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Exact Unconditional Inference for Risk Ratio in a Correlated 2 × 2 Table with Structural Zero
Author(s) -
Tang NianSheng,
Tang ManLai
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00972.x
Subject(s) - confidence interval , exact statistics , sample size determination , mathematics , statistical hypothesis testing , statistic , test statistic , statistics , inference , coverage probability , sample space , sample (material) , computer science , artificial intelligence , chemistry , chromatography
Summary. In this article, we consider small‐sample statistical inference for rate ratio ( RR ) in a correlated 2 × 2 table with a structural zero in one of the off‐diagonal cells. Existing Wald's test statistic and logarithmic transformation test statistic will be adopted for this purpose. Hypothesis testing and confidence interval construction based on large‐sample theory will be reviewed first. We then propose reliable small‐sample exact unconditional procedures for hypothesis testing and confidence interval construction. We present empirical results to evince the better confidence interval performance of our proposed exact unconditional procedures over the traditional large‐sample procedures in small‐sample designs. Unlike the findings given in Lui (1998, Biometrics 54 , 706–711), our empirical studies show that the existing asymptotic procedures may not attain a prespecified confidence level even in moderate sample‐size designs (e.g., n = 50). Our exact unconditional procedures on the other hand do not suffer from this problem. Hence, the asymptotic procedures should be applied with caution. We propose two approximate unconditional confidence interval construction methods that outperform the existing asymptotic ones in terms of coverage probability and expected interval width. Also, we empirically demonstrate that the approximate unconditional tests are more powerful than their associated exact unconditional tests. A real data set from a two‐step tuberculosis testing study is used to illustrate the methodologies.