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Abo Zaid G, Sauerbrei W, Riley RD. 2012. Individual patient data meta-analysis of prognostic factor studies: state of the art? BMC Med Res Meth 12:56 doi:10.1186/1471-2288-12-56 [abstract]

Abrams KR, Gillies CL, Lambert PC. 2005. Meta-analysis of heterogeneously reported trials assessing change from baseline. Stat Med 24:3823-3844. [abstract]

Ades AE, Lu G, Higgins JP. 2005. The interpretation of random-effects meta-analysis in decision models. Med Decis Making 25:646-654. [abstract]

Ahmed I, Sutton AJ, Riley RD. 2012. Assessment of publication bias, selection bias and unavailable data in meta-analyses using individual participant data: a database survey BMJ 344:d7762 [abstract

Anzures-Cabrera J, Higgins JPT. 2010. Graphical displays for univariate meta-analysis: an overview with suggestions for practice. Research Synthesis Methods 1: 66-80. [abstract]

Arends LR, Hunink MG, Stijnen T. 2008. Meta-analysis of summary survival curve data. Stat Med 27: 4381-4396. [abstract]


Balk EM, Bonis PAL, Moskowitz H, Schmid CH, Ioannidis JPA, Wang C, Lau J. Correlation of quality measures with estimates of treatment effect in meta-analyses of randomized controlled trials. JAMA 287:2973-2982, 2002.  [abstract]

Baker R, Jackson D. 2010. Inference for meta-analysis with a suspected temporal trend. Biom J. Aug; 52(4): 538-51. [abstract]

Bender R, Bunce C, Clarke M, Gates S, Lange S, Pace NL, Thorlund K. 2008. Attention should be given to multiplicity issues in systematic reviews. J Clin Epidemiol 61:857-865. [abstract]

Bender R, Koch A, Skipka G, Kaiser T, Lange S. 2010. No inconsistent trial assessments by NICE and IQWIG: different assessment goals may lead to different assessment results regarding subgroup analyses. J. Clin. Epidemiol. 63, 1305-1307. [abstract]

Bender R, Koch A, Skipka G, Kaiser T, Lange S. 2011. The assessment of heterogeneity is mandatory in clinical trials and systematic reviews. J. Clin. Epidemiol. 64, 452.[abstract]

Berlin JA, Santanna J, Schmid CH, Szczech LA, Feldman HI for the Anti-lymphocyte Antibody Induction Therapy Study Group. Individual Patient versus Group-level Data Meta-regressions for the Investigation of Treatment Effect Modifiers: Ecological Bias Rears Its Ugly Head. Statistics in Medicine 21: 371-387, 2002. [abstract]

Borenstein M, Hedges LV, Higgins J, Rothstein H. 2009. Introduction to Meta-Analysis. John Wiley & Sons Inc.

Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. 2010. A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods 1: 97-111. [abstract]

Borm GF, Donders AR. 2009. Updating meta-analyses leads to larger type I errors than publication bias. J Clin Epidemiol.62(8):825-830 [abstract]

Bowden J, Jackson D, Thompson SG.2010. Modelling multiple sources of dissemination bias in meta-analysis. Stat Med. Mar 30;29(7-8): 945-55. [abstract]

Bowden J, Tierney J, Simmonds M, Copas AJ, Higgins JPT. 2011. Individual patient data meta-analysis of time-to-event outcomes: one-stage versus two-stage approaches to estimating the hazard ratio under a random effects model. Research Synthesis Methods 2:150-62. [abstract

Bradburn MJ, Deeks JJ, Berlin JA, Russell LA. 2007. Much ado about nothing: a comparison of the performance of meta-analytical methods with rare events. Stat Med 26: 53-77. [abstract]

Brockhaus AC, Bender R, Skipka G. 2014. The Peto odds ratio viewed as a new effect measure. Stat Med 10; 33(28):4861-74. [abstract]

Brok J, Thorlund K, Gluud C, Wetterslev J. 2008. Trial sequential analysis reveals insufficient information size and potentially false positive results in many meta-analyses. J Clin Epidemiol 61:763-769. [abstract]

Brok J, Thorlund K, Wetterslev J, Gluud C. 2009. Apparently conclusive meta-analyses may be inconclusive--Trial sequential analysis adjustment of random error risk due to repetitive testing of accumulating data in apparently conclusive neonatal meta-analyses. Int J Epidemiol 38:287-298. [abstract]

