The Biplot as a diagnostic tool of local dependence in latent class models. A medical application

Correa, Rosa Sepúlveda; Villardón, José Luis Vicente; Vicente‐Galindo, Purificación

doi:10.1002/sim.3194

Cited by 22 publications

(20 citation statements)

References 27 publications

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“…Indeed, in the context of sparse data (many profiles with low frequencies), results with the asymptotic distribution may not apply and only those obtained with empirical distributions are recommended [12, 22–24, 36]. The empirical distribution was obtained by generating a large number of samples ( n = 500) from the null assumption and computing the corresponding statistic; the P -value was deduced from the quantile which corresponded to the statistic in the observed sample.Pairwise residual correlations and bivariate residual statistics were calculated to detect potential residual dependences between pairs of diagnostic tests that were not correctly taken into account [10, 12, 24, 37]. Leave-one-test-out analyses were performed by removing one by one each immunoenzymatic test in order to assess their influence on the diagnostic accuracy of the other medical tests [14].…”

Section: Methodsmentioning

confidence: 99%

Different latent class models were used and evaluated for assessing the accuracy of campylobacter diagnostic tests: overcoming imperfect reference standards?

Asselineau

Paye

Bessède³

et al. 2018

Epidemiol. Infect.

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In the absence of perfect reference standard, classical techniques result in biased diagnostic accuracy and prevalence estimates. By statistically defining the true disease status, latent class models (LCM) constitute a promising alternative. However, LCM is a complex method which relies on parametric assumptions, including usually a conditional independence between tests and might suffer from data sparseness. We carefully applied LCMs to assess new campylobacter infection detection tests for which bacteriological culture is an imperfect reference standard. Five diagnostic tests (culture, polymerase chain reaction and three immunoenzymatic tests) of campylobacter infection were collected in 623 patients from Bordeaux and Lyon Hospitals, France. Their diagnostic accuracy were estimated with standard and extended LCMs with a thorough examination of models goodness-of-fit. The model including a residual dependence specific to the immunoenzymatic tests best complied with LCM assumptions. Asymptotic results of goodness-of-fit statistics were substantially impaired by data sparseness and empirical distributions were preferred. Results confirmed moderate sensitivity of the culture and high performances of immunoenzymatic tests. LCMs can be used to estimate diagnostic tests accuracy in the absence of perfect reference standard. However, their implementation and assessment require specific attention due to data sparseness and limitations of existing software.

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Section: Methodsmentioning

confidence: 99%

Different latent class models were used and evaluated for assessing the accuracy of campylobacter diagnostic tests: overcoming imperfect reference standards?

Asselineau

Paye

Bessède³

et al. 2018

Epidemiol. Infect.

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“…The HCI in some medical problems may not be a realistic assumption, for example, when the two tests are based on a similar biological phenomenon (e.g. [20], [31]). The diagnostic of local dependence has been discussed by several authors [31]–[35] and different methods have been proposed.…”

Section: Methodsmentioning

confidence: 99%

“…[20], [31]). The diagnostic of local dependence has been discussed by several authors [31]–[35] and different methods have been proposed. Among others, Hagenaars [32] suggests the analysis of the standardized residuals for each pair of manifest variables.…”

Section: Methodsmentioning

confidence: 99%

“…Qu et al [35] also propose a graphical method, the correlation residual plot, which is obtained by plotting residuals of pairwise correlation coefficients, defined as the difference between the observed and expected correlations. Sepúlveda et al [31] propose the use of Biplot representations based on generalized linear models to identify conditional dependence between pairs of manifest variables within each latent class. In this field, Subtil el al.…”

Section: Methodsmentioning

confidence: 99%

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Bayesian Latent Class Models in Malaria Diagnosis

et al. 2012

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Aims The main focus of this study is to illustrate the importance of the statistical analysis in the evaluation of the accuracy of malaria diagnostic tests, without admitting a reference test, exploring a dataset ( 3317) collected in São Tomé and Príncipe. Methods Bayesian Latent Class Models (without and with constraints) are used to estimate the malaria infection prevalence, together with sensitivities, specificities, and predictive values of three diagnostic tests (RDT, Microscopy and PCR), in four subpopulations simultaneously based on a stratified analysis by age groups ( , 5 years old) and fever status (febrile, afebrile). Results In the afebrile individuals with at least five years old, the posterior mean of the malaria infection prevalence is 3.2% with a highest posterior density interval of [2.3–4.1]. The other three subpopulations (febrile 5 years, afebrile or febrile children less than 5 years) present a higher prevalence around 10.3% [8.8–11.7]. In afebrile children under-five years old, the sensitivity of microscopy is 50.5% [37.7–63.2]. In children under-five, the estimated sensitivities/specificities of RDT are 95.4% [90.3–99.5]/93.8% [91.6–96.0] – afebrile – and 94.1% [87.5–99.4]/97.5% [95.5–99.3] – febrile. In individuals with at least five years old are 96.0% [91.5–99.7]/98.7% [98.1–99.2] – afebrile – and 97.9% [95.3–99.8]/97.7% [96.6–98.6] – febrile. The PCR yields the most reliable results in four subpopulations. Conclusions The utility of this RDT in the field seems to be relevant. However, in all subpopulations, data provide enough evidence to suggest caution with the positive predictive values of the RDT. Microscopy has poor sensitivity compared to the other tests, particularly, in the afebrile children less than 5 years. This type of findings reveals the danger of statistical analysis based on microscopy as a reference test. Bayesian Latent Class Models provide a powerful tool to evaluate malaria diagnostic tests, taking into account different groups of interest.

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“…Un aporte importante es la utilización de los métodos Biplot basados en Modelos Lineales Generalizados, para diagnosticar la dependencia local en un Modelo de Clases Latentes (SEPÚLVEDA et al, 2008).…”

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Modelos de clases latentes en tablas poco ocupadas: una contribución basada en bootstrap

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The Biplot as a diagnostic tool of local dependence in latent class models. A medical application

Cited by 22 publications

References 27 publications

Different latent class models were used and evaluated for assessing the accuracy of campylobacter diagnostic tests: overcoming imperfect reference standards?

Different latent class models were used and evaluated for assessing the accuracy of campylobacter diagnostic tests: overcoming imperfect reference standards?

Bayesian Latent Class Models in Malaria Diagnosis

Modelos de clases latentes en tablas poco ocupadas: una contribución basada en bootstrap

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