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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
Title | On Confidence Interval Construction for Establishing Equivalence of Two Binary-Outcome Treatments in Matched-Pair Studies in the Presence of Incomplete Data |
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Authors | |
Keywords | Agresti-Coull Interval Incomplete Data Jeffreys Interval Method Of Variance Estimations Recovery Proportion Difference Wilson Interval |
Issue Date | 2011 |
Citation | Statistics In Biosciences, 2011, v. 3 n. 2, p. 223-249 How to Cite? |
Abstract | Matched-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 Identifier | http://hdl.handle.net/10722/172486 |
ISSN | 2023 Impact Factor: 0.8 2023 SCImago Journal Rankings: 0.485 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tang, ML | en_US |
dc.contributor.author | Li, HQ | en_US |
dc.contributor.author | Chan, ISF | en_US |
dc.contributor.author | Tian, GL | en_US |
dc.date.accessioned | 2012-10-30T06:22:45Z | - |
dc.date.available | 2012-10-30T06:22:45Z | - |
dc.date.issued | 2011 | en_US |
dc.identifier.citation | Statistics In Biosciences, 2011, v. 3 n. 2, p. 223-249 | en_US |
dc.identifier.issn | 1867-1764 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/172486 | - |
dc.description.abstract | Matched-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.language | eng | en_US |
dc.relation.ispartof | Statistics in Biosciences | en_US |
dc.subject | Agresti-Coull Interval | en_US |
dc.subject | Incomplete Data | en_US |
dc.subject | Jeffreys Interval | en_US |
dc.subject | Method Of Variance Estimations Recovery | en_US |
dc.subject | Proportion Difference | en_US |
dc.subject | Wilson Interval | en_US |
dc.title | On Confidence Interval Construction for Establishing Equivalence of Two Binary-Outcome Treatments in Matched-Pair Studies in the Presence of Incomplete Data | en_US |
dc.type | Article | en_US |
dc.identifier.email | Tian, GL: gltian@hku.hk | en_US |
dc.identifier.authority | Tian, GL=rp00789 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1007/s12561-011-9044-3 | en_US |
dc.identifier.scopus | eid_2-s2.0-80955142207 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-80955142207&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 3 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.spage | 223 | en_US |
dc.identifier.epage | 249 | en_US |
dc.identifier.isi | WOS:000219188200005 | - |
dc.identifier.scopusauthorid | Tang, ML=7401974011 | en_US |
dc.identifier.scopusauthorid | Li, HQ=41961453100 | en_US |
dc.identifier.scopusauthorid | Chan, ISF=35358702000 | en_US |
dc.identifier.scopusauthorid | Tian, GL=25621549400 | en_US |
dc.identifier.issnl | 1867-1764 | - |