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The 130th Annual Meeting of APHA |
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The 130th Annual Meeting of APHA |
Jean Orelien, MS, Analytical Sciences, Inc., 2605 Meridian Pkwy, Durham, NC 27713, (919)313-7598, jorelien@asciences.com and Lloyd J. Ewards, Phd, Biostatistics, University of North Carolina, CB # 7420, Chapel Hill, NC 27599.
Linear mixed models (LMM) is one of the methods commonly used to analyze data from longitudinal studies. However, few tools are readily available to assess model adequacy in the LMM. Likelihood ratio tests and statistics such as the Akaike information criterion (AIC) or the Schwartz Information criteria (SIC) that are used to assess goodness of fit require that several models be fitted to the same data. Vonesh et al. (1996) proposed a more intuitive statistic denoted the concordance correlation coefficient (CCC) that can be interpreted as a measure of agreement between the predicted and observed values. The performance of CCC has not been demonstrated either analytically or in simulation. To that end, we conducted a simulation with the goal of determining: a) the extent to which CCC can detect the adequacy of the conditional mean (fixed effect terms) given that the correct covariance structure for the random effect is specified and b) the extent to which CCC can detect adequacy of the covariance structure of the random effects given that the conditional mean specified is the correct one. Results from our simulations show that CCC performs reasonably well.
Learning Objectives:
Keywords: Statistics,
Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.