Abstract
PROC MIXED has become a standard tool for analyzing repeated measures data. Its popularity results from a wide choice of correlated error models compared to other software, e.g. PROC GLM. However, PROC MIXED's versatility comes at a price. Users must take care. Problems may result from MIXED defaults. These include: questionable criteria for selecting correlated error models; starting values that may impede REML estimation of covariance components; and biased standard errors and test statistics. Problems may be induced by inadequate design. This paper is a survey of current knowledge about mixed model methods for repeated measures. Examples are presented using PROC MIXED to demonstrate these problems and ways to address them.
Keywords
Repeated measures experiment; mixed model analysis; correlated error models
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Stroup, Walter W.
(1999).
"ON USING PROC MIXED FOR LONGITUDINAL DATA,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1259
ON USING PROC MIXED FOR LONGITUDINAL DATA
PROC MIXED has become a standard tool for analyzing repeated measures data. Its popularity results from a wide choice of correlated error models compared to other software, e.g. PROC GLM. However, PROC MIXED's versatility comes at a price. Users must take care. Problems may result from MIXED defaults. These include: questionable criteria for selecting correlated error models; starting values that may impede REML estimation of covariance components; and biased standard errors and test statistics. Problems may be induced by inadequate design. This paper is a survey of current knowledge about mixed model methods for repeated measures. Examples are presented using PROC MIXED to demonstrate these problems and ways to address them.
