%0 Journal Article %1 Carrasco2013 %A Carrasco, Josep L %A Phillips, Brenda R %A Puig-Martinez, Josep %A King, Tonya S %A Chinchilli, Vernon M %D 2013 %J Computer methods and programs in biomedicine %K Algorithms AreaUnderCurve BloodPressure BloodPressureDetermination BloodPressureDetermination:methods BloodPressureDetermination:standards ComputerSimulation ConfidenceIntervals Humans Hydrocortisone Hydrocortisone:blood LinearModels ObserverVariation ReproducibilityofResults Software StatisticsasTopic %N 3 %P 293-304 %R 10.1016/j.cmpb.2012.09.002 %T Estimation of the concordance correlation coefficient for repeated measures using SAS and R. %U http://www.ncbi.nlm.nih.gov/pubmed/23031487 %V 109 %X The concordance correlation coefficient is one of the most common approaches used to assess agreement among different observers or instruments when the outcome of interest is a continuous variable. A SAS macro and R package are provided here to estimate the concordance correlation coefficient (CCC) where the design of the data involves repeated measurements by subject and observer. The CCC is estimated using U-statistics (UST) and variance components (VC) approaches. Confidence intervals and standard errors are reported along with the point estimate of the CCC. In the case of the VC approach, the linear mixed model output and variance components estimates are also provided. The performance of each function is shown by means of some examples with real data sets. %@ 1872-7565; 0169-2607

@article{Carrasco2013, abstract = {The concordance correlation coefficient is one of the most common approaches used to assess agreement among different observers or instruments when the outcome of interest is a continuous variable. A SAS macro and R package are provided here to estimate the concordance correlation coefficient (CCC) where the design of the data involves repeated measurements by subject and observer. The CCC is estimated using U-statistics (UST) and variance components (VC) approaches. Confidence intervals and standard errors are reported along with the point estimate of the CCC. In the case of the VC approach, the linear mixed model output and variance components estimates are also provided. The performance of each function is shown by means of some examples with real data sets.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Carrasco, Josep L and Phillips, Brenda R and Puig-Martinez, Josep and King, Tonya S and Chinchilli, Vernon M}, biburl = {https://www.bibsonomy.org/bibtex/2e4f20bd0b7d12c348db58d635f598892/jepcastel}, city = {Biostatistics Unit, Public Health Department, University of Barcelona, Barcelona, Spain. Electronic address: jlcarrasco@ub.edu.}, doi = {10.1016/j.cmpb.2012.09.002}, interhash = {59d88f2cd7e1569b9b26b91fe98b5d85}, intrahash = {e4f20bd0b7d12c348db58d635f598892}, isbn = {1872-7565; 0169-2607}, issn = {1872-7565}, journal = {Computer methods and programs in biomedicine}, keywords = {Algorithms AreaUnderCurve BloodPressure BloodPressureDetermination BloodPressureDetermination:methods BloodPressureDetermination:standards ComputerSimulation ConfidenceIntervals Humans Hydrocortisone Hydrocortisone:blood LinearModels ObserverVariation ReproducibilityofResults Software StatisticsasTopic}, month = {3}, note = {7156<m:linebreak></m:linebreak>CI: Copyright (c) 2012; JID: 8506513; 2011/12/29 [received]; 2012/09/04 [revised]; 2012/09/04 [accepted]; aheadofprint;<m:linebreak></m:linebreak>Mesures de concordància; CCC; SAS; R; Dades longitudinals}, number = 3, pages = {293-304}, pmid = {23031487}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Estimation of the concordance correlation coefficient for repeated measures using SAS and R.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23031487}, volume = 109, year = 2013 }