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Article: On Confidence Interval Construction for Establishing Equivalence of Two Binary-Outcome Treatments in Matched-Pair Studies in the Presence of Incomplete Data

TitleOn Confidence Interval Construction for Establishing Equivalence of Two Binary-Outcome Treatments in Matched-Pair Studies in the Presence of Incomplete Data
Authors
KeywordsAgresti-Coull Interval
Incomplete Data
Jeffreys Interval
Method Of Variance Estimations Recovery
Proportion Difference
Wilson Interval
Issue Date2011
Citation
Statistics In Biosciences, 2011, v. 3 n. 2, p. 223-249 How to Cite?
AbstractMatched-pair design is often adopted in equivalence or non-inferiority trials to increase the efficiency of binary-outcome treatment comparison. Briefly, subjects are required to participate in two binary-outcome treatments (e. g., old and new treatments via crossover design) under study. To establish the equivalence between the two treatments at the α significance level, a (1-α)100% confidence interval for the correlated proportion difference is constructed and determined if it is entirely lying in the interval (-δ 0,δ 0) for some clinically acceptable threshold δ 0 (e. g., 0.05). Nonetheless, some subjects may not be able to go through both treatments in practice and incomplete data thus arise. In this article, a hybrid method for confidence interval construction for correlated rate difference is proposed to establish equivalence between two treatments in matched-pair studies in the presence of incomplete data. The basic idea is to recover variance estimates from readily available confidence limits for single parameters. We compare the hybrid Agresti-Coull, Wilson score and Jeffreys confidence intervals with the asymptotic Wald and score confidence intervals with respect to their empirical coverage probabilities, expected confidence widths, ratios of left non-coverage probability, and total non-coverage probability. Our simulation studies suggest that the Agresti-Coull hybrid confidence intervals is better than the score-test-based and likelihood-ratio-based confidence interval in small to moderate sample sizes in the sense that the hybrid confidence interval controls its true coverage probabilities around the pre-assigned coverage level well and yield shorter expected confidence widths. A real medical equivalence trial with incomplete data is used to illustrate the proposed methodologies. © 2011 International Chinese Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/172486
ISSN
2015 SCImago Journal Rankings: 0.591
References

 

DC FieldValueLanguage
dc.contributor.authorTang, MLen_US
dc.contributor.authorLi, HQen_US
dc.contributor.authorChan, ISFen_US
dc.contributor.authorTian, GLen_US
dc.date.accessioned2012-10-30T06:22:45Z-
dc.date.available2012-10-30T06:22:45Z-
dc.date.issued2011en_US
dc.identifier.citationStatistics In Biosciences, 2011, v. 3 n. 2, p. 223-249en_US
dc.identifier.issn1867-1764en_US
dc.identifier.urihttp://hdl.handle.net/10722/172486-
dc.description.abstractMatched-pair design is often adopted in equivalence or non-inferiority trials to increase the efficiency of binary-outcome treatment comparison. Briefly, subjects are required to participate in two binary-outcome treatments (e. g., old and new treatments via crossover design) under study. To establish the equivalence between the two treatments at the α significance level, a (1-α)100% confidence interval for the correlated proportion difference is constructed and determined if it is entirely lying in the interval (-δ 0,δ 0) for some clinically acceptable threshold δ 0 (e. g., 0.05). Nonetheless, some subjects may not be able to go through both treatments in practice and incomplete data thus arise. In this article, a hybrid method for confidence interval construction for correlated rate difference is proposed to establish equivalence between two treatments in matched-pair studies in the presence of incomplete data. The basic idea is to recover variance estimates from readily available confidence limits for single parameters. We compare the hybrid Agresti-Coull, Wilson score and Jeffreys confidence intervals with the asymptotic Wald and score confidence intervals with respect to their empirical coverage probabilities, expected confidence widths, ratios of left non-coverage probability, and total non-coverage probability. Our simulation studies suggest that the Agresti-Coull hybrid confidence intervals is better than the score-test-based and likelihood-ratio-based confidence interval in small to moderate sample sizes in the sense that the hybrid confidence interval controls its true coverage probabilities around the pre-assigned coverage level well and yield shorter expected confidence widths. A real medical equivalence trial with incomplete data is used to illustrate the proposed methodologies. © 2011 International Chinese Statistical Association.en_US
dc.languageengen_US
dc.relation.ispartofStatistics in Biosciencesen_US
dc.subjectAgresti-Coull Intervalen_US
dc.subjectIncomplete Dataen_US
dc.subjectJeffreys Intervalen_US
dc.subjectMethod Of Variance Estimations Recoveryen_US
dc.subjectProportion Differenceen_US
dc.subjectWilson Intervalen_US
dc.titleOn Confidence Interval Construction for Establishing Equivalence of Two Binary-Outcome Treatments in Matched-Pair Studies in the Presence of Incomplete Dataen_US
dc.typeArticleen_US
dc.identifier.emailTian, GL: gltian@hku.hken_US
dc.identifier.authorityTian, GL=rp00789en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/s12561-011-9044-3en_US
dc.identifier.scopuseid_2-s2.0-80955142207en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80955142207&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume3en_US
dc.identifier.issue2en_US
dc.identifier.spage223en_US
dc.identifier.epage249en_US
dc.identifier.scopusauthoridTang, ML=7401974011en_US
dc.identifier.scopusauthoridLi, HQ=41961453100en_US
dc.identifier.scopusauthoridChan, ISF=35358702000en_US
dc.identifier.scopusauthoridTian, GL=25621549400en_US

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