Testing and Sample Size for Polygonal One-Sided Hypotheses on Bivariate Binary Outcomes

Ziyue Liu, Menggang Yu, Yan Tong

Research output: Contribution to journalArticle

Abstract

In this article, we consider hypothesis testing and computationally feasible sample size determination for bivariate binary outcomes. The hypotheses are formulated as one-sided polygons, which allow flexible trade-offs between the two outcomes. Parameters are estimated by maximizing the likelihood. Hypothesis testing for each linear constraint is performed by the Wald, score, likelihood ratio, and exact tests. The overall hypothesis is then tested using either the union-intersection or intersection-union method. We propose methods to calculate both exact power functions and asymptotic power functions. Finite sample behaviors are evaluated by numerical examples. A data example is used for illustration.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalStatistics in Biopharmaceutical Research
Volume5
Issue number1
DOIs
StatePublished - 2013

Fingerprint

Binary Outcomes
Power Function
Hypothesis Testing
Sample Size
Union
Intersection
Sample Size Determination
Asymptotic Power
Exact Test
Testing
Likelihood Ratio Test
Linear Constraints
Polygon
Likelihood
Trade-offs
Calculate
Numerical Examples

Keywords

  • Bivariate binary data
  • Hypothesis testing
  • Polygonal hypothesis
  • Sample size determination

ASJC Scopus subject areas

  • Pharmaceutical Science
  • Statistics and Probability

Cite this

Testing and Sample Size for Polygonal One-Sided Hypotheses on Bivariate Binary Outcomes. / Liu, Ziyue; Yu, Menggang; Tong, Yan.

In: Statistics in Biopharmaceutical Research, Vol. 5, No. 1, 2013, p. 1-17.

Research output: Contribution to journalArticle

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