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The Effect of Clustering on the Analysis of Sets of 2 × 2 Contingency Tables

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Part of the book series: The University of Western Ontario Series in Philosophy of Science ((WONS,volume 38))

Abstract

The usual methods of analyzing data in sets of 2 × 2 contingency tables are invalid when observations are correlated. This happens when, for example, families rather than individuals are assigned to treatments. This paper compares two models of clustering in dichotomous data: the beta-binomial model and a model developed by Cohen and Altham. The beta-binomial distribution is then used to model responses in K 2 × 2 contingency tables. This leads to an expression for the variance of the maximum likelihood estimator of the common odds ratio, which is compared to the usual MLE of the odds ratio. The performance of the Mantel-Haenszel estimator of the odds ratio under cluster sampling is assessed using a simulation study.

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References

  • Altham, P. M. E. (1976), “Discrete variable analysis for individuals grouped into families.” Biometrika 63, 263–269.

    Article  MathSciNet  MATH  Google Scholar 

  • Bradley, R. A., and J. J. Gart (1962), “The asymptotic properties of ML estimators when sampling from associated populations.” Biometrika 49, 205–214.

    MathSciNet  MATH  Google Scholar 

  • Brier, S. (1980), “ Analysis of contingency tables under cluster sampling.” Biometrika 67, 591–596.

    Article  MathSciNet  MATH  Google Scholar 

  • Brillinger, D. R. (1975), “Statistical inference for stationary point processes.” In Stochastic Processes and Related Topics, ed. M. L. Puri. New York: Academic Press.

    Google Scholar 

  • Cochran, W. G. (1980), Sampling Techniques. New York: Wiley and Sons.

    Google Scholar 

  • Cohen, J. E. (1976), “The distribution of the chi-squared statistic under clustered sampling from contingency tables.” Journal of the American Statistical Association 71, 665–670.

    Article  MathSciNet  MATH  Google Scholar 

  • Donald, A. (1984), “The analysis of clustered data in sets of 2 × 2 contingency tables.” Ph.D. Thesis, The University of Western Ontario.

    Google Scholar 

  • Donald, A., and A. Donner (1985), “Effects of clustering on the analysis of sets of 2 × 2 contingency tables: maximum likelihood theory and adjustments to the Mantel-Haenszel procedures.” In preparation.

    Google Scholar 

  • Gart, J. J. (1962), “On the combination of relative risks.” Biometrics 18, 601–610.

    Article  MATH  Google Scholar 

  • Griffiths, D. A. (1973), “Maximum likelihood estimation for the beta-binomial distribution and an application to the household distribution of the total number of cases of a disease.” Biometrics 29, 637–648.

    Article  Google Scholar 

  • Hauck, W. W. (1981), “The large-sample variance of the Mantel-Haenszel estimator of a common odds ratio.” Biometrics 35, 817–819.

    Article  MathSciNet  Google Scholar 

  • Mantel, N., and W. Haenszel (1959), “Statistical aspects of the analysis of data from retrospective studies of disease.” Journal of the National Cancer Institute 22, 719–748.

    Google Scholar 

  • Mossimann, J. E. (1962), “On the compound multinomial distribution, the multivariate beta-distribution and correlation among proportions.” Biometrika, 49, 65–82.

    MathSciNet  Google Scholar 

  • Paul, S. R., and R. L. Plackett (1978), “Inference sensitivity for Poisson mixtures.” Biometrika 65, 591–602.

    Article  MathSciNet  MATH  Google Scholar 

  • Plackett, R. L., and S. R. Paul (1978), “Dirichlet models for square contingency tables.” Communications in Statistics A, Theory and Methods 10, 939–952.

    Article  Google Scholar 

  • Skellam, J. G. (1948), “A probability distribution derived from the binomial distribution regarding the probability of success as variable between sets of trials.” Journal of the Royal Statistical Society, Series B 10, 257–261.

    MathSciNet  Google Scholar 

  • Snedecor, G. W., and W. G. Cochran (1980), Statistical Methods, Seventh Edition. Ames, Iowa: Iowa State University Press.

    Google Scholar 

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© 1987 D. Reidel Publishing Company, Dordrecht, Holland

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Donald, A., Donner, A. (1987). The Effect of Clustering on the Analysis of Sets of 2 × 2 Contingency Tables. In: MacNeill, I.B., Umphrey, G.J., Donner, A., Jandhyala, V.K. (eds) Biostatistics. The University of Western Ontario Series in Philosophy of Science, vol 38. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-4794-8_8

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  • DOI: https://doi.org/10.1007/978-94-009-4794-8_8

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-8626-4

  • Online ISBN: 978-94-009-4794-8

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