scholarly journals Comparison of the Average Kappa Coefficients of Two Binary Diagnostic Tests with Missing Data

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2834
Author(s):  
José Antonio Roldán-Nofuentes ◽  
Saad Bouh Regad
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Diagnostic Test ◽  
Gold Standard ◽  
Diagnostic Tests ◽  
Missing At Random ◽  
Disease Status ◽  
Simulation Experiments ◽  
Sem Algorithm ◽  
The Em Algorithm

The average kappa coefficient of a binary diagnostic test is a parameter that measures the average beyond-chance agreement between the diagnostic test and the gold standard. This parameter depends on the accuracy of the diagnostic test and also on the disease prevalence. This article studies the comparison of the average kappa coefficients of two binary diagnostic tests when the gold standard is not applied to all individuals in a random sample. In this situation, known as partial disease verification, the disease status of some individuals is a missing piece of data. Assuming that the missing data mechanism is missing at random, the comparison of the average kappa coefficients is solved by applying two computational methods: the EM algorithm and the SEM algorithm. With the EM algorithm the parameters are estimated and with the SEM algorithm their variances-covariances are estimated. Simulation experiments have been carried out to study the sizes and powers of the hypothesis tests studied, obtaining that the proposed method has good asymptotic behavior. A function has been written in R to solve the proposed problem, and the results obtained have been applied to the diagnosis of Alzheimer's disease.

Parameter Estimation under Constraints for Multivariate Normal Distributions with Incomplete Data

2001 ◽  
Vol 26 (2) ◽  
pp. 219-232
Author(s):  
Alice Zoppé ◽  
Yuh-Pey Anne Buu ◽  
Bernard Flury
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Missing Values ◽  
Missing At Random ◽  
Maximum Likelihood Estimates ◽  
Data Matrix ◽  
Ratio Test ◽  
Multivariate Normal ◽  
The Em Algorithm ◽  
Log Likelihood

This work presents an application of the EM-algorithm to two problems of estimation and testing in a multivariate normal distribution with missing data. The assumptions are that the observations are multivariate normally distributed and that the missing values are missing at random. The two models are tested applying the log-likelihood ratio test; for deriving the maximum likelihood estimates and evaluating the corresponding log-likelihood functions the EM algorithm is used. The problem of different and non-monotone patterns of missing data is solved introducing suitable transformations and partitions of the data matrix. The algorithm is proposed for general constraints on the mean vector; the topic of exchangeability of random vectors is also presented.

scholarly journals PENDUGAAN DATA HILANG DENGAN METODE YATES DAN ALGORITMA EM PADA RANCANGAN LATTICE SEIMBANG

2015 ◽  
Vol 4 (2) ◽  
pp. 74
Author(s):  
MADE SUSILAWATI ◽  
KARTIKA SARI
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Basic Concept ◽  
Incomplete Data ◽  
Likelihood Function ◽  
Animal Husbandry ◽  
Lattice Design ◽  
The Em Algorithm ◽  
Missing Data Estimation ◽  
Data Estimation

Missing data often occur in agriculture and animal husbandry experiment. The missing data in experimental design makes the information that we get less complete. In this research, the missing data was estimated with Yates method and Expectation Maximization (EM) algorithm. The basic concept of the Yates method is to minimize sum square error (JKG), meanwhile the basic concept of the EM algorithm is to maximize the likelihood function. This research applied Balanced Lattice Design with 9 treatments, 4 replications and 3 group of each repetition. Missing data estimation results showed that the Yates method was better used for two of missing data in the position on a treatment, a column and random, meanwhile the EM algorithm was better used to estimate one of missing data and two of missing data in the position of a group and a replication. The comparison of the result JKG of ANOVA showed that JKG of incomplete data larger than JKG of incomplete data that has been added with estimator of data. This suggest  thatwe need to estimate the missing data.

Nonparametric spectral analysis with missing data via the EM algorithm

2005 ◽  
Vol 15 (2) ◽  
pp. 191-206 ◽  
Author(s):  
Yanwei Wang ◽  
Petre Stoica ◽  
Jian Li ◽  
Thomas L. Marzetta
Keyword(s):  
Spectral Analysis ◽  
Missing Data ◽  
Em Algorithm ◽  
The Em Algorithm

scholarly journals The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data

2016 ◽  
Vol 12 (1) ◽  
pp. 65-77
Author(s):  
Michael D. Regier ◽  
Erica E. M. Moodie
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Em Algorithm ◽  
Optimal Solution ◽  
Bias Reduction ◽  
Model Parameters ◽  
Finite Sample ◽  
Imperfect Data ◽  
The Em Algorithm ◽  
Finite Sample Properties

Abstract We propose an extension of the EM algorithm that exploits the common assumption of unique parameterization, corrects for biases due to missing data and measurement error, converges for the specified model when standard implementation of the EM algorithm has a low probability of convergence, and reduces a potentially complex algorithm into a sequence of smaller, simpler, self-contained EM algorithms. We use the theory surrounding the EM algorithm to derive the theoretical results of our proposal, showing that an optimal solution over the parameter space is obtained. A simulation study is used to explore the finite sample properties of the proposed extension when there is missing data and measurement error. We observe that partitioning the EM algorithm into simpler steps may provide better bias reduction in the estimation of model parameters. The ability to breakdown a complicated problem in to a series of simpler, more accessible problems will permit a broader implementation of the EM algorithm, permit the use of software packages that now implement and/or automate the EM algorithm, and make the EM algorithm more accessible to a wider and more general audience.

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 Estimation of the Average Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1694
Author(s):  
José Antonio Roldán-Nofuentes ◽  
Saad Bouh Regad
Keyword(s):  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Diagnostic Test ◽  
Gold Standard ◽  
Maximum Likelihood Method ◽  
Disease Prevalence ◽  
Kappa Coefficient ◽  
Likelihood Method ◽  
Simulation Experiments ◽  
Asymptotic Behaviors

The average kappa coefficient of a binary diagnostic test is a measure of the beyond-chance average agreement between the binary diagnostic test and the gold standard, and it depends on the sensitivity and specificity of the diagnostic test and on disease prevalence. In this manuscript the estimation of the average kappa coefficient of a diagnostic test in the presence of verification bias is studied. Confidence intervals for the average kappa coefficient are studied applying the methods of maximum likelihood and multiple imputation by chained equations. Simulation experiments have been carried out to study the asymptotic behaviors of the proposed intervals, given some application rules. The results obtained in our simulation experiments have shown that the multiple imputation by chained equations method provides better results than the maximum likelihood method. A function has been written in R to estimate the average kappa coefficient by applying multiple imputation. The results have been applied to the diagnosis of liver disease.

Multi-User Detection Analysis via Aitken-δ2 EM Acceleration Algorithm

2011 ◽  
Vol 225-226 ◽  
pp. 284-288
Author(s):  
Jin Long Xian ◽  
Jian Wu Li
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Iterative Algorithm ◽  
Convergence Speed ◽  
Speed Of Convergence ◽  
Detection Analysis ◽  
The Em Algorithm ◽  
Aitken Method

The EM iterative algorithm is commonly used in recent years for missing data, which has the character of easy and popular applicability. But the EM algorithm has a fatal weakness that the convergence speed is slowly; Acceleration of the EM algorithm using the Aitken method is proposed in order to solve this problem.In Multi-user Detection, via this accelerated algorithm, we get a good performance which trends to ML performance, and compared its speed of convergence with the EM algorithm that Aitken-acceleration algorithm has faster convergence than the standard EM algorithm, and we also illustrate the performance of simulation.

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