Literature DB >> 36213773

Confidence intervals for assessing equivalence of two treatments with combined unilateral and bilateral data.

Shi-Fang Qiu1, Ji-Ran Tao2.   

Abstract

Responses from the paired organs are generally highly correlated in bilateral studies, statistical procedures ignoring the correlation could lead to incorrect results. Note the intraclass correlation in the study of combined unilateral and bilateral outcomes; 11 confidence intervals (CIs) including 7 asymptotic CIs and 4 Bootstrap-resampling CIs for assessing the equivalence of 2 treatments are derived under Rosner's correlated binary data model. Performance is evaluated with respect to the empirical coverage probability (ECP), the empirical coverage width (ECW) and the ratio of the mesial non-coverage probability to the non-coverage probability (RMNCP) via simulation studies. Simulation results show that (i) all CIs except for the Wald CI and the bias-corrected Bootstrap percentile CI generally produce satisfactory ECPs and hence are recommended; (ii) all CIs except for the bias-corrected Bootstrap percentile CI provide preferred RMNCPs and are more symmetrical; (iii) as the measurement of the dependence increases, the ECWs of all CIs except for the score CI and the profile likelihood CI show increasing patterns that look like linear, while there is no obvious pattern on the ECPs of all CIs except for the profile likelihood CI. A data set from an otolaryngologic study is used to illustrate the proposed methods.
© 2021 Informa UK Limited, trading as Taylor & Francis Group.

Entities:  

Keywords:  Bootstrap-resampling method; combined unilateral and bilateral data; confidence interval; intra-class correlation; proportion difference

Year:  2021        PMID: 36213773      PMCID: PMC9543133          DOI: 10.1080/02664763.2021.1949440

Source DB:  PubMed          Journal:  J Appl Stat        ISSN: 0266-4763            Impact factor:   1.416


  15 in total

1.  On small-sample confidence intervals for parameters in discrete distributions.

Authors:  A Agresti; Y Min
Journal:  Biometrics       Date:  2001-09       Impact factor: 2.571

2.  A quasi-exact method for the confidence intervals of the difference of two independent binomial proportions in small sample cases.

Authors:  Xun Chen
Journal:  Stat Med       Date:  2002-03-30       Impact factor: 2.373

3.  Test-based exact confidence intervals for the difference of two binomial proportions.

Authors:  I S Chan; Z Zhang
Journal:  Biometrics       Date:  1999-12       Impact factor: 2.571

Review 4.  Goodness-of-fit tests for correlated paired binary data.

Authors:  Man-Lai Tang; Yan-Bo Pei; Weng-Kee Wong; Jia-Liang Li
Journal:  Stat Methods Med Res       Date:  2010-09-01       Impact factor: 3.021

5.  Confidence intervals for correlated proportion differences from paired data in a two-arm randomised clinical trial.

Authors:  Yanbo Pei; Man-Lai Tang; Weng-Kee Wong; Jianhua Guo
Journal:  Stat Methods Med Res       Date:  2010-05-04       Impact factor: 3.021

6.  Confidence interval construction for proportion difference in small-sample paired studies.

Authors:  Man-Lai Tang; Nian-Sheng Tang; Ivan S F Chan
Journal:  Stat Med       Date:  2005-12-15       Impact factor: 2.373

7.  Paired Bernoulli trials.

Authors:  G E Dallal
Journal:  Biometrics       Date:  1988-03       Impact factor: 2.571

8.  Asymptotic confidence intervals for the difference between two binomial parameters for use with small samples.

Authors:  S L Beal
Journal:  Biometrics       Date:  1987-12       Impact factor: 2.571

9.  Significance testing for correlated binary outcome data.

Authors:  B Rosner; R C Milton
Journal:  Biometrics       Date:  1988-06       Impact factor: 2.571

10.  Testing for linear trends in proportions using correlated otolaryngology or ophthalmology data.

Authors:  C T Le
Journal:  Biometrics       Date:  1988-03       Impact factor: 2.571

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