Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data

Poon, Wai‐Yin; Wang, Haibin

doi:10.1007/s11336-010-9164-6

Cited by 20 publications

(11 citation statements)

References 45 publications

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“…Espeland and Odoroff () expressed double‐sampling data as incomplete contingency tables, and used EM algorithm to analyze log‐linear models. Poon and Wang () adopted a unified EM framework for analyzing ordinal categorical data with misclassification, and Poon and Wang () considered a hybrid Gibbs sampler Bayesian approach for analyzing Probit models with surrogate outcome data. These approaches are typical methods in missing‐data frameworks, suggesting that a missing‐data‐type approach represents a promising future research direction for the comparisons of disease prevalence.…”

Section: Discussionmentioning

confidence: 99%

“…This study assessed how misdiagnosis affected the estimated prevalence of Plasmodium falciparum . Moreover, using the group variable as a covariate, the logistic regression approach proposed by Pepe () and the Bayesian analysis of multivariate probit models proposed by Poon and Wang () have been employed to analyze data with two independent groups. Treating the group variables as one of the two variables under study, in contrast, group comparison can also be achieved by examining the association between the group variable and the variable of interest in terms of the polychoric correlation in the bivariate latent variable normal model proposed by Yiu and Poon ().…”

Section: Introductionmentioning

confidence: 99%

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Comparison of disease prevalence in two populations in the presence of misclassification

2012

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Comparing disease prevalence in two groups is an important topic in medical research, and prevalence rates are obtained by classifying subjects according to whether they have the disease. Both high-cost infallible gold-standard classifiers or low-cost fallible classifiers can be used to classify subjects. However, statistical analysis that is based on data sets with misclassifications leads to biased results. As a compromise between the two classification approaches, partially validated sets are often used in which all individuals are classified by fallible classifiers, and some of the individuals are validated by the accurate gold-standard classifiers. In this article, we develop several reliable test procedures and approximate sample size formulas for disease prevalence studies based on the difference between two disease prevalence rates with two independent partially validated series. Empirical studies show that (i) the Score test produces close-to-nominal level and is preferred in practice; and (ii) the sample size formula based on the Score test is also fairly accurate in terms of the empirical power and type I error rate, and is hence recommended. A real example from an aplastic anemia study is used to illustrate the proposed methodologies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of disease prevalence in two populations in the presence of misclassification

2012

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“…Finally,

{\overset{true˜}{y}}_{ij}

are generated from the Bernoulli distribution with mean

{\overset{true˜}{p}}_{ij}

. In the simulation study, following R amalho () and P oon and W ang (), we have considered misclassification rates independent of the covariates and random effects. We simulate a data set of 1000 sibships each of size 5 and then ascertain sibships having at least one affected member.…”

Section: Simulation Studymentioning

confidence: 99%

Analysis of binary data with the possibility of wrong ascertainment

Roy¹,

Das²,

Sarkar

2013

Statistica Neerlandica

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Most genetic studies recruit high-risk families, and the discoveries are based on non-random selected groups. We must consider the consequences of this ascertainment process to apply the results of genetic research to the general population. In addition, in epidemiological studies, binary responses are often misclassified. We proposed a binary logistic regression model that provides a novel and flexible way to correct for misclassification in binary responses, taking into account the ascertainment issues. A hierarchical Bayesian analysis using Markov chain Monte Carlo method has been carried out to investigate the effect of covariates on disease status. The focus of this paper is to study the effect of classification errors and non-random ascertainment on the estimates of the model parameters. An extensive simulation study indicated that the proposed model results in substantial improvement of the estimates. Two data sets have been revisited to illustrate the methodology.

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“…To analyze the ordinal categorical data obtained by the double-sampling method, Yiu and Poon 7 proposed a latent variable normal model and developed a two-stage estimation procedure that used a minimum chi-squared approach to find the correlation of the two variables. Poon and Wang 8 developed a new class of parametric models by generalizing the multivariate probit model and the errors-invariables model to the ordinal partially validated data. More recently, for a single group of partially validated data, Tang et al.…”

Section: Introductionmentioning

confidence: 99%

Homogeneity testing for binomial proportions under stratified double-sampling scheme with two fallible classifiers

Qiu

2020

Stat Methods Med Res

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This article investigates the homogeneity testing problem of binomial proportions for stratified partially validated data obtained by double-sampling method with two fallible classifiers. Several test procedures, including the weighted-least-squares test with/without log-transformation, logit-transformation and double log-transformation, and likelihood ratio test and score test, are developed to test the homogeneity under two models, distinguished by conditional independence assumption of two classifiers. Simulation results show that score test performs better than other tests in the sense that the empirical size is generally controlled around the nominal level, and hence be recommended to practical applications. Other tests also perform well when both binomial proportions and sample sizes are not small. Approximate sample sizes based on score test, likelihood ratio test and the weighted-least-squares test with double log-transformation are generally accurate in terms of the empirical power and type I error rate with the estimated sample sizes, and hence be recommended. An example from the malaria study is illustrated by the proposed methodologies.

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Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data

Abstract: errors-in-variables, Gibbs sampler, Metropolis–Hastings algorithm, misclassification, multivariate probit model, parameter expansion, surrogate variable,

Cited by 20 publications

References 45 publications

Comparison of disease prevalence in two populations in the presence of misclassification

Comparison of disease prevalence in two populations in the presence of misclassification

Analysis of binary data with the possibility of wrong ascertainment

Homogeneity testing for binomial proportions under stratified double-sampling scheme with two fallible classifiers

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