lcc: an R package to estimate the concordance correlation, Pearson correlation and accuracy over time
| Autor: | Silvio Sandoval Zocchi, Clarice Garcia Borges Demétrio, Rafael de Andrade Moral, Thiago de Paula Oliveira, John Hinde |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Heteroscedasticity
Bioinformatics Inference lcsh:Medicine 01 natural sciences General Biochemistry Genetics and Molecular Biology 030218 nuclear medicine & medical imaging 010104 statistics & probability 03 medical and health sciences symbols.namesake 0302 clinical medicine Covariate Statistics 0101 mathematics Accuracy Mathematics Statistical hypothesis testing Longitudinal data General Neuroscience Design of experiments lcsh:R Regression analysis General Medicine Bootstrap procedures Precision Random effects model Pearson product-moment correlation coefficient REAMOSTRAGEM BOOTSTRAP Extent of agreement symbols General Agricultural and Biological Sciences Polynomial mixed-effects regression model |
| Zdroj: | PeerJ Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP Oliveira, T P, Moral, R A, Zocchi, S S, Demetrio, C G B & Hinde, J 2020, ' lcc: an R package to estimate the concordance correlation, Pearson correlation and accuracy over time ', PeerJ . https://doi.org/10.7717/peerj.9850 PeerJ, Vol 8, p e9850 (2020) |
| ISSN: | 2167-8359 |
| Popis: | Background and Objective 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. Methods The 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. Results The 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. 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|