Bujkieicz , Jones HE, Lai MCW, Cooper NJ, Hawkins N, Squires H, Abrams KR, Spiegelhalter DJ, Sutton AJ. 2011. Development of a Transparent Interactive Decision Interrogator to facilitate the decision making process in health care. Value in Health.14 (5):768-776 [abstract]


Caldwell DM, Ades AE, Higgins JP. 2005. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ 331:897-900. [abstract]

Caldwell DM, Welton NJ, Ades AE. 2010. Mixed treatment comparison analysis provides internally coherent treatment effect estimates based on overviews of reviews and can reveal inconsistency. J Clin Epidemiol 63(8):875-82. [abstract]

Cappelleri JC, Ioannidis JPA, Schmid CH, de Ferranti SD, Aubert M, Chalmers TC and Lau J. 1996. Large Trials Vs Meta-Analysis of Smaller Trials: How Do Their Results Compare? Journal of the American Medical Association 276:1332-1338. [abstract]

Carpenter J, Rücker G, and Schwarzer G 2011. Assessing the sensitivity of meta-analysis to selection bias: a multiple imputation approach. Biometrics 67 (3): 1066-1072. [abstract]

Carpenter J, Rücker G, Schwarzer G. 2008. Comments on 'Fixed vs random effects meta-analysis in rare event studies: the rosiglitazone link with myocardial infarction and cardiac death'. Stat Med 27:3910-3912. [abstract]

Carpenter JR, Schwarzer G, Rücker G, Kunstler R. 2009. Empirical evaluation showed that the Copas selection model provided a useful summary in 80% of meta-anlyses Clin Epidemiol 62:624-631. [abstract]

Chaimani A, Vasiliadis HS, Pandis N, Schmid CH, Welton NJ and Salanti G. 2013. Effects of study precision and risk of bias in networks of interventions: a network meta-epidemiological study. International Journal of Epidemiology 42: 1120-1131. [abstract]

Chu H, Guo H. 2009. A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics 10:201-203. [abstract]

Cooper NJ, Sutton AJ, Lu G, Khunti K. 2006. Mixed comparison of stroke prevention treatments in individuals with nonrheumatic atrial fibrillation. Archives of Internal Medicine 166:1269-1275. [abstract].

Cooper NJ, J Peters, MCW Lai, P Juni, S Wandel, S Palmer, M Paulden, S Conti, NJ Welton, KR Abrams, S Bujkiewicz, D Spiegelhalter, AJ Sutton. 2011. How valuable are multiple treatment comparison methods in evidence-based healthcare evaluation? Value in Health. Mar-Apr;14(2):371-80. [abstract]

Copas JB, Malley PF. 2008. A robust P-value for treatment effect in meta-analysis with publication bias. Stat Med 27:4267-4278. [abstract]

Crowther M, Riley RD, Wang J, Staessen J, Gueyffier F, Lambert PC. 2012. Individual participant data meta-analysis of survival data using Poisson regression models. BMC Med Res Meth 12:34 [abstract]

Curtin F, Altman DG, Elbourne D. 2002.  Meta-analysis combining parallel and cross-over clinical trials. I: Continuous outcomes. Stat Med 21:2131-2144. [abstract]

Curtin F, Elbourne D, Altman DG. 2002. Meta-analysis combining parallel and cross-over clinical trials. II: Binary outcomes. Stat Med 21:2145-2159. [abstract]

Curtin F, Elbourne D, Altman DG. 2002. Meta-analysis combining parallel and cross-over clinical trials. III: The issue of carry-over. Stat Med 21:2161-2173. [abstract]


Deeks JJ. 2002.  Issues in the selection of a summary statistic for meta-analysis of clinical trials with binary outcomes.  Stat Med 21:1575-1600. [abstract]

Dekkers OM, Soonawala D, Vandenbroucke JP, Egger M. 2011. Reporting of noninferiority trials was incomplete in trial registries. J Clin Epidemiol. 64(9):1034-8. [abstract]

Dias S, McNamee R, Vail A. 2008. Bias in frequently reported analyses of subfertility trials. Stat Med 27:5605-5619. [abstract]

Dias S, McNamee R, Vail A. 2006. Evidence of improving quality of reporting of randomized controlled trials in subfertility. Hum Reprod 21:2617-2627. [abstract]

