). Bayesian methods for meta-analysis. Statistics in Medicine, 39(17), 2338–2351.

Selection models

  • Vevea, J. L., & Hedges, L. V. (1995). A general linear model for estimating effect size in the presence of publication bias. Psychometrika, 60(3), 419–435.

  • Copas, J. B., & Shi, J. Q. (2001). A sensitivity analysis for publication bias in systematic reviews. Statistical Methods in Medical Research, 10(4), 251–265.

Mixture models

  • Maier, M., Bartoš, F., & Wagenmakers, E.-J. (2023). Robust Bayesian meta-analysis: Addressing publication bias with model-averaging. Psychological Methods, 28(1), 107–122.

BC-BNP

  • Verde, P. E., & Rosner, G. L. (2025). Bias-corrected Bayesian non-parametric meta-analysis. Biometrics. (Forthcoming)

Heterogeneity priors

  • Turner, R. M., Jackson, D., Wei, Y., Thompson, S. G., & Higgins, J. P. T. (2015). Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis. Statistics in Medicine, 34(6), 984–998.

  • Rhodes, K. M., Turner, R. M., & Higgins, J. P. T. (2015). Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. Journal of Clinical Epidemiology, 68(1), 52–60.

Bias-adjusted models

  • Verde, P. E. (2021). A bias-corrected meta-analysis model for combining studies of different types and quality. Biometrical Journal, 63(8), 1618–1640.

  • Jung, Y., & Aloe, A. M. (2026). Domain-specific bias adjustment in Bayesian meta-analysis. Research Synthesis Methods. (Forthcoming)

RoBMA

  • Bartoš, F., Maier, M., Wagenmakers, E.-J., Doucouliagos, H., & Stanley, T. D. (2023). Robust Bayesian meta-analysis: Model-based outlier and publication bias adjustment. Multivariate Behavioral Research, 58(4), 679–705.

  • Meng, X.-L., & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistica Sinica, 6(4), 831–860.

Multivariate meta-analysis

  • Riley, R. D., Abrams, K. R., Sutton, A. J., Lambert, P. C., & Thompson, J. R. (2007). Bivariate random-effects meta-analysis and the estimation of between-study correlation. BMC Medical Research Methodology, 7(1), 3.

  • Jackson, D., White, I. R., & Riley, R. D. (2013). A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression. Biometrical Journal, 55(2), 231–245.

One-stage models

  • Jackson, D., Law, M., Stijnen, T., Viechtbauer, W., & White, I. R. (2018). A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio. Statistics in Medicine, 37(7), 1059–1085.

INSPECT-SR

  • Wilkinson, J., Tanner, S., Mellor, A., & Brealey, S. (2025). INSPECT-SR: A tool for assessing the trustworthiness of randomised controlled trials. BMJ Open. (Forthcoming)

PRIMED

  • Pustejovsky, J. E., Zhang, Q., & Tipton, E. (2026). Preliminary investigation of meta-analytic data structure. Research Synthesis Methods. (Forthcoming)