Sentence examples for mixed models treat from inspiring English sources

Linear mixed models treat missing data as "missing at random".

Linear mixed models treat missing data as being missing at random.

Mixed models treat missing values as missing at random, which is a less-rigorous assumption than missing completely at random.

In this paper two 'standard' approaches, i.e. a linear mixed model treating the outcome variables as normally distributed and a Poisson mixed model treating the outcome variables as Poisson distributed, will be compared with a two-part joint regression model in order to analyse the development over time and to analyse the differences between low and high educated patients.

The correction of the phenotypes was based on residuals estimated from a linear mixed model treating batch as a fixed effect, litter and pen as random effects, age at infection and passive IgG as covariates.

Because mixed-effects models treat some variables as fixed and some as random comparing the relative importance of variables from these two different classes can be difficult, as we explain below.

ICC was calculated via two-way mixed effects modelling, treating subject as a random effect and method of adherence as a fixed effect with a consistency definition as described by McGraw and Wong.

†COEF, 95% confidence intervals and P values calculated using mixed effects linear regression models (treating GP practice as a random effect and patient characteristics as fixed effects).

Mixed effects linear regression models (treating the GP practice as a random effect and patient characteristics as fixed effects) were used to calculate differences in means and 95% confidence intervals (CI) for GPAQ and Trust in Physician scores.

We used a generalized linear mixed model that treated injury severity as a nominal three-level variable, (i.e., no injury, mild injury, and moderate or severe injury) to determine the odds ratios (OR) for circumstances associated with sustaining an injurious fall.

The proper model for such data is a generalized linear mixed model that treats the binomial errors using a logit link function [ 11].