scholarly journals An Iterative, Frequentist Approach for Latent Class Analysis to Evaluate Conditionally Dependent Diagnostic Tests

2021 ◽  
Vol 8 ◽  
Author(s):  
Clara Schoneberg ◽  
Lothar Kreienbrock ◽  
Amely Campe
Keyword(s):  
Veterinary Medicine ◽  
Latent Class Analysis ◽  
Bayesian Approach ◽  
Diagnostic Tests ◽  
Degrees Of Freedom ◽  
Latent Class ◽  
Latent Class Model ◽  
Test Accuracy ◽  
Class Analysis ◽  
The Bayesian Approach

Latent class analysis is a well-established method in human and veterinary medicine for evaluating the accuracy of diagnostic tests without a gold standard. An important assumption of this procedure is the conditional independence of the tests. If tests with the same biological principle are used, this assumption is no longer met. Therefore, the model has to be adapted so that the dependencies between the tests can be considered. Our approach extends the traditional latent class model with a term for the conditional dependency of the tests. This extension increases the number of parameters to be estimated and leads to negative degrees of freedom of the model, meaning that not enough information is contained in the existing data to obtain a unique estimate. As a result, there is no clear solution. Hence, an iterative algorithm was developed to keep the number of parameters to be estimated small. Given adequate starting values, our approach first estimates the conditional dependencies and then regards the resulting values as fixed to recalculate the test accuracies and the prevalence with the same method used for independent tests. Subsequently, the new values of the test accuracy and prevalence are used to recalculate the terms for the conditional dependencies. These two steps are repeated until the model converges. We simulated five application scenarios based on diagnostic tests used in veterinary medicine. The results suggest that our method and the Bayesian approach produce similar precise results. However, while the presented approach is able to calculate more accurate results than the Bayesian approach if the test accuracies are initially misjudged, the estimates of the Bayesian method are more precise when incorrect dependencies are assumed. This finding shows that our approach is a useful addition to the existing Bayesian methods, while it has the advantage of allowing simpler and more objective estimations.

scholarly journals Use of Individual-level Covariates to Improve Latent Class Analysis of Trypanosoma cruzi Diagnostic Tests

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Aaron W. Tustin ◽  
Dylan S. Small ◽  
Stephen Delgado ◽  
Ricardo Castillo Neyra ◽  
Manuela R. Verastegui ◽  
...  
Keyword(s):  
Trypanosoma Cruzi ◽  
Latent Class Analysis ◽  
Diagnostic Tests ◽  
Latent Class ◽  
Class Analysis ◽  
Individual Level

Politieke voorkeur en antwoordgedrag van respondenten in politieke opiniepeilingen

Res Publica ◽  
1994 ◽  
Vol 36 (2) ◽  
pp. 143-152
Author(s):  
Geert Loosveldt
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Response Category ◽  
The Political ◽  
Class Analysis ◽  
Background Characteristics ◽  
Class Model ◽  
Political Preference

In this article a typology of respondent's ability to participate in a survey interview is created by means of a latent class analysis. The indicators in the analysis are: the interviewer's evaluation of the respondent's ability, the use of the "don't know" response category and inconsistent answers. It was possible to fit a latent class model with three classes or types of respondents. The three types are clearly differentiated concerning ability. As expected, this typology is related to respondent's education and age. Ability to participate is higher for better educated and younger respondents. Given the fact that political preference is also related to these two background characteristics, there is a relationship between the respondent's typology and the political preference of the respondents.

Evaluation of post-mortem diagnostic tests' sensitivity and specificity for bovine tuberculosis using Bayesian latent class analysis

2019 ◽  
Vol 125 ◽  
pp. 14-23
Author(s):  
Paulo Martins Soares Filho ◽  
Alberto Knust Ramalho ◽  
André de Moura Silva ◽  
Mikael Arrais Hodon ◽  
Marina de Azevedo Issa ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Sensitivity And Specificity ◽  
Bovine Tuberculosis ◽  
Diagnostic Tests ◽  
Latent Class ◽  
Post Mortem ◽  
Class Analysis ◽  
Bayesian Latent Class Analysis

scholarly journals Latent class analysis of diagnostic tests for visceral leishmaniasis in Brazil

2012 ◽  
Vol 17 (10) ◽  
pp. 1202-1207 ◽  
Author(s):  
Tália Santana Machado de Assis ◽  
Ana Rabello ◽  
Guilherme Loureiro Werneck
Keyword(s):  
Visceral Leishmaniasis ◽  
Latent Class Analysis ◽  
Diagnostic Tests ◽  
Latent Class ◽  
Class Analysis

scholarly journals Diagnostic Test Accuracy in Childhood Pulmonary Tuberculosis: A Bayesian Latent Class Analysis

