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Confidence Intervals for the Risk Ratio Using Double Sampling with Misclassified Binomial Data
Volume 9, Issue 4 (2011), pp. 529–548
Dewi Rahardja   Dean M. Young  

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https://doi.org/10.6339/JDS.201110_09(4).0004
Pub. online: 10 July 2021      Type: Research Article      Open accessOpen Access

Published
10 July 2021

Abstract

Abstract: We derive three likelihood-based confidence intervals for the risk ratio of two proportion parameters using a double sampling scheme for mis classified binomial data. The risk ratio is also known as the relative risk. We obtain closed-form maximum likelihood estimators of the model parameters by maximizing the full-likelihood function. Moreover, we develop three confidence intervals: a naive Wald interval, a modified Wald interval, and a Fieller-type interval. We apply the three confidence intervals to cervical cancer data. Finally, we perform two Monte Carlo simulation studies to assess and compare the coverage probabilities and average lengths of the three interval estimators. Unlike the other two interval estimators, the modified Wald interval always produces close-to-nominal confidence intervals for the various simulation scenarios examined here. Hence, the modified Wald confidence interval is preferred in practice.

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Keywords
Binomial data double sampling misclassification

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