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Abstract
Background and ObjectiveObservational studies and experiments in medicine, pharmacology and agronomy are often concerned with assessing whether different methods/raters produce similar values over the time when measuring a quantitative variable. This article aims to describe the statistical package lcc, for are, that can be used to estimate the extent of agreement between two (or more) methods over the time, and illustrate the developed methodology using three real examples.MethodsThe longitudinal concordance correlation, longitudinal Pearson correlation, and longitudinal accuracy functions can be estimated based on fixed effects and variance components of the mixed-effects regression model. Inference is made through bootstrap confidence intervals and diagnostic can be done via plots, and statistical tests.ResultsThe main features of the package are estimation and inference about the extent of agreement using numerical and graphical summaries. Moreover, our approach accommodates both balanced and unbalanced experimental designs or observational studies, and allows for different within-group error structures, while allowing for the inclusion of covariates in the linear predictor to control systematic variations in the response. All examples show that our methodology is flexible and can be applied to many different data types.ConclusionsThe lcc package, available on the CRAN repository, proved to be a useful tool to describe the agreement between two or more methods over time, allowing the detection of changes in the extent of agreement. The inclusion of different structures for the variance-covariance matrices of random effects and residuals makes the package flexible for working with different types of databases.
Highlights
Agreement indices are generally used when the same experimental unit is measured by at least two methods or observers (King et al, 2007)
In biomedical sciences it is often necessary to study the reproducibility of continuous measurements made using specific diagnostic tools or methods, and that measurements can be taken over the time on the subjects of interest, such as in the studies of Pandit, Chair & Schuller (2019); Shinar et al (2019) and Loecher et al (2019)
The concordance correlation coefficient (CCC) introduced by Lin (1989) is a statistic commonly used to measure the agreement between methods when the response is continuous
Summary
Agreement indices are generally used when the same experimental unit is measured by at least two methods or observers (King et al, 2007). The concordance correlation coefficient (CCC) introduced by Lin (1989) is a statistic commonly used to measure the agreement between methods when the response is continuous. Observational studies and experiments in medicine, pharmacology and agronomy are often concerned with assessing whether different methods/raters produce similar values over the time when measuring a quantitative variable. This article aims to describe the statistical package lcc, for are, that can be used to estimate the extent of agreement between two (or more) methods over the time, and illustrate the developed methodology using three real examples. Conclusions: The lcc package, available on the CRAN repository, proved to be a useful tool to describe the agreement between two or more methods over time, allowing the detection of changes in the extent of agreement. The inclusion of different structures for the variance-covariance matrices of random effects and residuals makes the package flexible for working with different types of databases
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