Meta-Analysis of Clinical Trials

  • Keith O’Rourke
  • Beverley Shea
  • George A. Wells


Meta-analysis (MA) may loosely be defined as the explicit analysis and summary of multiple investigations or studies. Being such, it offers the potential to reach the goal of keeping up to date in the drug research and development process without sacrificing thoroughness. Theoretically, meta-analysis can effectively summarize the accumulated research on a topic, promote new questions on the matter, as well as channel the stream of clinical research toward relevant horizons. Consequently, meta-analyses can be very important in the drug research and development process—from initial consideration of a new drug through the conduct of Phase III studies to possible removal of a drug.


Markov Chain Monte Carlo Model Check Individual Study Random Effect Model Total Parenteral Nutrition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anonymous (1997). Meta-analyses under scrutiny. Lancet 350, 675.Google Scholar
  2. Bailey, K.R. (1987). Inter-study differences: How should they influence the interpretation and analysis of results? Statistics In Medicine 6, 351–358.CrossRefGoogle Scholar
  3. Begg, C., Cho, M., Eastwood, S., Horton, R., Moher, D., Olkin, I., Pitkin, R., Rennie, D., Schultz, K.F., Simel, D., Stroup, D.F. (1996). Improving the quality of reporting of randomized controlled trials: The consort statement. Journal of the American Medical Association 276 (8), 637–639.CrossRefGoogle Scholar
  4. Berkey, C.S., Hoaglin, D.C., Mosteller, F., Colditz, G.A. (1995). A random-effects regression model for meta-analysis. Statistics in Medicine 14, 395–411.CrossRefGoogle Scholar
  5. Bucher, H.C., Guyatt, G.H., Griffith, L.E., Walter, S.D. (1997). The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. Journal of Clinical Epidemiology 50 (6), 683–691.CrossRefGoogle Scholar
  6. Chatfield, C. (1995). Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society Association Series A 158, 418–466.Google Scholar
  7. Cochran, W.G. (1937). Problems arising in the analysis of a series of similar experiments. Journal of the Royal Statistical Society 4 (1), 102–118.Google Scholar
  8. Cochran, W.G. (1980). Summarizing the results of a series of experiments. Proceedings of the 25th Conference on the Design of Experiments in Army Research Development and Testing, Durham, NC. ARO Report. U.S. Army Research Office.Google Scholar
  9. Cook D.J., Sackett, D.L., Sptizer, W.O. (1995). Methodologic guidelines for systematic reviews of randomized controlled trials in health care from the Potsdam consultation of meta-analysis. Journal of Clinical Epidemiology 48, 167–171.CrossRefGoogle Scholar
  10. Counsell C. (1996). Formulating the questions and locating the studies for in- clusion in systematic reviews. Annals of Internal Medicine 127, 380–387.Google Scholar
  11. Cox, D.R. (1982). Combination of data. In: Kotz, S. and Johnson, N.L., eds., Encyclopedia of Statistical Science, Vol. 2. Wiley, New York.Google Scholar
  12. Cox, D.R., Snell, E.J. (1989). The Analysis of Binary Data. Second edition. Chapman Hall, London.Google Scholar
  13. DerSimonian, R., Laird, N.M. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials 7, 177–188.CrossRefGoogle Scholar
  14. Detsky, A.S., Naylor, C.D., O’Rourke, K., McGeer, A.J., L’Abbe, K.A. (1992). Incorporating variations in the quality of individual randomized trials into meta-analysis. Journal of Clinical Epidemiology 45 (3), 255–265.CrossRefGoogle Scholar
  15. Dickersin K., Scherer R., Lefebvre, C. (1994). Identifying relevant studies for systematic reviews. British Medical Journal 309 (6964), 1286–1291.CrossRefGoogle Scholar
  16. Droitcour, J., Silberman, G., Chelimsky, E. (1993). Cross-design synthesis—A new form of meta-analysis for combining results from randomized clinical trials and medical-practice databases. International Journal of Technology Assessment in Health Care 9 (3), 440–449.CrossRefGoogle Scholar
  17. Eddy, D.M., Hasselblad, V., Shachter, R. (1990). An introduction to a Bayesian method for meta-analysis: The confidence profile method. Medical Decision Making 10, 15–23.CrossRefGoogle Scholar
  18. Efron, B. (1996). Empirical Bayes methods for combining likelihoods. Journal of the American Statistical Association 91 (434), 538–565.MathSciNetzbMATHCrossRefGoogle Scholar
  19. Egger, M., Smith, G.D., O’Rourke, K. (2000). Rationale, potentials and promise. In: Egger, M., Smith, D.G., Altman, eds., Systematic Reviews in Health Care: Meta-Analysis in Context. BMJ Books, London.Google Scholar
  20. Egger, M., Schneider, M., Smith, G.D. (1998). Spurious precision? Meta- analysis of observational studies. British Medical Journal 316, 140–144.CrossRefGoogle Scholar
  21. Fisher, R.A. (1935). The Design of Experiments. Oliver and Boyd, Edinburgh, Scotland.Google Scholar
