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As mixed modelling allows incorporation of random effects to model unknown variance, we were also able to use the clinical and molecular genetic data obtained to model the progress of individual patients and predict their expected course of disease development.
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The mixed model allows the prediction of the Sauter mean diameter without any adjustable parameter.
The use of mixed models allows for the estimation of separate stratum variances thus providing a more complete description of the data (Gilmour et al. 1995).
Linear mixed model allows inclusion of correlated observations.
The mixed model allows a utility value for any combination of disease states and toxicities to be calculated.
The linear mixed model allows for the investigation of group differences while controlling for the non-independency of data (i.e. more than one child participated per family, which resulted in related measurements within groups and between groups).
Faculty sentiment is that this mixed model allows the participant to investigate learning the necessary bioinformatics skills on their own pre-workshop and attempt their application to their own research data, but then allows the participant to reinforce or gain detailed tool-usage techniques and analysis strategies specific to their own data set in the face-to-face learning environment.
Linear mixed modeling allowed systematic estimates of the independent marginal effects of each independent variable on each performance outcome of interest while controlling for the effects of variations in a batch's actual residence time, days since maintenance, and influent turbidity.
The mixed model allowed subject-specific intercepts and slopes.
Linear mixed models allowed us to accommodate multiple measures per person.
Mixed models allow for dependence of observations provided by the same respondent.
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