Dias S, Welton NJ, Marinho VCC, Salanti G, Higgins JPT, Ades AE. 2010. Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta-analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society) 173(3), July, 613–629.  [abstract]

Donner A, Klar N. 2002. Issues in the meta-analysis of cluster randomized trials. Stat Med 21:2971-2980. [abstract]

Donner A, Piaggio G, Villar J. 2001. Statistical methods for the meta-analysis of cluster randomization trials. Stat Methods Med Res 10:325-338. [abstract]

Egger M, Davey Smith G, Altman DG. 2001. Systematic Reviews in Health Care; Meta-analysis in context (2nd ed). London: BMJ Books.[abstract]

Elbourne DR, Altman DG, Higgins JP, Curtin F, Worthington HV, Vail A. 2002. Meta-analyses involving cross-over trials: methodological issues. Int J Epidemiol 31:140-149. [abstract]

Engels EA, Schmid CH, Terrin N, Olkin I and Lau J. Heterogeneity and Statistical Significance in Meta-Analysis: An Empirical Study of 125 Meta-Analyses. Statistics in Medicine 19:1707-1728, 2000.


Fotios Siannis, Jessica K Barrett and Vern T Farewell. 2010. One-stage parametric meta-analysis of time-to-event outcomes, Statistics in Medicine 29: 3030-3045. [abstract]

Friedrich JO, Adhikari NK, Beyene J. 2008. The ratio of means method as an alternative to mean differences for analyzing continuous outcome variables in meta-analysis: a simulation study. BMC Med Res Methodol 8:32. [abstract]

Furukawa TA, Barbui C, Cipriani A, Brambilla P, Watanabe N. 2006. Imputing missing standard deviations in meta-analyses can provide accurate results. J Clin Epidemiol 59:7-10. [abstract]


Glenny AM, Altman DG, Song F, Sakarovitch C, Deeks JJ, D'Amico R, Bradburn M, Eastwood AJ. 2005. Indirect comparisons of competing interventions. Health Technol Assess 9:1-iv. [abstract]

Greenland S, O’Rourke K. 2001. On the bias produced by quality scores in meta-analysis, and a hierarchical view of proposed solutions. Biostatistics 2(4), 463-471 [abstract]

Greenland S, O' Rourke K. 2008. Meta-Analysis. In Modern Epidemiology, 3rd ed. Edited by Rothman KJ,GreenlandS, Lash T. Lippincott Williams and Wilkins.

Guddat, C., Grouven, U., Bender, R. and Skipka, G. 2012. A note on the graphical presentation of prediction intervals in random-effects meta-analyses. Syst. Rev. 1, 34. [abstract]

Gumedze FN, Jackson D. 2011. A random effects variance shift model for detecting and accommodating outliers in meta-analysis. BMC Med Res Methodol. Feb 16;11:19. [abstract]


Harbord RM, Deeks JJ, Egger M, Whiting P, Sterne JA. 2007. A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics 8:239-251. [abstract]

Harbord RM, Egger M, Sterne JA. 2006. A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints. Stat Med 25:3443-3457. [abstract]

Herbison P, Hay-Smith J, Gillespie WJ. 2006. Adjustment of meta-analyses on the basis of quality scores should be abandoned. J Clin Epidemiol 59:1249-1256. [abstract]

Herbison P, Hay-Smith J, Gillespie WJ. 2011. Meta-analyses of small numbers of trials often agree with longer-term results. J Clin Epidemiol 64(2):145-53.[abstract]

Herbison P, Hay-Smith J, Gillespie WJ. 2011. Different methods of allocation to groups in randomized trials are associated with different levels of bias. A meta-epidemiological study. J Clin Epidemiol 64(10):1070-5.[abstract]

Higgins JPT, Green S. 2008. Cochrane Handbook for Systematic Reviews of Interventions . The Cochrane Collaboration. [link]

Higgins JPT, Whitehead A, Simmonds MC. 2011. Sequential methods for random-effects meta-analyses. Statistics in Medicine.30(9):903-21 [abstract]

Higgins JP, Thompson SG. 2002. Quantifying heterogeneity in a meta-analysis. Stat Med 21:1539-1558. [abstract]

Higgins JP, Thompson SG, Spiegelhalter DJ. 2009. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc 172:137-159. [abstract]

Higgins JP, White IR, Anzures-Cabrera J. 2008. Meta-analysis of skewed data: Combining results reported on log-transformed or raw scales. Stat Med 27:6072-6092. [abstract]

