TY - JOUR
T1 - Confidence intervals for proportion difference from two independent partially validated series
AU - Qiu, Shi Fang
AU - Poon, Wai Yin
AU - Tang, Man Lai
N1 - Publisher Copyright:
© SAGE Publications.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - Partially validated series are common when a gold-standard test is too expensive to be applied to all subjects, and hence a fallible device is used accordingly to measure the presence of a characteristic of interest. In this article, confidence interval construction for proportion difference between two independent partially validated series is studied. Ten confidence intervals based on the method of variance estimates recovery (MOVER) are proposed, with each using the confidence limits for the two independent binomial proportions obtained by the asymptotic, Logit-transformation, Agresti-Coull and Bayesian methods. The performances of the proposed confidence intervals and three likelihood-based intervals available in the literature are compared with respect to the empirical coverage probability, confidence width and ratio of mesial non-coverage to non-coverage probability. Our empirical results show that (1) all confidence intervals exhibit good performance in large samples; (2) confidence intervals based on MOVER combining the confidence limits for binomial proportions based on Wilson, Agresti-Coull, Logit-transformation, Bayesian (with three priors) methods perform satisfactorily from small to large samples, and hence can be recommended for practical applications. Two real data sets are analysed to illustrate the proposed methods.
AB - Partially validated series are common when a gold-standard test is too expensive to be applied to all subjects, and hence a fallible device is used accordingly to measure the presence of a characteristic of interest. In this article, confidence interval construction for proportion difference between two independent partially validated series is studied. Ten confidence intervals based on the method of variance estimates recovery (MOVER) are proposed, with each using the confidence limits for the two independent binomial proportions obtained by the asymptotic, Logit-transformation, Agresti-Coull and Bayesian methods. The performances of the proposed confidence intervals and three likelihood-based intervals available in the literature are compared with respect to the empirical coverage probability, confidence width and ratio of mesial non-coverage to non-coverage probability. Our empirical results show that (1) all confidence intervals exhibit good performance in large samples; (2) confidence intervals based on MOVER combining the confidence limits for binomial proportions based on Wilson, Agresti-Coull, Logit-transformation, Bayesian (with three priors) methods perform satisfactorily from small to large samples, and hence can be recommended for practical applications. Two real data sets are analysed to illustrate the proposed methods.
KW - Bayesian confidence interval
KW - method of variance estimates recovery
KW - partially validated series
KW - proportion difference
UR - http://www.scopus.com/inward/record.url?scp=84989924088&partnerID=8YFLogxK
U2 - 10.1177/0962280213519718
DO - 10.1177/0962280213519718
M3 - Journal article
C2 - 24448443
AN - SCOPUS:84989924088
SN - 0962-2802
VL - 25
SP - 2250
EP - 2273
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 5
ER -