A heteroscedastic measurement error model for method comparison data with replicate measurements

Nawarathna, Lakshika S.; Choudhary, Pankaj

doi:10.1002/sim.6424

Cited by 24 publications

(48 citation statements)

References 29 publications

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“…When considering the two approaches, the method of using the true mean as the covariate is much simpler than the method of using BLUP as the covariate, because of the complexity of calculating the BLUP than calculating the mean. However, method of using BLUP is more accurate than the other [14].…”

Section: Using Blup As the Covariatementioning

confidence: 94%

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Assessing inter-rater agreement between multiple medical instruments with heteroscedastic measurements

Dassanayake¹,

Nawarathna²

2017

J Med Stat Inform

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Background: In clinical medicine, agreement evaluation plays a major role in determining the compatibility and the accuracy of newly introduced methods with pre-existing methods. These methods may be assays, clinical observers, medical devices etc. It is vital to assess the compatibility and the accuracy of these newly introduced techniques because they deal with the measurements of the human body, such as blood pressure, cholesterol level, heart rate etc. In practice, agreement evaluation is carried out among two methods of measurements and deals with the data that are homoscedastic. The main objective of this study is to extend the standard mixed model to allow the error variances to depend on magnitude of measurement and evaluate agreement between multiple methods assuming the new model, taking the heteroscedasticity into account. Methods: In order to assess the agreement, there are two typical steps in method comparison studies. The first step is to model the data using the Heteroscedastic mixed effects model. The model fitting is carried out by using two main approaches, namely the mean method and the best linear unbiased predictor method. After fitting the model for the second step, the agreement evaluation is carried out using Concordance correlation coefficient and Total deviation index. Results: The illustrative example contained five methods of measurements and was with heteroscedastic measurements. First, the model fitting was carried out according to the two approaches and the resulting parameters were almost identical. After the model fitting, the agreement evaluation was performed. According to the values resulted from the agreement measurements, it is clear that all five methods agree sufficiently well with the reference method. Conclusions: The proposed model can be used to model the method comparison data with heteroscedastic measurements with multiple methods of measurements as well as balanced and unbalanced data designs. Under the proposed model, the agreement evaluation methodology for comparing multiple methods is also developed taking heteroscedasticity into account.

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Section: Using Blup As the Covariatementioning

confidence: 94%

“…In order to assess the agreement between the assays, the first step is to model the data. The Linear mixed effects model is most commonly used in modeling the method comparison data [6][7][8][9][10][11][12][13][14][15]. Because of the flexibility in modeling of within subject dependence, linear mixed models are popular.…”

Section: Introductionmentioning

confidence: 99%

Assessing inter-rater agreement between multiple medical instruments with heteroscedastic measurements

Dassanayake¹,

Nawarathna²

2017

J Med Stat Inform

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“…Therefore, in the present study, a multiple heteroscedastic mixed effects model is proposed to model the data and this fitted model is then used to assess the agreement between multiple methods of measurements. This methodology has been indicated by Nawarathna et al [24,25] as a way of measuring agreement in method comparison studies with heteroscedastic measurements.…”

Section: Fig 6 Variation Of Infected Area With Gendermentioning

confidence: 99%

Non-surgical Management of Large Periapical Lesions with the Agreement Evaluation of the Methods

Lakmali

Nawarathna

Fonseka

2019

AJPAS

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Aim:The aim of this study is to evaluate the agreement between three routinely used non-surgical management techniques for large periapical lesions namely the treatments with Calcium hydroxide, Mineralo-Trioxide Aggregate and Bio-dentine. Methods: Data was collected from 60 patients at the Department of Restorative Dentistry, Faculty of Dental Sciences, University of Peradeniya. The variables age, gender and area of the infected region before and after the treatment and the treatment type were considered. Two homoscedastic and heteroscedastic Mixed-effects models were fitted and the agreement between three treatments were assessed using Concordance Correlation Coefficient (CCC) and Total Deviation Index (TDI). Results: CCC value calculated for treatment types 1 & 2, 1 & 3 and 2 & 3 are (0.905, 0.909, 0.874) for homoscedastic model and (0.989, 0.990, 0.975) for heteroscedastic model. Further, corresponding TDI values for homoscedastic and heteroscedastic models are (3.148, 4.390, 1.647) and (2.963, 4.388, 1.457) respectively.Conclusions: Since all the CCC values are close to 1 and TDI values are low, there is a strong agreement between all three treatments and hence they be used interchangeably. Moreover, the agreement between Lakmali et al.; AJPAS, 5(1): 1-11, 2019; Article no.AJPAS.50952 2 Treatments with Calcium hydroxide and Bio-dentine is higher compared to the agreements between the other treatments. (i.e., Calcium hydroxide with Mineralo-Trioxide Aggregate and Biodentine with Mineralo-Trioxide Aggregate). Original Research Article

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“…Several methods have been proposed to model the method comparison data, among them the regression-based model doi: 10.7243/2053-7662-5-3 [6,9] and the standard mixed-effects models [24] are the most important. A standard mixed-effects model is used predominantly to model method comparison data [2,5,25,26,30]. The validation of the mixed-effects model highly depends on assumptions such as constant error variance (homoscedasticity) and normality of error terms.…”

Section: Introductionmentioning

confidence: 99%

“…If heteroscedasticity is not considered, the resulting agreement evaluation will be misleading. Several models have been proposed in [25,26] to analyze the heteroscedastic measurements with replicates. But all these models do not accommodate non-replicated measurements.…”

Section: Introductionmentioning

confidence: 99%

A statistical method for assessing agreement between two methods of heteroscedastic clinical measurements using the copula method

Aravind¹,

Nawarathna²

2017

J Med Stat Inform

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Background: A method comparison study is a topic of considerable interest in health and biomedical-related fields. The use of this study is to compare a new method with a standard established method. There are two typical steps in method comparison studies, namely modeling the data set and using the fitted model to analyze method comparison data. Methods: As a usual practice to model method comparison data, many recommend a mixed-effects model which assumes constant error variance (homoscedasticity) and normality of error terms. However, these assumptions are generally violated in practice. Thus, in this study, our main goal is to propose a copula based model to deal with non-replicated method comparison data. Moreover, a simulation procedure is carried out to validate the proposed model by means of different copula models. Results: Results indicate that the Normal and Gumbel copulas give more accurate results in terms of bias, mean-squared error and coverage probability values of estimators. Further it is confirmed that the accuracy of model increases with the Kendall tau (τ) correlation between methods and the number of observations. Furthermore, the proposed methodology is illustrated by analyzing finger and arm systolic blood pressure data. Besides the Total Deviation Index (TDI) and Concordance Correlation Coefficient (CCC) values are used to check the agreement between the two methods. Conclusion:The proposed model based on the Copula method can be used to model the method comparison data with balanced and unbalanced data designs.

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A heteroscedastic measurement error model for method comparison data with replicate measurements

Cited by 24 publications

References 29 publications

Assessing inter-rater agreement between multiple medical instruments with heteroscedastic measurements

Assessing inter-rater agreement between multiple medical instruments with heteroscedastic measurements

Non-surgical Management of Large Periapical Lesions with the Agreement Evaluation of the Methods

A statistical method for assessing agreement between two methods of heteroscedastic clinical measurements using the copula method

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