A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Ren, Dianxu; Stone, Roslyn A.

doi:10.1080/02664760701591895

Cited by 9 publications

(11 citation statements)

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“…To estimate the diagnostic accuracy without a gold standard, it requires information on the misclassification structure to make the model identifiable 11 . In model (1) for the IBTR status, the covariate vector x P (i.e., age, distant recurrence, and tumor stage in the MDACC dataset) plays the role of instrumental variables and it makes the sensitivity and specificity parameters identifiable when it has sufficient numbers of different realizations 12 .…”

Section: Discussionmentioning

confidence: 99%

“…In the presence of a single imperfect diagnostic test, some additional information on the misclassification structure is required in order to make the model identifiable and to adjust for potential bias due to misclassification 11 . To this end, Nagelkerke et al 12 suggested modeling the unobserved true disease status as a function of an instrumental variable, which is an additional parameter to increase the outcome degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous inference of a misclassified outcome and competing risks failure time data

Luo

et al. 2015

Journal of Applied Statistics

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Ipsilateral breast tumor relapse (IBTR) often occurs in breast cancer patients after their breast conservation therapy. The IBTR status’ classification (true local recurrence versus new ipsilateral primary tumor) is subject to error and there is no widely-accepted gold standard. Time to IBTR is likely informative for IBTR classification because new primary tumor tends to have a longer mean time to IBTR and is associated with improved survival as compared with the true local recurrence tumor. Moreover, some patients may die from breast cancer or other causes in a competing risk scenario during the follow-up period. Because the time to death can be correlated to the unobserved true IBTR status and time to IBTR (if relapse occurs), this terminal mechanism is non-ignorable. In this article, we propose a unified framework that addresses these issues simultaneously by modeling the misclassified binary outcome without a gold standard and the correlated time to IBTR, subject to dependent competing terminal events. We evaluate the proposed framework by a simulation study and apply it to a real dataset consisting of 4, 477 breast cancer patients. The adaptive Gaussian quadrature tools in procedure can be conveniently used to fit the proposed model. We expect to see broad applications of our model in other studies with a similar data structure.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous inference of a misclassified outcome and competing risks failure time data

Luo

et al. 2015

Journal of Applied Statistics

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“…For covariate misclassification, this has been applied by Ren and Stone (2007) and Hossain et al (2009). We denote the sensitivity and specificity for the second test as S 2 and C 2 .…”

Section: Case 2: Two Independent Diagnostic Testsmentioning

confidence: 99%

Bayesian Analysis and Design for Joint Modeling of Two Binary Responses With Misclassification

Stamey

Beavers

Sherr

2015

Sociological Methods & Research

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Survey data are often subject to various types of errors such as misclassification. In this article, we consider a model where interest is simultaneously in two correlated response variables and one is potentially subject to misclassification. A motivating example of a recent study of the impact of a sexual education course for adolescents is considered. A simulation-based sample size determination scheme is applied to illustrate the impact of misclassification on power and bias for the parameters of interest.

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“…23 When no gold standard, but two imperfect measures are available, one can account for the misclassification using a maximum likelihood approach. 24 Because the likelihood function often involves complicated integrations that a closed form is unavailable for most models, the expectation–maximization algorithm 17 and Bayesian inference using Markov chain Monte Carlo (MCMC) methods 5,6,25,26 have been widely used to correct for misclassification. There are several advantages of using Bayesian inference framework: (a) the approximation of the integrals in the likelihood is not required, and the unobserved variables can be sampled along with the model parameters from their full posterior distribution; (b) the available prior information of some parameters can be readily incorporated; and (c) with the development of BUGS projects, 27 the implementation in OpenBUGS is made simple by specifying the likelihood function and the prior distribution of all unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

Binomial regression with a misclassified covariate and outcome

Luo

Chan

Detry

et al. 2012

Stat Methods Med Res

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Misclassification occurring in either outcome variables or categorical covariates or both is a common issue in medical science. It leads to biased results and distorted disease–exposure relationships. Moreover, it is often of clinical interest to obtain the estimates of sensitivity and specificity of some diagnostic methods even when neither gold standard nor prior knowledge about the parameters exists. We present a novel Bayesian approach in binomial regression when both the outcome variable and one binary covariate are subject to misclassification. Extensive simulation results under various scenarios and a real clinical example are given to illustrate the proposed approach. This approach is motivated and applied to a dataset from the Baylor Alzheimer’s Disease and Memory Disorders Center.

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A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Cited by 9 publications

References 50 publications

Simultaneous inference of a misclassified outcome and competing risks failure time data

Simultaneous inference of a misclassified outcome and competing risks failure time data

Bayesian Analysis and Design for Joint Modeling of Two Binary Responses With Misclassification

Binomial regression with a misclassified covariate and outcome

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