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A Comparison of Some Approximate Confidence Intervals for a Single Proportion for Clustered Binary Outcome Data

Author

Listed:
  • Saha Krishna K.
  • Miller Daniel

    (Department of Mathematical Sciences, Central Connecticut State University, 1615 Stanley Street, New Britain, CT 06050, USA)

  • Wang Suojin

    (Department of Statistics, Texas A&M University, College Station, TX 77843, USA)

Abstract

Interval estimation of the proportion parameter in the analysis of binary outcome data arising in cluster studies is often an important problem in many biomedical applications. In this paper, we propose two approaches based on the profile likelihood and Wilson score. We compare them with two existing methods recommended for complex survey data and some other methods that are simple extensions of well-known methods such as the likelihood, the generalized estimating equation of Zeger and Liang and the ratio estimator approach of Rao and Scott. An extensive simulation study is conducted for a variety of parameter combinations for the purposes of evaluating and comparing the performance of these methods in terms of coverage and expected lengths. Applications to biomedical data are used to illustrate the proposed methods.

Suggested Citation

  • Saha Krishna K. & Miller Daniel & Wang Suojin, 2016. "A Comparison of Some Approximate Confidence Intervals for a Single Proportion for Clustered Binary Outcome Data," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-18, November.
    • Handle: RePEc:bpj:ijbist:v:12:y:2016:i:2:p:18:n:2
    DOI: 10.1515/ijb-2015-0024
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