Academic literature on the topic 'SAS PROC MIXED'

Munzert, Manfred. "PROC GLM versus PROC MIXED." In Landwirtschaftliche und gartenbauliche Versuche mit SAS. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54506-1_7.

"Fitting Individual Growth Models Using SAS PROC MIXED." In Modeling Intraindividual Variability With Repeated Measures Data. Psychology Press, 2013. http://dx.doi.org/10.4324/9781410604477-11.

"subject k(i) gets formulation R, ε if response Y is from formulation T and ε if response Y is from formulation R. The means of T and R are then µ and µ , respectively. Then Var[s ] = σ , the between-subject variance for T , Var[s ] = σ , the between-subject variance for R, Var[s ] = σ , the subject-by-formulation interaction variance, Cov[s ] = ρσ σBR = σ , Var[ε ] = σ and Var[ε ] = σ . Further, we assume that all ε’s are pairwise independent, both within and between subjects. For the special case [ of equal group sizes (n Var[µˆ ] = [ σ − 2σ + ] (σ +σ )/2 /N = σ + (σ )/2 /N where N = 4n. The variances and interactions needed to assess IBE can easily be ob-tained by fitting an appropriate mixed model using proc mixed in SAS. The necessary SAS code for our example will be given below. However, before doing that we need some preliminary results. Using the SAS code we will obtain estimates σˆ , ωˆ , σˆ and σˆ , where ω is the between-subject covariance of R and T, and was earlier denoted by σ . These are normally distributed in the limit with a variance-covariance matrix appropriate to the structure of the fitted model. The model is fitted using REML (restricted maximum likelihood, see Section 6.3 of Chapter 6) and this can be done with SAS proc mixed with the REML option. In addition we fit an unstructured covariance structure using the type =UN option in proc mixed. The estimates of the vari-." In Design and Analysis of Cross-Over Trials. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9781420036091-21.

"Subject AUC AUC Cmax Cmax Test Ref Test Ref 2 150.12 142.29 5.145 3.216 4 36.95 5.00 2.442 0.498 6 24.53 26.05 1.442 2.728 7 22.11 34.64 2.007 3.309 9 703.83 476.56 15.133 11.155 12 217.06 176.02 9.433 8.446 14 40.75 152.40 1.787 6.231 16 52.76 51.57 3.570 2.445 17 101.52 23.49 4.476 1.255 19 37.14 30.54 2.169 2.613 22 143.45 42.69 5.182 3.031 23 29.80 29.55 1.714 1.804 25 63.03 92.94 3.201 5.645 28 . . 0.891 0.531 29 56.70 21.03 2.203 1.514 30 61.18 66.41 3.617 2.130 33 1376.02 1200.28 27.312 22.068 34 115.33 135.55 4.688 7.358 38 17.34 40.35 1.072 2.150 40 62.23 64.92 3.025 3.041 41 48.99 61.74 2.706 2.808 42 53.18 17.51 3.240 1.702 46 . . 1.680 . 48 98.03 236.17 3.434 7.378 49 1070.98 1016.52 21.517 20.116 log(Cmax) as needed for the TOST analysis is given below, where we fit a mixed model using SAS proc mixed. This model fits a random term for subjects within sequences. Using a mixed model we can produce an analysis that includes the data from all subjects, including those with only one value for AUC or Cmax. However, including the subjects with only one response does not change the results in any significant way and so we will report the results obtained using the subsets of data that have values in both periods for AUC (45 subjects) and Cmax (47 subjects)." In Design and Analysis of Cross-Over Trials. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9781420036091-9.

"ances and covariances obtained from REML are normally distributed with expectation vector and variance-covariance matrix equal to the fol-low  ing, r  espectiv    ely,   When σˆ > 0.04, let νˆ = δˆ + σˆ + σˆ − 2ωˆ + σˆ − (1 + c (7.6) be an estimate for the (7.3) reference-scaled metric in accordance with FDA Guidance (2001) and using a REML UN model. Then (Patter-son, 2003; Patterson and Jones, 2002b), this estimate is asymptotically normally distributed and unbiased with E[νˆ ] = δ +σ − (1 + c and Var[νˆ ] = 4σ + l + 4l + (1 + c ) (l )+ 2l −2(1+c − 2(1+c +4(1+c −2(1+c . Similarly, for the constant-scaled metric, when σˆ ≤ 0.04, νˆ = δˆ + σˆ + σˆ − 2ωˆ + σˆ − σˆ − 0.04(c ) (7.7) E[νˆ ] = δ +σ − 0.04(c ) Var[νˆ ] = 4σ + l + 4l + 2l − 2l − 4l + 4l − 2l . The required asymptotic upper bound √ of the 90% confidence interval can √ then be calculated as νˆ + 1.645× V̂ ar[νˆ ] or νˆ + 1.645× V̂ ar[νˆ ], where the variances are obtained by ‘plugging in’ the estimated values of the variances and covariances obtained from SAS proc mixed into the formulae for Var[νˆ ] or Var[νˆ ]. The necessary SAS code to do this is given in Appendix B. The output reveals that σˆ = 0.0714 and the upper bound is−0.060 for log(AUC). For log(Cmax), σˆ = 0.1060 and the upper bound is −0.055. As both of these upper bounds are below zero, IBE can be claimed." In Design and Analysis of Cross-Over Trials. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9781420036091-22.

Frederickson, Lee, Mario Leoni, and Fletcher Miller. "Carbon Particle Generation and Lab-Scale Small Particle Heat Exchange Receiver Experimentation and Modeling." In ASME 2014 8th International Conference on Energy Sustainability collocated with the ASME 2014 12th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/es2014-6640.