Higgins JP, White IR, Wood AM. 2008. Imputation methods for missing outcome data in meta-analysis of clinical trials. Clin Trials 5:225-239. [abstract]


Ioannidis JP, Patsopoulos NA, Evangelou E. 2007. Uncertainty in heterogeneity estimates in meta-analyses. BMJ 335:914-916. [abstract]


Jackson D, Law M, Stijnen T, Viechtbauer W, White IR. A comparison of 7 random-effects models for meta-analyses that estimate the summary odds ratio. Stat Med 2018 [Epub ahead of print] [abstract]

Jackson D, Riley R, White IR. 2011. Multivariate meta-analysis: Potential and promise. Stat Med. Sep 10;30(20):2481-98 [abstract]

Jackson, Baker R, Bowden J. 2010. How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts? Journal of Statistical Planning and Inference, (140) 961-970.[abstract]

Jackson D, White IR, Thompson SG. 2010. Extending DerSimonian and Laird's methodology to perform multivariate random effects meta-analyses. Stat Med. May 30;29(12):1282-97. [abstract]

Jansen JP, Schmid CH and Salanti G. 2012. Directed acyclic graphs can help understand bias in indirect and mixed treatment comparisons. Journal of Clinical Epidemiology 65:798–807. [abstract]

Jones AP, Riley RD, Williamson PR, Whitehead A. 2009. Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials. Clin Trials 6:16-27. [abstract]


Kirkham JJ, Riley RD, Williamson PR. 2012. A multivariate meta-analysis approach for reducing the impact of outcome reporting bias in systematic reviews. Stat Med 31(20):2179-95. [abstract]

Kontopantelis E, Reeves D. 2009. MetaEasy: A Meta-Analysis Add-In for Microsoft Excel. Journal of Statistical Software 30(7). [abstract]

Kontopantelis E, Reeves D. 2010. metaan: Random-effects meta-analysis. The Stata Journal 10(3):395-407. [abstract]

Kontopantelis E, Reeves D. 2012. Performance of statistical methods for meta-analysis when true study effects are non-normally distributed: A simulation study. Stat Methods 21(4): 409-426. [abstract]

Kontopantelis E, Springate DA, Reeves D. 2013. A Re-Analysis of the Cochrane Library Data: The Dangers of Unobserved Heterogeneity in Meta-Analyses. PLoS ONE 8(7): e69930.  [abstract]

Krogsboll LT, Hrobjartsson A, Gotzsche PC. 2009. Spontaneous improvement in randomised clinical trials: meta-analysis of three-armed trials comparing no treatment, placebo and active intervention. BMC Med Res Methodol 9:1. [abstract]

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. [abstract]

Kulinskaya, E., Dollinger, M. and Bjørkestøl, K. (2011) On the moments of Cochran's Q statistic under the null hypothesis; with application to the meta-analysis of risk difference, Research Synthesis Methods, 2(4), 254-270. [abstract]

Kulinskaya E, Dollinger MB, Knight E, Gao H. 2004. A Welch-type test for homogeneity of contrasts under heteroscedasticity with application to meta-analysis. Stat Med 23:3655-3670. [abstract]

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.[abstract]

Kulinskaya, E., Morgenthaler, S. and Staudte, R. 2010. Combining the Evidence using Stable Weights. Research Synthesis Methods. 1: 284–296.[abstract]

Kulinskaya, E., Morgenthaler, S. and Staudte, R. 2010. Variance Stabilizing the Difference of two Binomial Proportions. The American Statistician, November 2010, 64(4), 350-356. [abstract]

Kulinskaya, E., Morgenthaler, S., Staudte R.G.  2011. Meta-analysis. In: Miodrag Lovric (ed.), International Encyclopedia of Statistical Science, Part 13, 811-815, Springer-Verlag Berlin Heidelberg.

