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Corrected profile likelihood confidence interval for binomial paired incomplete data
Author(s) -
Pradhan Vivek,
Me Sandeep,
Das Ujjwal
Publication year - 2013
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1551
Subject(s) - confidence interval , binomial proportion confidence interval , coverage probability , statistics , binomial (polynomial) , binary data , credible interval , interval estimation , mathematics , interval (graph theory) , binomial distribution , robust confidence intervals , tolerance interval , nominal level , point estimation , binary number , confidence distribution , computer science , negative binomial distribution , poisson distribution , arithmetic , combinatorics
Clinical trials often use paired binomial data as their clinical endpoint. The confidence interval is frequently used to estimate the treatment performance. Tang et al. (2009) have proposed exact and approximate unconditional methods for constructing a confidence interval in the presence of incomplete paired binary data. The approach proposed by Tang et al. can be overly conservative with large expected confidence interval width (ECIW) in some situations. We propose a profile likelihood‐based method with a Jeffreys' prior correction to construct the confidence interval. This approach generates confidence interval with a much better coverage probability and shorter ECIWs. The performances of the method along with the corrections are demonstrated through extensive simulation. Finally, three real world data sets are analyzed by all the methods. Statistical Analysis System (SAS) codes to execute the profile likelihood‐based methods are also presented. Copyright © 2013 John Wiley & Sons, Ltd.