We fitted the bivariate random effects models using the NLMIXED (non-linear mixed effect) procedure of SAS version 9.2 (SAS Institute, Cary, NC, USA).
We applied the empirical option for the mixed effects procedure in SAS, also known as the "sandwich" estimator which computes a robust variance covariance matrix of the fixed effects parameters using an asymptotically consistent estimator [56 59].
In Part A, the use of a non-linear mixed effects procedure required simulation techniques to estimate power for a given sample size and expected magnitude of effect.
We used the linear mixed effects procedure to fit the linear mixed effects models and the generalised linear mixed models penalised quasilikelihood procedure 27 to fit the logistic regression mixed effects models.
The mixed effect statistical procedure was used to evaluate the influence of control measures and seniority on current exposure levels.
Changes over time in quality of life items will be evaluated with a repeated measurement ANOVA using a mixed effect modeling procedure (SAS Proc Mixed).
As such the statistical analysis plan will be updated to include an additional coprimary final analysis entailing a repeated measures analysis over time, specifically a mixed effect modelling procedure, taking into account the MELD measurement captured at baseline, days 30, 60 and 90.
We used a general linear mixed effect model (GLMM) procedure fitted with residual maximum likelihood estimation (REML, lme function; [65] to investigate the effect of body size on the acoustic parameters of groans (F0-related parameters: 186 groans; 10.9±1.1 per individual; Formant frequencies and Dfmin: 144 groans; 8.5±0.8 per individual; N = 17).
This experiment was analyzed with a mixed effect model using the mixed procedure of SAS software.
The models were fit as a mixed effect linear model, using the lme-procedure in R version 2.5.1 (nlme library), allowing for correction for temporal autocorrelation as well as inclusion of random effects of individuals.
