Sas 9.2 proc mixed

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SAS 9.2 Proc Mixed is a statistical procedure within the SAS software suite that is used for the analysis of linear mixed models. It provides a flexible and powerful tool for modeling data with correlated observations, such as longitudinal or clustered data. Proc Mixed allows users to specify random effects and covariance structures, making it useful for a wide range of analytical applications.

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Proc mixed SAS 9-2 SAS 9.2 proc SAS PROC MIXED PROC MIXED SAS (SAS 9.2: SAS 9.4

2 protocols using sas 9.2 proc mixed

Linear Growth Model of Depression Therapy

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We conducted linear growth models in SAS 9.2 Proc Mixed.⁴⁷ The base model treated number of therapy sessions since beginning therapy as the unit of time, which included baseline, 4 sessions after baseline, 8 sessions after baseline, and 12 sessions after baseline (coded 0, 4, 8, & 12, respectively). The intercept represented depression at baseline and the time effect (i.e., session number) as the change in depression per therapy session. A treatment (CBT vs. CA-CBT) main effect and treatment by time interaction were also included representing treatment differences at baseline (the main effect) and treatment differences in the rate of change in depression (treatment X time). Missing HDRS scores were treated as missing at random and automatically accommodated in the growth model. All models treated the intercept and time as random (with the two random parameters freely covarying), and the parameters were estimated using restricted maximum likelihood. An alpha of .05 two-tailed was adopted for all analyses.

Hwang W.C., Myers H., Chiu E., Mak E., Butner J., Fujimoto K., Wood J, & Miranda J. (2015). Culturally Adapted Cognitive Behavioral Therapy for Depressed Chinese Americans: A Randomized Controlled Trial. Psychiatric services (Washington, D.C.), 66(10), 1035-1042.

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Multivariable Analysis of Experimental Settings

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Effects of the experimental settings were investigated in a multivariable linear mixed model. The experimental variables describing the methods used for the different submissions were included as fixed effects (Fig. 2b). The VC ratio, the VB count, the IB count, the bundle overlap percentage, and the bundle overreach percentage were modeled as dependent variables, each of which is used for the calculation of a separate model. The submitting group was modeled as a random effect. The software SAS 9.2, Proc Mixed, SAS Institute Inc., Cary, NC, USA, was used for the analysis.

Maier-Hein K.H., Neher P.F., Houde J.C., Côté M.A., Garyfallidis E., Zhong J., Chamberland M., Yeh F.C., Lin Y.C., Ji Q., Reddick W.E., Glass J.O., Chen D.Q., Feng Y., Gao C., Wu Y., Ma J., He R., Li Q., Westin C.F., Deslauriers-Gauthier S., González J.O., Paquette M., St-Jean S., Girard G., Rheault F., Sidhu J., Tax C.M., Guo F., Mesri H.Y., Dávid S., Froeling M., Heemskerk A.M., Leemans A., Boré A., Pinsard B., Bedetti C., Desrosiers M., Brambati S., Doyon J., Sarica A., Vasta R., Cerasa A., Quattrone A., Yeatman J., Khan A.R., Hodges W., Alexander S., Romascano D., Barakovic M., Auría A., Esteban O., Lemkaddem A., Thiran J.P., Cetingul H.E., Odry B.L., Mailhe B., Nadar M.S., Pizzagalli F., Prasad G., Villalon-Reina J.E., Galvis J., Thompson P.M., Requejo F.D., Laguna P.L., Lacerda L.M., Barrett R., Dell’Acqua F., Catani M., Petit L., Caruyer E., Daducci A., Dyrby T.B., Holland-Letz T., Hilgetag C.C., Stieltjes B, & Descoteaux M. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications, 8, 1349.

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