On the robustness of latent class models for diagnostic testing with no gold standard

Estimating sensitivity and specificity of diagnostic tests using latent class models that account for conditional dependence between tests: a simulation study

BACKGROUND

Latent class models are increasingly used to estimate the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and are commonly fitted using Bayesian methods. These models allow us to account for 'conditional dependence' between two or more diagnostic tests, meaning that the results from tests are correlated even after conditioning on the person's true disease status. The challenge is that it is not always clear to researchers whether conditional dependence exists between tests and whether it exists in all or just some latent classes. Despite the increasingly widespread use of latent class models to estimate diagnostic test accuracy, the impact of the conditional dependence structure chosen on the estimates of sensitivity and specificity remains poorly investigated.

METHODS

A simulation study and a reanalysis of a published case study are used to highlight the impact of the conditional dependence structure chosen on estimates of sensitivity and specificity. We describe and implement three latent class random-effect models with differing conditional dependence structures, as well as a conditional independence model and a model that assumes perfect test accuracy. We assess the bias and coverage of each model in estimating sensitivity and specificity across different data generating mechanisms.

RESULTS

The findings highlight that assuming conditional independence between tests within a latent class, where conditional dependence exists, results in biased estimates of sensitivity and specificity and poor coverage. The simulations also reiterate the substantial bias in estimates of sensitivity and specificity when incorrectly assuming a reference test is perfect. The motivating example of tests for Melioidosis highlights these biases in practice with important differences found in estimated test accuracy under different model choices.

CONCLUSIONS

We have illustrated that misspecification of the conditional dependence structure leads to biased estimates of sensitivity and specificity when there is a correlation between tests. Due to the minimal loss in precision seen by using a more general model, we recommend accounting for conditional dependence even if researchers are unsure of its presence or it is only expected at minimal levels.

Performance of saliva compared with nasopharyngeal swab for diagnosis of COVID-19 by NAAT in cross-sectional studies: Systematic review and meta-analysis

Nucleic acid amplification testing (NAAT) is the preferred method to diagnose coronavirus disease 2019 (COVID-19). Saliva has been suggested as an alternative to nasopharyngeal swabs (NPS), but previous systematic reviews were limited by the number and types of studies available. The objective of this systematic review and meta-analysis was to assess the diagnostic performance of saliva compared with NPS for COVID-19. We searched Ovid MEDLINE, Embase, Cochrane, and Scopus databases up to 24 April 2021 for studies that directly compared paired NPS and saliva specimens taken at the time of diagnosis. Meta-analysis was performed using an exact binomial rendition of the bivariate mixed-effects regression model. Risk of bias was assessed using the QUADAS-2 tool. Of 2683 records, we included 23 studies with 25 cohorts, comprising 11,582 paired specimens. A wide variety of NAAT assays and collection methods were used. Meta-analysis gave a pooled sensitivity of 87 % (95 % CI = 83-90 %) and specificity of 99 % (95 % CI = 98-99 %). Subgroup analyses showed the highest sensitivity when the suspected individual is tested in an outpatient setting and is symptomatic. Our results support the use of saliva NAAT as an alternative to NPS NAAT for the diagnosis of COVID-19.

Bayesian latent class analysis produced diagnostic accuracy estimates that were more interpretable than composite reference standards for extrapulmonary tuberculosis tests

BACKGROUND

Evaluating the accuracy of extrapulmonary tuberculosis (TB) tests is challenging due to lack of a gold standard. Latent class analysis (LCA), a statistical modeling approach, can adjust for reference tests' imperfect accuracies to produce less biased test accuracy estimates than those produced by commonly used methods like composite reference standards (CRSs). Our objective is to illustrate how Bayesian LCA can address the problem of an unavailable gold standard and demonstrate how it compares to using CRSs for extrapulmonary TB tests.

METHODS

We re-analyzed a dataset of presumptive extrapulmonary TB cases in New Delhi, India, for three forms of extrapulmonary TB. Results were available for culture, smear microscopy, Xpert MTB/RIF, and a non-microbiological test, cytopathology/histopathology, or adenosine deaminase (ADA). A diagram was used to define assumed relationships between observed tests and underlying latent variables in the Bayesian LCA with input from an inter-disciplinary team. We compared the results to estimates obtained from a sequence of CRSs defined by increasing numbers of positive reference tests necessary for positive disease status.

RESULTS

Data were available from 298, 388, and 230 individuals with presumptive TB lymphadenitis, meningitis, and pleuritis, respectively. Using Bayesian LCA, estimates were obtained for accuracy of all tests and for extrapulmonary TB prevalence. Xpert sensitivity neared that of culture for TB lymphadenitis and meningitis but was lower for TB pleuritis, and specificities of all microbiological tests approached 100%. Non-microbiological tests' sensitivities were high, but specificities were only moderate, preventing disease rule-in. CRSs' only provided estimates of Xpert and these varied widely per CRS definition. Accuracy of the CRSs also varied by definition, and no CRS was 100% accurate.

CONCLUSION

Unlike CRSs, Bayesian LCA takes into account known information about test performance resulting in accuracy estimates that are easier to interpret. LCA should receive greater consideration for evaluating extrapulmonary TB diagnostic tests.

Simplifying the estimation of diagnostic testing accuracy over time for high specificity tests in the absence of a gold standard

Many different methods for evaluating diagnostic test results in the absence of a gold standard have been proposed. In this paper, we discuss how one common method, a maximum likelihood estimate for a latent class model found via the Expectation-Maximization (EM) algorithm can be applied to longitudinal data where test sensitivity changes over time. We also propose two simplified and nonparametric methods which use data-based indicator variables for disease status and compare their accuracy to the maximum likelihood estimation (MLE) results. We find that with high specificity tests, the performance of simpler approximations may be just as high as the MLE.

Role of Diagnostics in Epidemiology, Management, Surveillance, and Control of Leptospirosis

A One Health approach to the epidemiology, management, surveillance, and control of leptospirosis relies on accessible and accurate diagnostics that can be applied to humans and companion animals and livestock. Diagnosis should be multifaceted and take into account exposure risk, clinical presentation, and multiple direct and/or indirect diagnostic approaches. Methods of direct detection of Leptospira spp. include culture, histopathology and immunostaining of tissues or clinical specimens, and nucleic acid amplification tests (NAATs). Indirect serologic methods to detect leptospiral antibodies include the microscopic agglutination test (MAT), the enzyme-linked immunosorbent assay (ELISA), and lateral flow methods. Rapid diagnostics that can be applied at the point-of-care; NAAT and lateral flow serologic tests are essential for management of acute infection and control of outbreaks. Culture is essential to an understanding of regional knowledge of circulating strains, and we discuss recent improvements in methods for cultivation, genomic sequencing, and serotyping. We review the limitations of NAATs, MAT, and other diagnostic approaches in the context of our expanding understanding of the diversity of pathogenic Leptospira spp. Novel approaches are needed, such as loop mediated isothermal amplification (LAMP) and clustered regularly interspaced short palindromic repeats (CRISPR)-based approaches to leptospiral nucleic acid detection.