Computer methods and programs in biomedicine, 2013-03, Vol.109 (3), p.293-304
2013
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- Autor(en) / Beteiligte
- Titel
- Estimation of the concordance correlation coefficient for repeated measures using SAS and R
- Ist Teil von
- Computer methods and programs in biomedicine, 2013-03, Vol.109 (3), p.293-304
- Ort / Verlag
- Kidlington: Elsevier Ireland Ltd
- Erscheinungsjahr
- 2013
- Quelle
- MEDLINE
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0169-2607eISSN: 1872-7565DOI: 10.1016/j.cmpb.2012.09.002Titel-ID: cdi_proquest_miscellaneous_1323257098
- Format
- –
- Schlagworte
- Algorithms, Area Under Curve, Biological and medical sciences, Blood Pressure, Blood Pressure Determination - methods, Blood Pressure Determination - standards, Computer Simulation, Concordance correlation coefficient, Confidence Intervals, Humans, Hydrocortisone - blood, Internal Medicine, Linear mixed models, Linear Models, Medical sciences, Observer Variation, Other, Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects), Repeated measures, Reproducibility of Results, Software, Statistics as Topic, Technology. Biomaterials. Equipments. Material. Instrumentation, U-statistics, Variance components