An Extended ANOVA F-Test with Applications to the Heterogeneity Problem in Meta-Analysis
The classical F‐test in the one‐way random effects ANOVA model is extended to solve the long outstanding problem of testing the between‐group variance on values also different from zero. This is done first for homoscedastic and heteroscedastic cases in not necessarily balanced models and secondly fo...
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Published in | Biometrical journal Vol. 43; no. 2; pp. 135 - 146 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Berlin
WILEY-VCH Verlag Berlin GmbH
01.05.2001
WILEY‐VCH Verlag Berlin GmbH Wiley-VCH |
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Abstract | The classical F‐test in the one‐way random effects ANOVA model is extended to solve the long outstanding problem of testing the between‐group variance on values also different from zero. This is done first for homoscedastic and heteroscedastic cases in not necessarily balanced models and secondly for balanced homoscedastic models. By simulation, the tests are shown to attain acceptable significance levels and high power even in data that do not follow the usual ANOVA model. An important application of the tests is given by the heterogeneity questions concerning the treatment effects across studies in meta‐analysis. |
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AbstractList | The classical F‐test in the one‐way random effects ANOVA model is extended to solve the long outstanding problem of testing the between‐group variance on values also different from zero. This is done first for homoscedastic and heteroscedastic cases in not necessarily balanced models and secondly for balanced homoscedastic models. By simulation, the tests are shown to attain acceptable significance levels and high power even in data that do not follow the usual ANOVA model. An important application of the tests is given by the heterogeneity questions concerning the treatment effects across studies in meta‐analysis. |
Author | Makambi, Kepher Henry Hartung, Joachim Argaç, Dog̃an |
Author_xml | – sequence: 1 givenname: Joachim surname: Hartung fullname: Hartung, Joachim organization: University of Dortmund, Dortmund, Germany – sequence: 2 givenname: Kepher Henry surname: Makambi fullname: Makambi, Kepher Henry organization: Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya – sequence: 3 givenname: Dog̃an surname: Argaç fullname: Argaç, Dog̃an organization: University of Dortmund, Dortmund, Germany |
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Keywords | F test Data analysis Covariance analysis Meta analysis Risk Statistical estimation Multivariate analysis Variance analysis Heteroscedasticity Hypothesis test Heterogeneity One way analysis of variance Random effect Linear estimation Sampling |
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References | Biggerstaff, B.J. and Tweedie, R.L., 1997: Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Statistics in Medicine 16, 753-768. Patnaik, P.B., 1949: The non-central χ2- and F-distributions and their applications. Biometrika 36, 202-232. Hartung, J., 1999: An alternative method for meta-analysis. Presented at the 45th Biometrical Colloquium of the German Region of the International Biometrical Society, Dortmund, 1999; in: Biometrical Journal 41, 901-916. Hardy, R.H. and Thompson, S.G., 1996: A likelihood approach to meta-analysis with random effects. Statistics in Medicine 15, 619-629. Lipsitz, S.R., Dear, K.B.G., Laird, N.M., and Molenberghs, G., 1998: Test for homogeneity of the risk difference when data are sparse. Biometrics 54, 148-160. Whitehead, A. and Whitehead, J., 1991: A general parametric approach to the meta-analysis of randomized clinical trials. Statistics in Medicine 10, 1665-1677. Herbach, L.H., 1959: Properties of model II-type analysis of variance tests, A: optimum nature of the F-test for model II in the balanced case. Ann. Math. Statist. 30, 939-959. DerSimonian, R. and Laird, N., 1986: Meta-analysis in clinical trials. Controlled clinical trials 7, 177-188. Hartung, J. and Voet, B., 1986: Best invariant unbiased estimators for the mean squared error of variance component estimators. J. Amer. Statist. Assoc. 81, 689-691. Normand, S.T., 1999: Meta-analysis: formulating, evaluating, combining, and reporting. Statistics in Medicine 18, 321-359. 1959; 30 1949; 36 1997; 16 1986; 81 1999; 41 1986; 7 1998; 54 1991; 10 1996; 15 1999; 18 Hartung (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB5) 1986; 81 Biggerstaff (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB1) 1997; 16 DerSimonian (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB2) 1986; 7 Hardy (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB3) 1996; 15 Lipsitz (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB7) 1998; 54 Herbach (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB6) 1959; 30 Whitehead (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB10) 1991; 10 Patnaik (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB9) 1949; 36 Hartung (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB4) 1999; 41 Normand (10.1002/1521-4036(200105)43:2<135::AID-BIMJ135>3.0.CO;2-H-BIB8) 1999; 18 |
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Snippet | The classical F‐test in the one‐way random effects ANOVA model is extended to solve the long outstanding problem of testing the between‐group variance on... The classical F-test in the one-way random effects ANOVA model is extended to solve the long outstanding problem of testing the between-group variance on... |
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SubjectTerms | ANOVA F-test Attained power Attained significance level Between-group variance Exact sciences and technology Linear inference, regression Mathematics Meta-analysis Multivariate analysis Probability and statistics Random effects model Risk difference Sciences and techniques of general use Statistics |
Title | An Extended ANOVA F-Test with Applications to the Heterogeneity Problem in Meta-Analysis |
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