title: Corrected profile likelihood confidence interval for binomial paired incomplete data.
abstract: 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.
authors.affiliation: Boston Scientific, 100 Boston Scientific Way, Marlborough, MA 01752, USA.
authors.forename: Vivek
authors.initials: V
authors.lastname: Pradhan
authors.forename: Sandeep
authors.initials: S
authors.lastname: Menon
authors.forename: Ujjwal
authors.initials: U
authors.lastname: Das
mesh_terms: D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D033183:Interdisciplinary Communication; D016013:Likelihood Functions; D008318:Malpractice; D019300:Medical Errors / Q000517:prevention & control; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data; D062209:Patient Handoff; D012107:Research Design / Q000706:statistics & numerical data*; D018401:Sample Size; D016896:Treatment Outcome