2016 ◽  
Vol 184 (9) ◽  
pp. 690-700 ◽  
Author(s):  
Samuel G. Schumacher ◽  
Maarten van Smeden ◽  
Nandini Dendukuri ◽  
Lawrence Joseph ◽  
Mark P. Nicol ◽  
...  
Keyword(s):  
Pulmonary Tuberculosis ◽  
Latent Class Analysis ◽  
Chest Radiography ◽  
Latent Class ◽  
Credible Interval ◽  
Smear Microscopy ◽  
Test Accuracy ◽  
Class Analysis ◽  
Antituberculosis Treatment ◽  
Bayesian Latent Class Analysis

AbstractEvaluation of tests for the diagnosis of childhood pulmonary tuberculosis (CPTB) is complicated by the absence of an accurate reference test. We present a Bayesian latent class analysis in which we evaluated the accuracy of 5 diagnostic tests for CPTB. We used data from a study of 749 hospitalized South African children suspected to have CPTB from 2009 to 2014. The following tests were used: mycobacterial culture, smear microscopy, Xpert MTB/RIF (Cepheid Inc.), tuberculin skin test (TST), and chest radiography. We estimated the prevalence of CPTB to be 27% (95% credible interval (CrI): 21, 35). The sensitivities of culture, Xpert, and smear microscopy were estimated to be 60% (95% CrI: 46, 76), 49% (95% CrI: 38, 62), and 22% (95% CrI: 16, 30), respectively; specificities of these tests were estimated in accordance with prior information and were close to 100%. Chest radiography was estimated to have a sensitivity of 64% (95% CrI: 55, 73) and a specificity of 78% (95% CrI: 73, 83). Sensitivity of the TST was estimated to be 75% (95% CrI: 61, 84), and it decreased substantially among children who were malnourished and infected with human immunodeficiency virus (56%). The specificity of the TST was 69% (95% CrI: 63%, 76%). Furthermore, it was estimated that 46% (95% CrI: 42, 49) of CPTB-negative cases and 93% (95% CrI: 82; 98) of CPTB-positive cases received antituberculosis treatment, which indicates substantial overtreatment and limited undertreatment.

scholarly journals Evaluating diagnostic tests for bovine tuberculosis in the southern part of Germany: A latent class analysis

PLoS ONE ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. e0179847 ◽  
Author(s):  
Valerie-Beau Pucken ◽  
Gabriela Knubben-Schweizer ◽  
Dörte Döpfer ◽  
Andreas Groll ◽  
Angela Hafner-Marx ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Bovine Tuberculosis ◽  
Diagnostic Tests ◽  
Latent Class ◽  
Class Analysis

scholarly journals The Use of Latent Class Analysis to Estimate the Sensitivities and Specificities of Diagnostic Tests for Squash vein yellowing virus in Cucurbit Species When There Is No Gold Standard

2013 ◽  
Vol 103 (12) ◽  
pp. 1243-1251 ◽  
Author(s):  
William W. Turechek ◽  
Craig G. Webster ◽  
Jingyi Duan ◽  
Pamela D. Roberts ◽  
Chandrasekar S. Kousik ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Diagnostic Test ◽  
Test Performance ◽  
Gold Standard ◽  
Diagnostic Tests ◽  
Latent Class ◽  
Visual Assessment ◽  
Class Analysis ◽  
Rt Pcr ◽  
Yellowing Virus

Squash vein yellowing virus (SqVYV) is the causal agent of viral watermelon vine decline, one of the most serious diseases in watermelon (Citrullus lanatus L.) production in the southeastern United States. At present, there is not a gold standard diagnostic test for determining the true status of SqVYV infection in plants. Current diagnostic methods for identification of SqVYV-infected plants or tissues are based on the reverse-transcription polymerase chain reaction (RT-PCR), tissue blot nucleic acid hybridization assays (TB), and expression of visual symptoms. A quantitative assessment of the performance of these diagnostic tests is lacking, which may lead to an incorrect interpretation of results. In this study, latent class analysis (LCA) was used to estimate the sensitivities and specificities of RT-PCR, TB, and visual assessment of symptoms as diagnostic tests for SqVYV. The LCA model assumes that the observed diagnostic test responses are linked to an underlying latent (nonobserved) disease status of the population, and can be used to estimate sensitivity and specificity of the individual tests, as well as to derive an estimate of the incidence of disease when a gold standard test does not exist. LCA can also be expanded to evaluate the effect of factors and was done here to determine whether diagnostic test performances varied among the type of plant tissue being tested (crown versus vine tissue), where plant samples were taken relative to the position of the crown (i.e., distance from the crown), host (i.e., genus), and habitat (field-grown versus greenhouse-grown plants). Results showed that RT-PCR had the highest sensitivity (0.94) and specificity (0.98) of the three tests. TB had better sensitivity than symptoms for detection of SqVYV infection (0.70 versus 0.32), while the visual assessment of symptoms was more specific than TB and, thus, a better indicator of noninfection (0.98 versus 0.65). With respect to the grouping variables, RT-PCR and TB had better sensitivity but poorer specificity for diagnosing SqVYV infection in crown tissue than it did in vine tissue, whereas symptoms had very poor sensitivity but excellent specificity in both tissues for all cucurbits analyzed in this study. Test performance also varied with habitat and genus but not with distance from the crown. The results given here provide quantitative measurements of test performance for a range of conditions and provide the information needed to interpret test results when tests are used in parallel or serial combination for a diagnosis.