Kulinskaya, E., Morgenthaler, S. and Staudte R.G. 2014. Combining Statistical Evidence. International Statistical Review 82 (2): 214-242. [abstract]

Kulinskaya, E. and Olkin,I. 2014. An overdispersion model in meta analysis. -Statistical Modelling: An International Journal 14 (1): 49-76, [abstract]

Kulinskaya E, Staudte RG. 2006. Interval estimates of weighted effect sizes in the one-way heteroscedastic ANOVA. Br J Math Stat Psychol 59:97-111. [abstract]

Kulinskaya E, Staudte RG. 2007. Confidence intervals for the standardized effect arising in the comparison of two normal populations. Stat Med 26:2853-2871. [abstract]

Kulinskaya, E. and Wood, J. 2014. Trial sequential methods for meta-analysis. Research Synthesis Methods 5 (3):212-220 [abstract]


Lange S, Freitag G. 2005. Choice of delta: requirements and reality--results of a systematic review. Biom J. 47(1): 12-27. [abstract]

Lau J, Ioannidis JPA and Schmid CH. Quantitative Synthesis in Systematic Overviews. Annals of Internal Medicine 127 (9): 820-826, 1997 [abstract] and in Systematic Reviews: Synthesis of Best Evidence for Health Care Decisions. Philadelphia: American College of Physicians, 91-101, 1998.

Lau J, Ioannidis JPA and Schmid CH. 1998. Summing Up Evidence: One Answer Is Not Always Enough. Lancet 351 (9096): 123-127. [abstract]

Lau J, Ioannidis JPA, Terrin N, Schmid CH and Olkin I. 2006. The case of the misleading funnel plot.  British Medical Journal 333: 597-600. [abstract]

Lau J, Schmid CH and Chalmers TC. 1995. Cumulative Meta-Analysis of Clinical Trials Builds Evidence for Exemplary Medical Care. Journal of Clinical Epidemiology 48 (1): 45-57. [abstract]

Li T, Saldanha IJ, Vedula SS, Yu T, Rosman L, Twose C, N Goodman S, Dickersin K. 2014. Learning by doing – teaching systematic review methods in 8 weeks. Research Synthesis Methods 5 (3): 254-263. [abstract]


Mengersen K, Gurevitch J and Schmid CH. Meta-analysis of primary data in Handbook of Meta-Analysis in Ecology and Evolution, eds. J Koricheva, J Gurevitch and K Mengersen.  Princeton, NJ: Princeton University Press, 2013, 300-312.

Mengersen K, Jennions MD and Schmid CH. Statistical models for the meta-analysis of non-independent data in Handbook of Meta-Analysis in Ecology and Evolution, eds. J Koricheva, J Gurevitch and K Mengersen. Princeton, NJ: Princeton University Press 2013, 255-283.

Mengersen K and Schmid CH. Maximum likelihood approaches to meta-analysis in Handbook of Meta-Analysis in Ecology and Evolution, eds. J Koricheva, J Gurevitch and K Mengersen. Princeton, NJ: Princeton University Press, 2013, 125-144.

Mengersen K, Schmid CH, Jennions MD and Gurevitch J. Statistical models and approaches to inference for meta-analysis in Handbook of Meta-Analysis in Ecology and Evolution, eds. J Koricheva, J Gurevitch and K Mengersen. Princeton, NJ: Princeton University Press, 2013, 89-107.

Mills EJ, Chan AW, Wu P, Vail A, Guyatt GH, Altman DG. 2009. Design, analysis,and presentation of crossover trials.  Trials 10:27. [abstract]

Moher D, Pham B, Jones A, Cook DJ, Jadad AR, Moher M, Tugwell P, Klassen TP. 1998. Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? Lancet 352:609-613. [abstract]

Moreno SG, Sutton AJ, Ades AE, Cooper NJ, Abrams KR. 2011. Adjusting for publication biases across similar interventions performed well when compared with gold standard data. Journal of Clinical Epidemiology 64 (11): 1230-41. [abstract]

Moreno SG, Sutton AJ, Ades AE, Stanley TD, Abrams KR, Peters JL, Cooper NJ. 2009. Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study. BMC Med Res Methodol 9:2. [abstract]

Murad MH, Montori VM, Walter SD, Guyatt GH. 2009. Estimating risk difference from relative association measures in meta-analysis can infrequently pose interpretational challenges. J Clin Epidemiol 62:865-867. [abstract]


Newcombe, R.G.and Bender, R. 2014. Implementing GRADE – calculating the risk difference from the baseline risk and the relative risk. Evid. Based Med. 19(1), 6-8. [abstract]

Nieminen P, Carpenter JR, Rücker G, Schumacher M 2006. The relationship between quality of research and citation frequency. BMC Medical Research Methodology. 6(42):1-8. [abstract]

