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The Generalized ANOVA: A Classic Song Sung with Modern Lyrics

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Statistical Modeling for Biological Systems
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Abstract

The widely used analysis of variance (ANOVA) suffers from a series of flaws that not only raise questions about conclusions drawn from its use, but also undercut its many potential applications to modern clinical and observational research. In this paper, we propose a generalized ANOVA model to address the limitations of this popular approach so that it can be applied to many immediate as well as potential applications ranging from an age-old technical issue in applying ANOVA to cutting-edge methodological challenges. By integrating the classic theory of U-statistics, we develop distribution-free inference for this new class of models to address missing data for longitudinal clinical trials and cohort studies.

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References

  1. Clarke, G. N. (1995). Improving the transition from basic efficacy research to effectiveness studies: Methodological issues and procedures. Journal of Consulting and Clinical Psychology, 63, 718–725.

    Article  Google Scholar 

  2. Duran, B. S. (1976). A survey of nonparametric tests for scale. Communications in Statistics - Theory and Methods, 5, 1287–1312.

    Article  MathSciNet  Google Scholar 

  3. Hoeffding, W. (1948). A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics, 19, 293–325.

    Article  MathSciNet  Google Scholar 

  4. Hogarty, G. E., Schooler, N. R., & Baker, R. W. (1997). Efficacy versus effectiveness. Psychiatric Services, 48, 1107.

    Article  Google Scholar 

  5. Kowalski, J., & Tu, X. M. (2007). Modern applied U-statistics. New York: Wiley.

    Book  Google Scholar 

  6. Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47, 583–621.

    Article  Google Scholar 

  7. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.

    MATH  Google Scholar 

  8. Lu, N., Tang, W., He, H., Yu, Q., Crits-Christoph, P., Hui, Z., et al. (2009). On the impact of parametric assumptions and robust alternatives for longitudinal data analysis. Biometrical Journal, 51, 627–643.

    Article  MathSciNet  Google Scholar 

  9. Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18, 50–60.

    Article  MathSciNet  Google Scholar 

  10. Randles, R. H., & Wolfe, D. A. (1979). Introduction to the theory of nonparametric statistics. New York: Wiley.

    MATH  Google Scholar 

  11. Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90, 106–121.

    Article  MathSciNet  Google Scholar 

  12. Serfling, R. J. (1980). Approximation theorems of mathematical statistics. New York: Wiley.

    Book  Google Scholar 

  13. Taube, C. A., Mechanic, D., & Hohmann, A. A. (1989). The future of mental health services research. Washington: U.S. Department of Health and Human Services.

    Google Scholar 

  14. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics, 1, 80–83.

    Article  MathSciNet  Google Scholar 

  15. Zhang, H., & Tu, X. M. (2011). Generalized ANOVA for concurrently modeling mean and variance within a longitudinal data setting. Technical Report, Department of Biostatistics and Computational Biology, University of Rochester.

    Google Scholar 

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Acknowledgements

The authors sincerely thank Dr. W. Jack Hall and Ms. Cheryl Bliss-Clark at the University of Rochester for their help to improve the presentation of the manuscript. This paper was also supported by the ASA Best Student Paper Award and ENAR Distinguished Student Paper Award to be presented at the 2009 JSM in Washington and 2010 ENAR Spring Meeting in New Orleans, respectively.

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Correspondence to Hui Zhang .

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Zhang, H., Tu, X. (2020). The Generalized ANOVA: A Classic Song Sung with Modern Lyrics. In: Almudevar, A., Oakes, D., Hall, J. (eds) Statistical Modeling for Biological Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-34675-1_15

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