Oliveira Thiago P, Moral Rafael A, Zocchi Silvio S, Demetrio Clarice G B, Hinde John
School of Mathematics, Statistics and Applied Mathematics, NUI Galway, Galway, Ireland.
The Insight Centre for Data Analytics, NUI Galway, Galway, Ireland.
PeerJ. 2020 Sep 17;8:e9850. doi: 10.7717/peerj.9850. eCollection 2020.
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.
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.
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.
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.
医学、药理学和农学领域的观察性研究与实验常常关注评估不同方法/评估者在测量定量变量时随时间推移是否产生相似的值。本文旨在描述用于评估重复性的统计软件包lcc，它可用于估计两种（或更多）方法随时间推移的一致性程度，并通过三个实际例子来说明所开发的方法。
纵向一致性相关性、纵向皮尔逊相关性和纵向准确性函数可基于混合效应回归模型的固定效应和方差分量进行估计。通过自助置信区间进行推断，可通过绘图和统计检验进行诊断。
该软件包的主要特点是使用数值和图形摘要对一致性程度进行估计和推断。此外，我们的方法适用于平衡和不平衡的实验设计或观察性研究，并允许不同的组内误差结构，同时允许在线性预测器中纳入协变量以控制响应中的系统变化。所有例子都表明我们的方法灵活且可应用于许多不同的数据类型。
CRAN存储库中提供的lcc软件包被证明是描述两种或更多方法随时间推移的一致性的有用工具，能够检测一致性程度的变化。纳入随机效应和残差的方差协方差矩阵的不同结构使该软件包在处理不同类型的数据库时具有灵活性。