  22. L’Abbe, K.A., Detsky, A.S., O’Rourke, K. (1987). Meta-analysis in clinical research. Annals of Internal Medicine 107, 224–233.Google Scholar
  23. Lee, Y., Nelder, J.A. (1996). Hierarchical generalized linear models. Journal of the Royal Statistical Society 58 (4), 619–678.MathSciNetzbMATHGoogle Scholar
  24. Longford, N.T., Nelder, J.A. (1999). Statistics versus statistical science in the regulatory process. Statistics In Medicine, 18 (17–18), 2311–2320.CrossRefGoogle Scholar
  25. McCullagh, P., Nelder, J.A. (1989). Generalized Linear Models, 2“1 ed. Chapman Hall, New York.Google Scholar
  26. McGeer, A.J., Detsky, A.S., O’Rourke K. (1990). Parenteral nutrition in cancer patients undergoing chemotherapy: A meta-analysis. Nutrition 8 (3), 233–240.Google Scholar
  27. Moher, D., Cook, D.J., Eastwood, S., Olkin, I., Rennie, D., Stroup, D.F. (1999). Improving the quality of reports of meta-analyses of randomised controlled trials: The QUOROM statement. Quality of Reporting of Meta-analyses. Lancet 354 (9193), 1896–1900.CrossRefGoogle Scholar
  28. Mosteller, F., Chalmers, T.C. (1992). Some progress and problems in meta-analysis of clinical trials. Statistical Science 7 (2), 227–236.MathSciNetzbMATHCrossRefGoogle Scholar
  29. Muirow, C.D., Oxman, A., eds. (1997). How to conduct a Cochrane systematic review. Cochrane Collaboration Handbook. The Cochrane Library. The Cochrane Collaboration, Issue 4. Update Software, Oxford.Google Scholar
  30. Normand, S.-L.T. (1995). Meta-analysis software: A comparative review. The American Statistician 49 (3), 298–309.MathSciNetGoogle Scholar
  31. Normand, S.-L.T. (1999). Tutorial in biostatistics meta-analysis: Formulating, evaluating, combining, and reporting. Statistics in Medicine 18, 321–359.CrossRefGoogle Scholar
  32. O’Rourke, K. (2001). Meta-analysis: Conceptual issues of addressing apparent failure of individual study replication or “inexplicable” heterogeneity. In: Ahmed, S.E. and Reid, N., eds., Lecture notes in statistics: Empirical Bayes and Likelihood Inference. Springer-Verlag, New York.Google Scholar
  33. O’Rourke, K., Detsky, A.S. (1989). Meta-analysis in medical research: Strong encouragement for higher quality in individual research efforts. Journal of Clinical Epidemiology 42 (10), 1021–1024.CrossRefGoogle Scholar
  34. Pearson, K. (1904). Report on certain enteric fever inoculation statistics. British Medical Journal 3, 1243–1246.Google Scholar
  35. Poole, C., Greenland, S. (1999). Random-effects meta-analyses are not al- ways conservative. American Journal of Epidemiology 150 (5), 469–475.CrossRefGoogle Scholar
  36. Richardson, W.S., Wilson, M.S., Mishikawa, J., Hayward, R.S.A. (1995). The well-built clinical question: A key to evidence-based decisions. American College of Physicians Journal Club 123(3), Al2—A13.Google Scholar
  37. Ripley, B. (1999). Modern data analysis in S-PLus. Proceedings of the 1999 International S-PLUS User Conference Workshop. MathSoft, Seattle, WA.Google Scholar
  38. Rosenbaum, P.R. (1999). Choice as an alternative to control in observational studies. Statistical Science 14 (3), 259–304.zbMATHCrossRefGoogle Scholar
  39. Rothman, K.J., Greenland, S. (1998). Modern Epidemiology. Lippincott—Raven, Philadelphia, PA.Google Scholar
  40. Rubin, D.B. (1992). Meta-analysis: Literature synthesis or effect-size surface estimation? Journal of Educational Statistics 17 (4), 363–374.CrossRefGoogle Scholar
  41. Senn, S.J. (1996). The AB/BA cross-over: How to perform the two-stage analysis if you can’t be persuaded that you shouldn’t. In: Hansen, B., De Ridder, M., eds. Liber Amicorum Roel van Strik. Erasmus University, Rotterdam, 93–100.Google Scholar
  42. Shea, B., Dube, C., Moher, D. (2000). Assessing the quality of reports of meta-analyses: a systematic review of scales and checklists. In: Egger, M., Smith, G.D., Altman, D., eds., Systematic Reviews in Health Care: Meta-Analysis in Context. BMJ Books, London.Google Scholar
  43. Smith, T.C., Spiegelhalter, D.J., Parmar, M.H.K. (1996). Bayesian meta-analysis of randomized trials using graphical models and BUGS. In: Berry, D.A., Stangl, D.K., Bayesian Biostatistics. Marcel Dekker, New York.Google Scholar
  44. Stigler, S.M. (1986). The History of Statistics: The Measurement of Uncertainty before 1900. Belknap Press of Harvard University Press, Cambridge, MA.zbMATHGoogle Scholar
  45. Venables, W.N., Ripley, B.D. (1994). Modern Applied Statistics with S-PLUS. Springer-Verlag, New York.zbMATHGoogle Scholar
  46. Yates, F., Cochran, W.G. (1938). The analysis of groups of experiments. Journal of Agricultural Science 28, 556–580.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Keith O’Rourke
    • 1
  • Beverley Shea
    • 1
  • George A. Wells
    • 2
  1. 1.Ottawa HospitalUniversity of OttawaOttawaCanada
  2. 2.University of OttawaOttawaCanada

Personalised recommendations