Nieminen P, Rücker G, Miettunen J, Carpenter J, Schumacher M. 2007. Statistically significant papers in psychiatry were cited more often than others. J Clin Epidemiol 60:939-946. [abstract]

Nikolakopoulou A, Chaimani A, Veroniki AA, Vasiliadis HS, Schmid CH and Salanti G. 2014. Characteristics of networks of interventions: A description of a database of 186 published networks. PLOS ONE 9(1):e86754. [full text]

Nikolakopoulou A, Mavridis D, Egger M and Salanti G. 2016. Continuously updated network meta-analysis and statistical monitoring for timely decision-making. Statistical methods in Medical Research. [abstract] 

Novelli, N., Cooper, N.J., Sutton, A.J., Abrams, K.R. 2010. A Bayesian model selection framework for meta-analysis of diagnostic test accuracy data: Application to Ddimer for deep venous thrombosis. Research Synthesis Methodology 1:226-238 [abstract]

Novielli N, Cooper NJ, Sutton AJ, Abrams KR.  2010. How is evidence on test performance synthesised in economic decision models of diagnostic tests? A systematic appraisal of Health Technology Assessments in the UK since 1997. Value in Health 13(8): 952 -957. [abstract]

Nüesch E, Trelle S, Reichenbach S, Rutjes AW, Tschannen B, Altman DG, Egger M, Jüni P. 2010. Small study effects in meta-analyses of osteoarthritis trials: meta-epidemiological study. BMJ. Jul 16;341:c3515. [abstract]

Nüesch E, Trelle S, Reichenbach S, Rutjes AW, Burgi E, Scherer M, Altman DG, Juni P. 2009. The effects of excluding patients from the analysis in randomised controlled trials: meta-epidemiological study. BMJ 339:b3244. [abstract]


O’Rourke K. 2001. Meta-analysis: Conceptual issues of addressing apparent failure of individual study replication or "inexplicable" heterogeneity. Ahmed SE and Reid N, editors. Lecture notes in statistics: Empirical Bayes and likelihood inference. Springer

O’Rourke K. 2002. Meta-analytical themes in the history of statistics: 1700 to 1938, Pakistan Journal of Statistics. S. Ejaz Ahmed Special Issue 18(20):285-299.

O’Rourke K. 2007. An historical perspective on meta-analysis: dealing quantitatively with varying study results. J R Soc Med 100:07-09-03.1–4 [abstract]

O'Rourke K, Altman DG. 2005. Bayesian random effects meta-analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales. Stat Med 24:2733-2742. [abstract]

O' Rourke K, Shea B, Wells G. 2001. Chapter 10:MetaAnalysis in Clinical Trials. In S-PLUS in the Pharmaceutical Industry. Edited by Millard S, Krause A. Springer-Verlag. 


Parekh-Bhurke S, Kwok CS, Pang C, Hooper L, Loke YK, Ryder JJ, Sutton AJ, Hing CB, Harvey I, Song F. 2011. Uptake of methods to deal with publication bias in systematic reviews has increased over time, but there is still much scope for improvement. J Clin Epidemiol. Apr;64(4):349-57. [abstract]

Perera R, Glasziou P. 2007. A simple method to correct for the design effect in systematic reviews of trials using paired dichotomous data. J Clin Epidemiol 60:975-978. [abstract]

Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L. 2008. Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. J Clin Epidemiol 61:991-996. [abstract]

Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L. 2007. Performance of the trim and fill method in the presence of publication bias and between-study heterogeneity. Stat Med 26:4544-4562. [abstract].

Phillips R, Sutton AJ, Stewart L 2010. “Cross hairs” plots for diagnostic meta-analysis Research Synthesis Methods.1:1308-315. [abstract]


Riley RD, Abrams KR, Sutton AJ, Lambert PC, Thompson JR. 2007. Bivariate random-effects meta-analysis and the estimation of between-study correlation. BMC Med Res Methodol 7:3. [abstract]

Riley RD, Abrams KR, Lambert PC, Sutton AJ, Thompson JR. 2007. An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes. Stat Med 26:78-97. [abstract]

Riley RD, Gates S, Neilson J, Alfirevic Z. 2011. Statistical methods can be improved within Cochrane pregnancy and childbirth reviews. J Clin Epidemiol 64(6):608-18. Epub 2010 Dec 13. [abstract]

