OrdinalTvem code Public

Supporting code for Dziak, J. J., Li, R., Zimmerman, M. A., & Buu, A. (2014). Time-varying effect models for ordinal responses with applications in substance abuse research. Statistics in Medicine, 33(29): 5126–5137. See  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4227951/

README

OrdinalTvem & OrdinalTvemLoop macros (Version 2.0)

Authors

John DZIAK, Runze LI, and Anne BUU

Description

The OrdinalTvem macro fits a TVEM model to (potentially longitudinal) ordinal data. The OdinalTvemLoop macro repeatedly calls OrdinalTvem for model selection. The underlying model for OrdinalTvem and OrdinalTvemLoop is further explained in the following publication:

Dziak, J. J., Li, R., Zimmerman, M. A., & Buu, A. (2014). Time-varying effect models for ordinal responses with applications in substance abuse research. Statistics in Medicine, 33: 5126-5137. doi: 10.1002/sim.6303

The full text is available here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4227951/

Usage

These macros have been tested on SAS version 9.2. Descriptions of macro parameters are included in the source files.

File Manifest / Data Structure

  • OrdinalTvem.sas: The OrdinalTvem macro
  • OrdinalTvemLoop.sas The OrdinalTvemLoop macro

Acknowledgements & References

OrdinalTvem uses code which was written by Xianming TAN to construct the spline bases and which is also found in the MixTVEM macro. We fit a varying-coefficient (time-varying effect) model for ordinal data, using either a linear model or a proportional odds logistic model, and an unpenalized B-spline approach. See:

  • Eilers, P.H.C. and Marx, B.D. (1996). Flexible smoothing using B-splines and penalized likelihood. Statistical Science 11(2): 89-121.
  • Hastie, T., & Tibshirani, R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society, Series B, 55, 757-796.
  • Shiyko, M. P., Lanza, S. T., Tan, X., Li, R., Shiffman, S. (2012). Using the Time-Varying Effect Model (TVEM) to Examine Dynamic Associations between Negative Affect and Self Confidence on Smoking Urges: Differences between Successful Quitters and Relapsers. Prevention Science, 13, 288-299.
  • Ramsay, J., Hooker, G., & Graves, S. (2009). Functional Data Analysis with R and MATLAB. New York: Springer.
  • SAS Institute Inc. (2011). SAS/STAT (c) 9.3 user's guide: The GLIMMIX procedure (chapter)). Cary, NC: SAS Institute Inc.
  • Tan, X., Shiyko, M. P., Li, R., Li, Y., & Dierker, L. (2011, November 21). A time-varying effect model for intensive longitudinal data. Psychological Methods. Advance online publication. doi: 10.1037/a0025814.

Copyright & License

Copyright (c) 2014 The Pennsylvania State University

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either version 2 of
the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.

Items in this Work

User Activity Date
User John Dziak has attached README.txt to OrdinalTvem code about 2 months ago
User Seth R Erickson has updated OrdinalTvem code about 2 months ago
User John Dziak has attached OrdinalTvemLoop.sas to OrdinalTvem code 2 months ago
User John Dziak has attached OrdinalTvem.sas to OrdinalTvem code 2 months ago
User John Dziak has deposited OrdinalTvem code 2 months ago