Glimmix

Manufactured by SAS Institute

Sourced in United States

GLIMMIX is a software package for fitting generalized linear mixed models. It provides a flexible framework for modeling a wide range of data types, including normal, binary, count, and time-to-event data. GLIMMIX can handle both fixed and random effects, and allows for the specification of complex correlation structures.

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Lab products found in correlation

Sas 9, by SAS Institute (13 mentions) Sas v9, by SAS Institute (3 mentions) Sas studio, by SAS Institute (2 mentions) Sas version 9, by SAS Institute (2 mentions) Sas proc mixed, by SAS Institute (1 mentions) Mixed procedure, by SAS Institute (1 mentions) Prism 6, by GraphPad (1 mentions) Proc glm, by SAS Institute (1 mentions) Sas for windows version 9, by SAS Institute (1 mentions)

43 protocols using glimmix

Fungal Infection Treatment Evaluation

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For fungal culture scores and Wood’s lamp scores, differences between the two treatments were evaluated using a generalized linear mixed model using GLIMMIX of SAS, assuming a binomial distribution and using a logit link.
For ‘time to mycological cure’, values were evaluated as ordinal outcomes. Cats that did not reach mycological cure by the study conclusion were considered censored and assigned to the category ‘>9 weeks’. Differences between the two treatments were evaluated using a generalized linear mixed model using GLIMMIX of SAS, assuming a multinomial distribution and using a cumulative logit link.
The models included treatment group as a fixed effect and room and cohort nested in room as random effects. A P value <0.05 was determined significant.
For lesion scores (erythema, induration, scale/crust), the effectiveness of itraconazole in improving the total lesion score for each of the assessed primary lesions at each examination day was calculated according to the formula:

% improvement = \frac{(\begin{array}{l} total score in 40 control cats - \\ total score in 40 treated cats \end{array})}{total score in 40 control cats} \times 100

Puls C., Johnson A., Young K., Hare J., Rosenkrans K., Young L, & Moriello K. (2017). Efficacy of itraconazole oral solution using an alternating-week pulse therapy regimen for treatment of cats with experimental Microsporum canis infection. Journal of Feline Medicine and Surgery, 20(10), 869-874.

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Analysis of Cercospora Leaf Spot Control

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For all experiments, treatment was evaluated as the fixed effect of interest and replicate was considered a random effect. Due to differences in experimental treatments and design, years and locations were analyzed separately. Analysis of variance (ANOVA) was conducted using SAS (Statistical Analysis System) v. 9.4 software package (SAS Institute, Inc. Cary, NC, United States) to determine treatment effects on percent C. beticola sporulation and isolation, standardized leaf area, percent leaf degradation, early-season lesion counts from sentinel beets, AUDPC, yield, percent sugar, RWS, and RWSH values. Sentinel beet lesion count data were normalized using the lognormal distribution option to best fit this data (Gbur et al., 2012 ). Statistical analyses (mixed model ANOVA) were conducted using the generalized linear mixed model (GLIMMIX) procedure (SAS Institute Inc., 2013 ) and evaluated at the α = 0.05 significance level. Fisher’s protected least significance difference (LSD) was used for mean comparisons. LSD was calculated to compare treatment differences using letter separation option “mult” macro (Piepho, 2012 ).

Hernandez A.P., Bublitz D.M., Wenzel T.J., Ruth S.K., Bloomingdale C., Mettler D.C., Bloomquist M.W., Hanson L.E, & Willbur J.F. (2023). An in-field heat treatment to reduce Cercospora beticola survival in plant residue and improve Cercospora leaf spot management in sugarbeet. Frontiers in Plant Science, 14, 1100595.

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Anticipated Risk Compensation Analysis

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Given potential autocorrelation among dyads involving the same respondent, generalized linear mixed models with a random effect for respondent were used. Models were estimated using the PROC GLIMMIX (SAS Institute, 2011 ) procedure (SAS v9.3). Anticipated risk compensation was regressed on each of the key measures described above. Covariates reaching p<0.10 in bivariate analyses were entered into multivariate analyses. Unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were reported.

