The broad aim of a meta-analysis is to provide a review of the literature on some scientific question and to summarize the information in a quantitative manner. It is hoped that by reviewing all the available studies a stronger consensus view can emerge. Meta-analysis taken in this sense is more than statistics and involves a whole methodology for research synthesis.

The statistical part of such a review covers several aspects, of which the combination of the statistical evidence from several experiments is the most important one. It is fair to say that unless the statistical methods being used are sound, the numerical summaries from a meta-analysis may be misleading and no benefits may accrue from combining the studies.

We are developing new statistical techniques for meta-analysis and research synthesis with application to research synthesis in medical and actuarial sciences. Of particular interest are multivariate and sequential methods of meta-analysis in random effects model setting.



  1. Kulinskaya, E., Morgenthaler, S. and Staudte, R. (2010) Combining the Evidence using Stable Weights. Research Synthesis Methods. 1: 284–296. doi: 10.1002/jrsm.20
  2. Kulinskaya, E. and Koricheva, J. (2010), Use of quality control charts for detection of outliers and temporal trends in cumulative meta-analysis. Research Synthesis Methods, 1: 297–307. doi: 10.1002/jrsm.29
  3. Kulinskaya, E., Dollinger, MB and Bjørkestøl, K. (2011) Testing for Homogeneity in Meta-Analysis I. The One Parameter Case: Standardized Mean Difference, Biometrics, 67, 203–212, DOI: 10.1111/j.1541-0420.2010.01442 
  4. Damodaram MS, Story L, Kulinskaya E, Rutherford M, Kumar, S. (2011) Early adverse perinatal complications in preterm growth restricted fetuses. The Australian and New Zealand Journal of Obstetrics and Gynaecology. 51: 204–209
  5. Kulinskaya, E., Dollinger, M. and Bjørkestøl, K. (2012) On the moments of Cochran's Q statistic under the null hypothesis; with application to the meta-analysis of risk difference, Research Synthesis Methods, 2011, 2(4), 254-270, Article first published online: 4 MAR 2012 | DOI: 10.1002/jrsm.54
  6. Kulinskaya, E. and Olkin,I. (2014) An overdispersion model in meta analysis. –Statistical Modelling: An International Journal. February 2014, 14, 1, 49-76, link
  7. Kulinskaya, E., Morgethaler, S. and Staudte R.G. (2014) Combining Statistical Evidence. International Statistical Review, 82, pp. 214–242. Article first published online: 4 FEB 2014.DOI: 10.1111/insr.12037, link
  8. Kulinskaya, E. and Wood, J. (2014) Trial sequential methods for meta-analysis. Research Synthesis Methods, 5(3), 212-220. Article first published online: 28 NOV 2013 DOI: 10.1002/jrsm.1104 
  9. Kulinskaya, E. and Dollinger, M. B. (2015) An improved test for homogeneity of odds ratios based on Cochran's Q-statistic. BMC Medical Research Methodology 2015, 15:49; DOI 10.1186/s12874-015-0034-x
  10. Kulinskaya, E., Huggins, R. and Dogo, S. (2015) Sequential biases in accumulating evidence. Research Synthesis Methods. Published online Dec 1, 2015 DOI: 10.1002/jrsm.1185. 2016 Sep;7(3):294-305 
  11. Kulinskaya, E. and Dollinger, M.B. Commentary on ‘Misunderstandings about Q and ‘Cochran’s Q test’ in meta-analysis’, Statistics in Medicine, 2015, 35(4):501-502 · February 20, 2016, DOI: 10.1002/sim.6758 
  12. Kulinskaya. E. and Wood, J. Re: The knowledge system underpinning healthcare is not fit for purpose and must change. BMJ Rapid Responses 26/10/2015
  13. Bakbergenuly, I., Kulinskaya E. and Morgenthaler S. Inference for Binomial Probability Based on Dependent Bernoulli Random Variables with Applications to Meta-analysis and Group Level Studies. Biometrical Journal 58 (2016) 4, 896–914 DOI: 10.1002/bimj.201500115, published online 18 May 2016,
  14. Samson Henry Dogo, Allan Clark, Elena Kulinskaya (2016) Sequential change detection and monitoring of temporal trends in random-effect meta-analysis; Research Synthesis Methods, First published: 8 December 2016, DOI: 10.1002/jrsm.1222
  15. Bakbergenuly, I. and Kulinskaya E. (2017) Beta-binomial model for meta-analysis of odds-ratios, Statistics in Medicine,36: 1715–1734. doi:10.1002/sim.7233. published online 25/01/2017, One of 20 most downloaded paper in SM 2016-2018.
  16. Bakbergenuly, I. and Kulinskaya E. (2018) Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study. BMC Medical Research Methodology, 18:70,
  17. Bakbergenuly, I., Hoaglin, DC. and Kulinskaya E. (2019) Pitfalls of Parameter Restrictions in Meta-Analysis. Research Synthesis Methods, 10:398–419, doi = 10.1002/jrsm.1347,, published online 10 March 2019. 
  18. Bakbergenuly I, Hoaglin DC and Kulinskaya E. Estimation in meta-analyses of mean difference and standardized mean difference. Statistics in Medicine.2019;1–21. .2020; 3 (2), 171-191
  19. Julia Koricheva and Elena Kulinskaya. Temporal trends in effect sizes: a threat to policy making? Trends in Ecology and Evolution (TREE) 34(10), 895-902 , Oct 2019, online June 10, 2019 
  20. Bakbergenuly, I., Hoaglin, D. and Kulinskaya E. Methods for estimating between-study variance and overall effect in meta-analysis of odds-ratios Research Synthesis Methods, 11:426–442, 2020b. doi: 10.1002/jrsm.1404. 
  21. Bakbergenuly, I., Hoaglin, D. and Kulinskaya E. Estimation in meta-analyses of response ratios. BMC Medical Research Methodology, (2020) 20:263 Oct 2020.

Research Team

Prof. Elena Kulinskaya,  Ilyas BakbergenulEung Mah


  • Prof Michael B Dollinger, Pacific Lutheran University, USA
  • David C. Hoaglin. University of Massachusetts Medical School
  • Prof Richard Huggins, Melbourne, Australia
  • Prof Julia Koricheva, Royal Holloway, UK
  • Prof Stephan Morgenthaler, EPFL, Lausanne, Switzerland
  • Prof Robert G. Staudte, La Trobe University, Melbourne, Australia