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Many to one comparisons in a longitudinal binary data setup

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Abstract

Motivated by an example we discuss the problem of comparison to control in a scenario where we have repeated binary data from a large number independent individuals under each comparison group. We use the generalized quasi-likelihood (GQL) estimators for the comparison group effects including the effect of the control group to define a single step and step-down method for performing many-to-one comparisons. We discuss the asymptotic properties of our method and provide ways of computing the critical values. We follow up with simulations for Type I error and marginal power and show that our proposed method performs well in practice.

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Acknowledgements

The first author wishes to acknowledge Washington Tree Fruit Research Commission and Grant WTFRC AP-09-908 “Modeling Washington Apple bloom phenology and fruit growth” for providing the problem and the data for this paper. The authors would also like to thank the Associate Editor and two referees for their helpful comments.

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Authors and Affiliations

  1. Department of Statistics, Washington State University, Pullman, WA, 99164, USA

    Nairanjana Dasgupta & Limin Yang

  2. Department of Mathematics and Statistics, Memorial University of NewFoundland, St. John’s, Canada, A1C 5S7

    Brajendra Sutradhar

Authors
  1. Nairanjana Dasgupta
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  2. Limin Yang
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  3. Brajendra Sutradhar
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Correspondence to Nairanjana Dasgupta.

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Dasgupta, N., Yang, L. & Sutradhar, B. Many to one comparisons in a longitudinal binary data setup. Sankhya B 74, 268–285 (2012). https://doi.org/10.1007/s13571-012-0049-9

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  • DOI: https://doi.org/10.1007/s13571-012-0049-9

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AMS (2000) subject classification

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