Young A.M., Halgin D.S, & Havens J.R. (2015). Relationship-level analysis of drug users’ anticipated changes in risk behavior following HIV vaccination. AIDS care, 27(8), 1000-1004.

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Comparison of HIV-Positive Mothers and Outcomes

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We conducted an analysis comparing those lost to follow-up at the three year assessment (n=82) with those retained (n=455). There were only two significant differences: those who were lost to follow-up were less likely to have a recent sexual partner at the baseline interview and more likely to have a monthly income less than 2000 rand. Our primary analysis compared MLH and MWOH using random effects regression models, with MWOH as the reference group. For the purpose of the analysis, a MLH is defined as a mother that reported a positive HIV status at least once during the study (during pregnancy or at any follow-up assessment). Logistic random effects regression models were used for binary outcomes and a Poisson random effects regression model was used for count outcomes. All models were adjusted for neighborhood clustering and for repeated measures, where appropriate. The developmentally sensitive measures of language and executive functioning were also adjusted for children's age in months. Comparisons of MLH and MWOH utilized a random participant effect to control for the longitudinal nature of the assessments of partner relationships, physical and mental health, and family issues. The random effects regression was carried out using SAS PROC MIXED or GLIMMIX for continuous or binary/count variables, respectively (version 9.4; SAS Institute Inc., Cary, North Carolina).

Rotheram-Borus M.J., Tomlinson M., Scheffler A., Harris D.M, & Nelson S. (2017). Adjustment of a Population of South African Children of Mothers Living with/and without HIV through Three Years Post-Birth. AIDS and behavior, 21(6), 1601-1610.

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Randomized Complete Block Design Analysis

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Data were analyzed as a randomized complete block design with pen as experimental unit. Continuous data (e.g., initial BW) were analyzed using the MIXED procedure (SAS 9.4 Inc., Cary, NC), with treatment as a fixed effect and block as a random effect. A generalized linear mixed model (GLIMMIX, SAS 9.4 Inc.) was used to analyze categorical data with the model effects described previously. Model estimation was performed using a logit scale to link events/trials responses to a binomial distribution. Initial estimates of treatment means and respective standard errors are reported on the data scale using an inverse link method (ILINK Option, SAS 9.4 Inc.). When overall treatment effect was significant (P < 0.10), treatment means were partitioned using Tukey’s HSD post hoc analysis.

Szasz J.I., McMurphy C.P., Bryant T.C., Luque J., Barcelo C., Sepulveda G., Blood K.S., Bernhard B.C, & Hughes H.D. (2018). Influence of therapeutic use of feedgrade tetracyclines in combination with tulathromycin metaphylaxis on animal health and performance of Holstein steer calves. Translational Animal Science, 3(1), 185-194.

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Pathogen Life-History Trade-Offs Across Hosts

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Lastly, I set up four models to test whether there are potential trade‐offs between pathogen life‐history traits and whether host species affect potential life‐history trait correlations. First, to test whether high infectivity in one host species has a cost as reduced infectivity in other host species, three models in Proc REG in SAS (SAS Institute) using the mean infectivity of each P. sparsa strain on each host species as the response variable were run. Secondly, to understand whether fast sporulation is related to a high sporulation abundance at the end of the experiment and whether host species influences this relationship, I ran a GLMM in Proc Glimmix in SAS (SAS Institute) using the mean sporulation stage of the pathogen on each host genotype as a continuous response variable (model j in Table 1). Speed to sporulation was used as a continuous explanatory variable. Host species and host genotype nested under host species were used as class explanatory variables. P. sparsa strain was included as a random variable. A Gaussian error distribution was assumed. Interactions among host species, host genotype nested under host species, and speed to sporulation were tested, and the final model was selected that minimized the AIC.