scholarly journals Using Latent Class Analysis to Identify Different Risk Patterns for Patients With Masked Hypertension

2021 ◽  
Vol 8 ◽  
Author(s):  
Ming Fu ◽  
Xiangming Hu ◽  
Shixin Yi ◽  
Shuo Sun ◽  
Ying Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Fasting Blood Glucose ◽  
Low Risk ◽  
Biological Information ◽  
Masked Hypertension ◽  
Class Analysis ◽  
The Impact

Background: There is controversy whether masked hypertension (MHT) requires additional intervention. The aim of this study is to evaluate whether MHT accompanied with high-risk metabolic syndrome (MetS), as the subphenotype, will have a different prognosis from low-risk MetS.Methods: We applied latent class analysis to identify subphenotypes of MHT, using the clinical and biological information collected from High-risk Cardiovascular Factor Screening and Chronic Disease Management Programme. We modeled the data, examined the relationship between subphenotypes and clinical outcomes, and further explored the impact of antihypertensive medication.Results: We included a total of 140 patients with MHT for analysis. The latent class model showed that the two-class (high/low-risk MetS) model was most suitable for MHT classification. The high-risk MetS subphenotype was characterized by larger waist circumference, lower HDL-C, higher fasting blood glucose and triglycerides, and prevalence of diabetes. After four years of follow-up, participants in subphenotype 1 had a higher non-major adverse cardiovascular event (MACE) survival probability than those in subphenotype 2 (P = 0.016). There was no interaction between different subphenotypes and the use of antihypertensive medications affecting the occurrence of MACE.Conclusions: We have identified two subphenotypes in MHT that have different metabolic characteristics and prognosis, which could give a clue to the importance of tracing the clinical correlation between MHT and metabolic risk factors. For patients with MHT and high-risk MetS, antihypertensive therapy may be insufficient.

scholarly journals Applying latent class analysis in the identification of occupational accident patterns

2020 ◽  
Vol 2020 (146) ◽  
pp. 339-355
Author(s):  
Marzena NOWAKOWSKA ◽  
◽  
Michał PAJĘCKI ◽  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Severe Accident ◽  
Occupational Accidents ◽  
Occupational Accident ◽  
Class Analysis ◽  
Data Set ◽  
Pilot Studies ◽  
Individual Record

Purpose: The objective of the study is to use selected data mining techniques to discover patterns of certain recurring mechanisms related to the occurrence of occupational accidents in relation to production processes. Design/methodology/approach: The latent class analysis (LCA) method was employed in the investigation. This statistical modeling technique enables discovering mutually exclusive homogenous classes of objects in a multivariate data set on the basis of observable qualitative variables, defining the class homogeneity in terms of probabilities. Due to a bilateral agreement, Statistics Poland provided individual record-level real data for the research. Then the data were preprocessed to enable the LCA model identification. Pilot studies were conducted in relation to occupational accidents registered in production plants in 2008-2017 in the Wielkopolskie voivodeship. Findings: Three severe accident patterns and two light accident patterns represented by latent classes were obtained. The classes were subjected to descriptive characteristics and labeling, using interpretable results presented in the form of probabilities classifying categories of observable variables, symptomatic for a given latent class. Research limitations/implications: The results from the pilot studies indicate the necessity to continue the research based on a larger data set along with the analysis development, particularly as regards selecting indicators for the latent class model characterization. Practical implications: The identification of occupational accident patterns related to the production process can play a vital role in the elaboration of efficient safety countermeasures that can help to improve the prevention and outcome mitigation of such accidents among workers. Social implications: Creating a safe work environment comprises the quality of life of workers, their families, thus affirming the enterprises' principles and values in the area of corporate social responsibility. Originality/value: The investigation showed that latent class analysis is a promising tool supporting the scientific research in discovering the patterns of occupational accidents. The proposed investigation approach indicates the importance for the research both in terms of the availability of non-aggregated occupational accident data as well as the type of value aggregation of the variables taken for the analysis.

Sign in / Sign up

Export Citation Format

Share Document