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Proc mixed sas 9 2

Manufactured by SAS Institute

Proc mixed SAS 9-2 is a statistical analysis procedure within the SAS software suite. It is used for fitting linear mixed models, which are useful for analyzing data with correlated observations, such as repeated measurements or hierarchical data structures. Proc mixed provides a flexible framework for modeling and analyzing these types of data.

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Lab products found in correlation

3 protocols using proc mixed sas 9 2

1

Multivariate Neuroimaging Analysis of NAA

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We included only voxels with less than 30% standard deviation error in NAA and Cr determined from the Cramer-Rao bounds in LCModel. The median number of usable voxels per participant/time-point was 35 [IQR:17–40, Max:70]. The average composition of the spectroscopy region was (31% grey, 52% white, 11% CSF and 0.06% lesions) with a small, not statistically significant difference from baseline and exit (+0.4% for grey, −1.7% white, +0.2% CSF and −0.5% lesions; p>0.6 for all differences); there were no significant differences in the composition between the included and excluded voxels (differences included-excluded: 1% grey, −0.6% white, 0.9% lesion, 0.6% CSF; p>0.2 all differences). To account for the correlation between voxels we used a mixed model (proc mixed SAS 9-2, Cary, NC) with an anisotropic power spatial covariance matrix with terms for time and the voxel coordinates in the halfway space. To calculate the degrees of freedom, we used Kenward Rogers’ approximation. The model’s dependent variable was NAA, and the main effect was visit. The covariates were percent of GM, WM, CSF and lesions in the voxel. To reference NAA to Cr, LCModel’s estimate of Cr was included as a covariate; similarly, to reference to the water content the resampled PD was included. NAA and Cr were log transformed so the model would be in a multiplicative scale. The analyses were intention to treat.
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2

Statistical Comparison of Performance Data

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Performance data were compared using the mixed model analysis of variance and a compound symmetry covariance matrix (PROC MIXED, SAS 9.2, SAS Inc., Cary, NC). p<0.05 or p<0.001 was considered to be significant. The obtained results revealed that the differences among the compared data were non-significant.
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3

Nutrient Changes in Watersheds

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Statistically significant changes in event mean nutrient concentrations were determined using a linear mixed effects (LME) model. The LME model is:
with Y representing the variable of interest (Ln TP, Ln TDP, Ln TN, Ln NO 2 þNO 3 eN, Ln NH 4 eN, and Ln organic N); ns i is either the NW or SW; ba j indicates when the measurement was taken, before or after installation of the AGSS, and is represented by b or a, respectively; ev k is the random event and illustrates the storm event that has taken place in both watersheds; and e ijk is the error associated with the above three parameters. The Ln transformations were implemented in order to hold normality assumptions within modeling. Analysis was conducted using Proc Mixed SAS 9.2 (SAS, 2008) with ns and ba as fixed effects and ev as a random effect. Statistical significance was set at P ¼ 0.05. Storm events (ev k ) represent a specific individual storm with a varying probability of intensity and duration that occurred within both watersheds. As a result, this variable was held as a random factor within Equation (1). In addition, stream water samples were taken in the same location before and after installation of the AGSS within both the NW and SW. Thus, the parentheses in Equation (1) represent a nested design in which the ba j factor is nested within the ns i factor.
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