, & Susi H. (2024). Alternative host shapes transmission and life‐history trait correlations in a multi‐host plant pathogen. Evolutionary Applications, 17(3), e13672.

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Analyzing Algal Residue Characteristics

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Data for various algal residues were analyzed using the Proc Glimmix of SAS version 9.2 [14 ]. Differences between least square means were tested using Satterthwaite approximation for the denominator degrees of freedom as an option. Quadruplicate laboratory analyses were conducted for in vitro gas production analysis. Four runs of gas measurements within variation less than ± 5% at 72 h incubation were used for the analysis. Data fitting a non-linear model were analyzed using Proc NLIN of SAS [14 ], which was developed by Dr. P. J. Weimer (personal communication).

Han K.J, & McCormick M.E. (2014). Evaluation of nutritive value and in vitro rumen fermentation gas accumulation of de-oiled algal residues. Journal of Animal Science and Biotechnology, 5(1), 31.

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Analyzing Sensory and Instrumental Meat Quality

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All data were subjected to analysis of variance (ANOVA) via the generalised linear mixed model, GLIMMIX procedure of SAS (SAS Inst. Inc., Cary, NC, USA) using block and production system as sources of variation. Animal was the experimental unit. The random tool of the GLIMMIX procedure was used to include “time” as a repeated measurement for the analysis of pH decline and assessor for the analysis of the sensory data. Data are presented as least squares means and when significant effects were detected, the post hoc Tukey test was used to separate the means. The level of significance used was P < 0.05.
The following models were used for instrumental and sensory measurements, respectively:

µ = intercept; ∑ error. Fixed factors P: production system; B: animal block. Random effect: P * ID: Production System * Animal identity, C (ID): consumer within ID of the animal.

Moran L., Wilson S.S., McElhinney C.K., Monahan F.J., McGee M., O’Sullivan M.G., O’Riordan E.G., Kerry J.P, & Moloney A.P. (2019). Suckler Bulls Slaughtered at 15 Months of Age: Effect of Different Production Systems on the Fatty Acid Profile and Selected Quality Characteristics of Longissimus Thoracis. Foods, 8(7), 264.

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Eggshell Thickness and Water Loss

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Pearson correlation coefficients (Pearson's r) were calculated for the mass loss between weighing sessions, across all fragments. Nested ANOVA (SAS v9.2 Proc NESTED) was conducted to partition the percentage of variability in G_H₂O that was directly attributable to egg section (within an egg), individual egg (within a species) and individual species (within a family). We analysed whether the differences in G_H₂O were associated with the species identity, eggshell thickness and shell section using generalised linear mixed models (SAS v9.2 Proc GLIMMIX; accounting for repeated measures from replicate fragments within an egg as a random effect). Preliminary tests confirmed that eggshell thickness and adult body mass were highly correlated with each other, with >78% of the variation explained. Therefore, in subsequent analyses, only eggshell thickness, and not adult body mass, was included. No other measures were correlated at levels >50%.

Portugal S.J., Maurer G., Thomas G.H., Hauber M.E., Grim T, & Cassey P. (2014). Nesting behaviour influences species-specific gas exchange across avian eggshells. The Journal of Experimental Biology, 217(18), 3326-3332.

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Pollen Foraging and Flower Abundance

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To assess if the amount of pollen carried by the captured insects is related to flower abundance, we first screened the data to determine which insect species were captured in sufficient numbers and carried sufficient E. visheri pollen for statistical analysis. For those species, we ran negative binomial regressions in PROC GLIMMIX (SAS Institute Inc. 2018; SAS Institute Inc. (c) 2002–2012) to assess the relationship between the amount of E. visheri pollen an insect carried and the number of E. visheri flowers present. Analyses were carried out within years.

Larson D.L., Larson J.L., Symstad A.J., Buhl D.A, & Portman Z.M. (2021). Coflowering invasive plants and a congener have neutral effects on fitness components of a rare endemic plant. Ecology and Evolution, 11(9), 4750-4762.

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