Meta-Analysis in Clinical Risk Assessment
III
Judith D. Goldberg,
Heather N. Watson,
Herman P. Friedman,
Judith D. Goldberg
New York University School of Medicine, New York, NY, USA
Search for more papers by this authorHeather N. Watson
New York University School of Medicine, New York, NY, USA
Search for more papers by this authorHerman P. Friedman
Statistical Science and Technology Associates, Inc., New York, NY, USA
Search for more papers by this authorJudith D. Goldberg,
Heather N. Watson,
Herman P. Friedman,
Judith D. Goldberg
New York University School of Medicine, New York, NY, USA
Search for more papers by this authorHeather N. Watson
New York University School of Medicine, New York, NY, USA
Search for more papers by this authorHerman P. Friedman
Statistical Science and Technology Associates, Inc., New York, NY, USA
Search for more papers by this authorFirst published: 15 September 2008
Abstract
Meta-analysis is a statistical approach that provides a logical structure and a quantitative methodology for the review, evaluation, and synthesis of information from independent studies. Combining the results of multiple independent studies in a well-conducted meta-analysis provides an objective summary assessment of the overall results of a series of independent studies. With the use of meta-analytic techniques, a series of clinical trials for example, can be summarized, and the overall treatment effect can be estimated with increased precision so that the risks of reaching incorrect conclusions from individual trials are reduced. In this article, we introduce the process of meta-analysis, issues in the conduct of a meta-analysis, and some statistical models that are commonly used to conduct a meta-analysis.
References
- 1 Friedman, H.P. & Goldberg, J.D. (1996). Meta-analysis: an introduction and point of view, Hepatology 23, 917–928.
- 2 K.W. Wachter & M.L. Straf (eds) (1990). The Future of Meta-Analysis, Russell Sage Foundation, New York.
- 3
Glass, G.V.
(1976).
Primary, secondary, and meta-analysis of research,
Educational Research
6,
3–8.
10.3102/0013189X005010003 Google Scholar
- 4 Cochran, W.G. (1954). The combination of estimates from different experiments, Biometrics 10, 101–129.
- 5 Goldberg, J.D. & Koury, K.J. (1992). Design and analysis of multicenter trials, in Statistical Methodology in the Pharmaceutical Sciences, D.A. Berry, ed, Marcel Dekker, New York, pp. 201–237.
- 6 Canner, P.L. (1987). An overview of six clinical trials of aspirin in coronary heart disease, Statistics in Medicine 6, 255–263.
- 7 National Research Council (1992). Combining Information: Statistical Issues and Opportunities for Research, National Academy Press, Washington, DC.
- 8 Aldous, P. (1994). A hearty endorsement for aspirin, Science 263, 24.
- 9 Higgins, J.P.T. & Thompson, S.G. (2002). Quantifying heterogeneity in a meta-analysis, Statistics in Medicine 21, 1539–1558.
- 10 DerSimonian, R. & Laird, N. (1986). Meta-analysis in clinical trials, Controlled Clinical Trials 7, 177–188.
- 11 Antman, E.M., Lau, J., Kupelnick, B., Mosteller, F. & Chalmers, T.C. (1992). A comparison of results of meta-analysis of randomized control trials and recommendations of clinical experts, Journal of the American Medical Association 268, 240–248.
- 12 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.
- 13 Bryk, A.S. & Raudenbush, S.W. (1992). Hierarchical Linear Models, Sage Publications, Newbury Park.
- 14
R Development Core Team
(2007).
R: A Language and Environment for Statistical Computing, Version 2.5.1,
R Foundation for Statistical Computing,
Vienna, at
http://www.R-project.org.
10.1111/j.1462-2920.2006.01017.x Google Scholar
- 15
Carlin, B.P. &
Louis, T.A.
(2000).
Bayes and Empirical Bayes Methods for Data Analysis,
2nd Edition,
Chapman & Hall/CRC Press,
Boca Raton.
10.1201/9781420057669 Google Scholar
- 16 Parmigiani, G. (2002). Meta-analysis, in Modeling in Medical Decision Making: A Bayesian Approach, G. Parmigiani, ed, John Wiley & Sons, West Sussex, pp. 123–163.
- 17 Spiegelhalter, D.J., Thomas, A. & Best, N.G. (1999). WinBUGS with DoodleBUGS Version 1.4.2 User Manual, Imperial College and Medical Research Council, at http://www.mrc-bsu.cam.ac.uk/bugs.
- 18 Lunn, D.J., Thomas, A., Best, N. & Spiegelhalter, D. (2000). WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility, Statistics and Computing 10, 325–337.
- 19 Warn, D.E., Thompson, S.G. & Spiegelhalter, D.J. (2002). Bayesian random effects meta-analysis of trials with binary outcomes: methods for absolute risk difference and relative risk scales, Statistics in Medicine 21, 1601–1623.
- 20 Berry, D.A., Berry, S.M., McKellar, J. & Pearson, T.A. (2003). Comparison of the dose response relationships of 2 lipid-lowering agents: a Bayesian meta-analysis, American Heart Journal 145, 1036–1045.
- 21
Light, R. &
Pillemer, D.B.
(1984).
Summing Up,
Harvard University Press,
Cambridge.
10.4159/9780674040243 Google Scholar
- 22 Hedges, L.V. & Olkin, I. (1985). Statistical Methods for Meta-Analysis, Academic Press, Orlando.
- 23 Cooper, H. & Hedges, L.V. (1994). Potentials and limitations of research synthesis, in The Handbook of Research Synthesis, H. Cooper & L.V. Hedges, eds, Russell Sage Foundation, New York, Chapter 32.
