Abstract:
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Hundreds of method comparison studies for evaluating agreement between two or more methods of measuring a continuous or categorical response are published each year in biomedical disciplines. This course will discuss statistical methodologies for analysis of such studies. For continuous response, we will focus on a new approach that involves modeling of data by a mixed-effects model and performing inference on measures of agreement, such as limits of agreement, concordance correlation, and total deviation index. Besides providing a more informative analysis than the popular approach of Bland and Altman (1986, Lancet) that has over 25000 citations, this approach offers a unified framework for analyzing a variety of data, including paired measurements, repeated measurements, and longitudinal data and data from multiple methods, which will be specifically considered in the course. For categorical response, we will focus on kappa coefficients and related measures. A detailed case-study will be presented for illustrating the analysis of each data type. Relevant R software code will be provided. The course is based on a new Wiley monograph, Measuring Agreement, by the instructors, and presumes knowledge of the basics of statistical inference, regression modeling, and R. Some familiarity with mixed-effects models is beneficial but not necessary.
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