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2016 | OriginalPaper | Chapter

5. Adaptive Regression Modeling of Multivariate Continuous Outcomes in SAS

Authors : George J. Knafl, Kai Ding

Published in: Adaptive Regression for Modeling Nonlinear Relationships

Publisher: Springer International Publishing

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Abstract

This chapter provides a description of how to use the genreg macro for adaptive regression modeling in the case of multivariate continuous outcomes treated as multivariate normally distributed. Data in wide format are often used in multivariate outcome modeling with outcome measurements under different conditions (for example, ages for the dental measurement data analyzed in Chap. 4) in separate variables (columns) and with one observation (row) per matched set of related outcome measurements (for example, the matched sets of the dental measurement data correspond to dental measurements for different children). However, mixed modeling as used in this chapter to analyze multivariate outcome data requires that the data be converted to long format with all outcome measurements in the same variable, an extra variable to identify the measurement condition, and one observation for each outcome measurement, and so an example of such a conversion is presented. Example analyses are provided of marginal modeling of means and variances for multivariate outcomes using either order 1 autoregressive or exchangeable correlations with parameter estimates based on either maximum likelihood (ML) or on generalized estimating equations (GEE). Example analyses are also presented of transition modeling and general conditional modeling of means and variances for multivariate outcomes. Example residual analyses are presented as well, together with sensitivity analyses to assess the impact of outlying observations. Practice exercises are provided for conducting analyses similar to those presented in Chaps. 4 and 5.

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Literature
go back to reference Khattree, R., & Naik, D. N. (1999). Applied multivariate statistics with SAS software (2nd ed.). Cary, NC: SAS Institute. Khattree, R., & Naik, D. N. (1999). Applied multivariate statistics with SAS software (2nd ed.). Cary, NC: SAS Institute.
go back to reference Littell, R. C., Milliken, G. A., Stroup, R., Wolfinger, R. D., & Schabenberger, O. (2006). SAS for mixed models (2nd ed.). Cary, NC: SAS Institute. Littell, R. C., Milliken, G. A., Stroup, R., Wolfinger, R. D., & Schabenberger, O. (2006). SAS for mixed models (2nd ed.). Cary, NC: SAS Institute.
go back to reference Littell, R. C., Stroup, W. W., & Freund, R. J. (2002). SAS for linear models (4th ed.). Cary, NC: SAS Institute. Littell, R. C., Stroup, W. W., & Freund, R. J. (2002). SAS for linear models (4th ed.). Cary, NC: SAS Institute.
Metadata
Title
Adaptive Regression Modeling of Multivariate Continuous Outcomes in SAS
Authors
George J. Knafl
Kai Ding
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-33946-7_5

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