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Software


This is a brief overview. No warranty or guarantee of any kind is offered with this software. The software can be freely used for non-commercial purposes and can be freely distributed for non-commercial purposes only. For more information or to download the software, click on individual items.

ALR: The SAS PROC GENMOD code used to obtain alternating logistic regression results from the green tobacco sickness data presented in Preisser, Arcury, and Quandt (2003, Am J Epidemiol, 158:495-501) is provided. Program statements are provided for the 3 level model presented in Tables 3 and 4 of the paper. Also, code for a more complicated 4-parameter model (expression 6 in the paper) making use of the ZDATA option on the repeated statement of SAS PROC GENMOD is provided. Alternatively, the sas program makezmat.sas provides a simple example showing how SAS PROC SQL may be used to create the Z-matrix for the log pairwise odds ratio model.

CRTFASTGEEPWR: A SAS macro CRTFASTGEEPWR implements a fast, non-simulation based analytical power procedure to compute power for cluster randomized trials (CRTs) based on generalized estimating equations as described in the open access article by Zhang, Preisser, Turner, Rathouz, Toles and Li (2023). The macro code, slide deck that provides an overview with examples, readme file containing explanations of provided SAS and output files with numerous applications of the macro to complete and incomplete stepped wedge designs are provided. The SAS macro can also be used to compute power for parallel and cluster-crossover CRT designs. Versions 2.02 (2023) and 2.04 (2024) by Ying Zhang.

GEEORD: A SAS macro GEEORD, as described by Gao X, Schwartz T, Preisser JS, and Perin J, for the analysis of ordinal responses with repeated measures through a regression model that flexibly allows the proportional odds assumption to apply (or not) separately for each explanatory variable. The macro additionally provides relevant tests of the proportional odds assumption.
Version 1.02 by John Preisser and Jamie Perin. Modifications in Version 1.03 by Todd Schwartz (June 10, 2017).

GEEDIAG: A SAS macro GEEDIAG, originally described by Hammill & Preisser (Comput. Statist Data Analysis 2006;51:1197-1212), applies generalized estimating equations (Liang & Zeger, 1986) for estimation of population-averaged models. It includes the following in addition to "standard" options: (1) GEE1 regression diagnostics based on Preisser & Qaqish (1996, Biometrika 83, 551-62) inlcuding DFBETA and Cook's Distance based upon cluster and observation deletion; (2) bias-corrected covariance estimates for marginal mean regression parameters (Mancl & DeRouen, 2001, Bioometrics, 57, 126-134); (3) time-variant, heterogeneous scale parameter specification; and (4) several options to produce output SAS datasets for post-modeling calculations.
Version 1.02 by Bradley Hammill and John Preisser (2005); Version 1.05 by John Preisser (2018).

GEECORR: GEECORR SAS macro v. 2.0 for binary data based upon the generalized estimating equations procedure of Prentice (1988, Biometrics, 44, 1033-1048) with the more detailed fitting algorithm described therein as an alternative choice of model fitting procedure. The macro provides estimates of parameters and their standard errors for regression coefficients in the marginal mean model and in the within-cluster pairwise correlation model. Link function options are logit (default), identity, and log for the marginal mean model and identity (default), log, logit, and Fisher's Z link options for the pairwise correlation model. The macro also provides (1) bias-corrected covariance estimates that extend those of Mancl & DeRouen (2001, Biometrics, 57, 126-134) to encompass correlation parameters as well as mean parameters; (2) deletion diagnostics for clusters and observations (DBETA, Cook's Distance) proposed by Preisser and Perin (Statistics and Computing, 2007, 17, 381-393) that extend the GEE1 diagnostics of Preisser and Qaqish (1996, Biometrika, 83, 551-562) to include correlation model parameters; and (3) correlation selection criteria.
Version 1.01 by Richard Zink (2003). Modifications: Version 1.04 by John Preisser and Bing Lu (2004); Version 1.05 by Jamie Perin (2005); Version 2.0 by Tracie Shing (2020)

GEEMAEE: The GEEMAEE SAS macro for clustered binary, continuous and count outcomes implements the "usual" GEE for estimation of the marginal mean regression parameters and matrix-adjusted estimating equations or MAEE of Preisser, Lu and Qaqish (2008, Statistics in Medicine 27:5764-5785) for estimation of the within-cluster correlation model parameters; MAEE implements finite-sample bias corrections (for small number of clusters) to improve the estimation of correlation parameters in terms of reduced bias and improved confidence interval coverage relative to the approach of Sharples and Breslow (1992, Journal of Statistical Computation and Simulation 42:1-20) and the uncorrected estimating equations of Prentice (1988, Biometrics, 44, 1033-1048). Link function options are identity, log, logit and probit for the marginal mean model and identity, log, logit, and Fisher's Z link options for the pairwise correlation model. In addition to standard errors based on the "usual" sandwich variance estimators of Prentice, the macro provides standard errors from three different bias-corrected sandwich variance estimators for both the marginal mean model and within-cluster pairwise correlation models. Other files include simulated sas datasets and sas code for a stepped wedge cluster randomized trial. A full description of the GEEMAEE SAS macro is provided by Zhang et al. (2023, Computer Methods and Programs in Biomedicine. 230:107362. doi: 10.1016/j.cmpb.2023.107362.)
Version 1.01 by Bing Lu and John Preisser (2005); Modifications: Versions 1.04 (2021), 2.0 (2022) and 2.01 (2023) by Ying Zhang and John Preisser.

Marginalized Mixture Models for Count Outcomes: A file containing supplementary material, including SAS Proc NLMIXED code for fitting the models as well as information on selection of starting values for parameters, for the article "Marginalized mixture models for count data from multiple source populations" by Habtamu K. Benecha, Brian Neelon, Kimon Divaris and John S. Preisser, published in the Journal of Statistical Distributions and Applications (2017), 4:3.

ORTH/ALR regression diagnostics (link to location on B. Qaqish's software page): The SAS/IML macro applies alternating logistic regressions or (optionally) a generalization of the procedure called orthogonalized residuals (Qaqish, Zink, Preisser, Scandinavian Journal of Statistics, 2012; 39:515-527) for the regression analysis of correlated binary data. It also produces cluster-deletion diagnostics (e.g., Cooks Distance, DFBETA) for both the marginal mean (logistic) model and the within-cluster association (log odds ratio) model (Preisser, By, Perin, Qaqish, Biometrical Journal, 2012; 54:701-715). By Kunthel By, John Preisser, Jamie Perin, Richard Zink and Bahjat Qaqish.

ORTH.ordinal: The SAS/IML macro applies an adaptation of generalized estimating equations for modeling an ordinal outcome under a proportional odds cumulative logits model, and the within-cluster association modeled based on global pairwise odds ratio with an implementation of orthogonalized residuals (Perin J, Preisser JS, Qaqish B, Phillips C. 2014. Regression analysis of correlated ordinal data using orthogonalized residuals. Biometrics 70, 902-909.) The macro by Jamie Perin extends ORTH software for binary data originally written by Richard Zink.

ORTH.macro.MMEE: The SAS/IML macro ORTHRES.macro.MMEE adapts the finite-sample bias corrections of Preisser, Lu and Qaqish (2008) to orthogonalized residuals analysis, an implementation of GEE and alternating logistics regressions, to the regression analysis of correlated binary data (Perin J, Preisser J. 2017. Alternating logistic regressions with improved finite sample properties. Biometrics 73, 696-705). The macro was written by Jamie Perin.

Go To Bahjat F. Qaqish's Software Page


last modified October 20, 2023