- 24 Chalmers, T.C., Levin, H., Sacks, H.S., Reitman, D., Berrier, J. & Nagalingam, R. (1987). Meta-analysis of clinical trials as a scientific discipline. I: control of bias and comparison with large co-operative trials, Statistics in Medicine 6, 315–325.
- 25 Collins, R., Gray, R., Godwin, J. & Peto, R. (1987). Avoidance of large biases and large random errors in the assessment of moderate treatment effects: the need for systematic overviews, Statistics in Medicine 6, 245–250.
- 26 DeMets, D.L. (1987). Methods for combining randomized clinical trials: strength and limitations, Statistics in Medicine 6, 341–348.
- 27 Hennekens, C.H., Buring, J.E. & Hebert, P.R. (1987). Implications of overviews of randomized trials, Statistics in Medicine 6, 397–402.
- 28 Peto, R. (1987). Why do we need systemic overviews of randomized trials? Statistics in Medicine 6, 233–240.
- 29 Wittes, R.E. (1987). Problems in the medical interpretation of overviews, Statistics in Medicine 6, 269–276.
- 30 Yusuf, S. (1987). Obtaining medically meaningful answers from an overview of randomized clinical trials, Statistics in Medicine 6, 281–286.
- 31 Pocock, S. (1993). Guest editorial, Statistical Methods in Medical Research 2, 117–119.
- 32 Oakes, M. (1993). The logic and role of meta-analysis in clinical research, Statistical Methods in Medical Research 2, 147–160.
- 33 Fleiss, J.L. (1993). The statistical basis of meta-analysis, Statistical Methods in Medical Research 2, 121–145.
- 34 Thompson, S.G. (1993). Controversies in meta-analysis: the case of the trials of serum cholesterol reduction, Statistical Methods in Medical Research 2, 173–192.
- 35 Chalmers, T.C. & Lau, J. (1993). Meta-analytic stimulus for changes in clinical trials, Statistical Methods in Medical Research 2, 161–172.
- 36
Dear, K.B.G. &
Begg, C.B.
(1992).
An approach for assessing publication bias prior to performing a meta-analysis,
Statistical Science
7,
237–245.
10.1214/ss/1177011363 Google Scholar
- 37 Hedges, L.V. (1992). Modelling publication selection effects in meta-analysis, Statistical Science 7, 246–255.
- 38
Iyengar, S. &
Greenhouse, J.B.
(1988).
Selection models and the file drawer problem,
Statistical Science
3,
109–135.
10.1214/ss/1177013012 Google Scholar
- 39
Mosteller, F. &
Chalmers, T.C.
(1992).
Some progress and problems in meta-analysis of clinical trials,
Statistical Science
7,
227–236.
10.1214/ss/1177011362 Google Scholar
- 40
Olkin, I.
(1992).
Meta-analysis: methods for combining independent studies,
Statistical Science
7,
226.
10.1214/ss/1177011361 Google Scholar
- 41 Mantel, N. & Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies of disease, Journal of the National Cancer Institute 22, 719–748.
- 42 Whitehead, A. & Whitehead, J. (1991). A general parametric approach to the meta-analysis of randomized clinical trials, Statistics in Medicine 10, 1665–1677.
- 43 Goodman, S.N. (1989). Meta-analysis and evidence, Controlled Clinical Trials 10, 188–204.
- 44 Zucker, D. & Yusuf, S. (1989). The likelihood ratio versus the p-value in meta-analysis: where is the evidence? Comment on the paper by S. N. Goodman, Controlled Clinical Trials 10, 205–208.
- 45 Berlin, J.E., Laird, N.M., Sacks, H.S. & Chalmers, T.C. (1989). A comparison of statistical methods for combining event rates from clinical trials, Statistics in Medicine 8, 141–151.
- 46 Thompson, S.G. & Pocock, S.J. (1991). Can meta-analysis be trusted? Lancet 338, 1127–1130.
- 47 Boden, W.E. (1992). Meta-analysis in clinical trials reporting: has a tool become a weapon? The American Journal of Cardiology 69, 681–686.
- 48 Mann, C. (1990). Meta-analysis in the breech, Science 249, 476–480.
- 49 Rubin, D. (1990). A new perspective, in The Future of Meta-Analysis, K.W. Wachter & M.L. Straf, eds, Russell Sage Foundation, New York, pp. 55–165.
- 50 Mosteller, F. (1990). Summing up, in The Future of Meta-Analysis, K.W. Wachter & M.L. Straf, eds, Russell Sage Foundation, New York, pp. 185–190.
- 51 J.P.T. Higgins & S. Green (eds) (2005). Cochrane Handbook for Systematic Reviews of Interventions 4.2.5, The Cochrane Library, Issue 3, John Wiley & Sons Chichester.
- 52 Rosenthal, R. (1979). The “file drawer problem” and tolerance for mere results, Psychological Bulletin 86, 638–641.
- 53 Sacks, H.S., Berrier, J., Reitman, D., Ancona-Berk, V.A. & Chalmers, T.C. (1987). Meta-analyses of randomized controlled trials, The New England Journal of Medicine 316, 450–455.
- 54 Sacks, H.S., Berrier, J., Reitman, D., Pagano, D. & Chalmers, T.C. (1992). Meta-analyses of randomized control trials: an update of the quality and methodology, in Medical Uses of Statistics, 2nd Edition, J.C. Builar & F. Mosteller, eds, NEJM Books, Boston, pp. 427–442.
Encyclopedia of Quantitative Risk Analysis and Assessment
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