Riley RD, Higgins JP, Deeks JJ. 2011. The interpretation of random-effects meta-analysis. BMJ 342:d549.[abstract]

Riley RD, Lambert PC, Abo-Zaid G. 2010. Meta-analysis of individual participant data: conduct, rationale and reporting. BMJ 340:c221. [abstract]

Riley RD, Lambert PC, Staessen JA, Wang J, Gueyffier F, Thijs L, Boutitie F. 2008. Meta-analysis of continuous outcomes combining individual patient data and aggregate data. Stat Med 27:1870-1893. [abstract]

Riley RD, Simmonds MC, Look MP. 2007. Evidence synthesis combining individual patient data and aggregate data: a systematic review identified current practice and possible methods. J Clin Epidemiol 60:  431-439.[abstract]

Riley RD, Sutton AJ, Abrams KR, Lambert PC. 2004. Sensitivity analyses allowed more appropriate and reliable meta-analysis conclusions for multiple outcomes when missing data was present. J Clin Epidemiol 57:911-924.  [abstract]

Riley RD, Thompson JR, Abrams KR. 2008. An alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown. Biostatistics 9:172-186. [abstract]

Riley RD, Steyerberg EW. 2010. Meta-analysis of a binary outcome using individual participant data and aggregate data. J Research Synthesis Methods 1: 2-9.[abstract]

Rücker G 2012. Network meta-analysis, electrical networks and graph theory, Res Synth Methods, vol. 3, no. 312-324, 2012. [abstract]

Rücker G, Carpenter JR, Schwarzer G. 2011. Detecting and adjusting for small-study effects in meta-analysis.Biometrical Journal,53(2):351-368. [abstract]

Rücker G, Schwarzer G. 2014. Reduce dimension or reduce weights? Comparing two approaches to multi-armed studies in network meta-analysis.Statistics in Medicine, 10, 33(25):4353-69. [abstract]

Rücker G, Schwarzer G, Carpenter J. 2008. Arcsine test for publication bias in meta-analyses with binary outcomes. Stat Med 27:746-763. [abstract]

Rücker G, Schwarzer G, Carpenter J, Binder H, Schumacher M. 2010. Treatment effect estimates adjusted for small-study effects via a limit meta-analysis. Biostatistics 12(1):122-142. [abstract]

Rücker G, Schwarzer G, Carpenter JR, Schumacher M. 2008. Undue reliance on I(2) in assessing heterogeneity may mislead. BMC Med Res Methodol 8:79. [abstract]

Rücker G, Schwarzer G, Carpenter J, Olkin I. 2009. Why add anything to nothing? The arcsine difference as a measure of treatment effect in meta-analysis with zero cells. Stat Med 28:721-738. [abstract]

Rücker G, Schumacher M. 2008. Simpson's paradox visualized: The example of the Rosiglitazone meta-analysis", BMC Medical Research Methodology 8(1).  [abstract]

Rücker G, Schumacher M. 2010. Summary ROC curve based on the weighted Youden index for selecting an optimal cutpoint in meta-analysis of diagnostic accuracy. Statistics in Medicine 29:3069-3078. [abstract]

Rücker G, Cates CJ, Schwarzer G.  Methods for including information from multi-arm trials in pairwise meta-analysis. Res Synth Methods. 2017 Jul 31. doi: 10.1002/jrsm.1259 [abstract]

Rücker G, Schwarzer G.  Automated drawing of network plots in network meta-analysis.  Res Synth Methods. 2016 Mar;7(1):94-107 [abstract]

Rücker G, Schwarzer G.  Ranking treatments in frequentist network meta-analysis works without resampling methods.  BMC Med Res Methodol 2015; 15:58 [abstract]


Salanti G, Higgins JP, Ades AE, Ioannidis JP. 2008. Evaluation of networks of randomized trials. Stat Methods Med Res 17:279-301. [abstract]

Salanti G, Higgins JP, White IR. 2006. Bayesian synthesis of epidemiological evidence with different combinations of exposure groups: application to a genegene- environment interaction. Stat Med 25:4147-4163.[abstract]

Salanti G, Ioannidis JP. 2009. Synthesis of observational studies should consider credibility ceilings. J Clin Epidemiol 62:115-122. [abstract]

Salanti G, Marinho V, Higgins JP. 2009. A case study of multiple-treatments meta-analysis demonstrates that covariates should be considered. J Clin Epidemiol. 62(8):857-64.[abstract]

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