Sentence examples for mixed models indicated from inspiring English sources

Exact(6)

Mixed models indicated that the differences among INVR scores among the three trimesters were independent of gravidity, planned pregnancy and age.

Linear mixed models indicated a significant exposure-response association between PM2.5 exposure and urinary 8-OHdG levels (p = 0.03).

Mixed models indicated that there were no significant changes in depression, general anxiety, avoidance, or recurrence worry over the three study time points.

Mixed models indicated significant interactions between genotype and light and nutrient conditions affecting both traits, and interaction plots of these two traits showed both changes in rank and response.

Fitting generalized additive mixed models indicated that the association between gestational age at sample collection and urinary BPA concentrations was linear, and there was no interaction with PTB (data not shown).

Overall time effects analyzed by Friedman tests and generalized linear mixed models indicated significant findings for subjective health status (EQ-5D VAS) and pain/discomfort (an EQ-5D subdimension).

Similar(54)

A generalized linear mixed model indicated that the capture rate was significantly negatively affected by the agricultural village, the city park, the rainy weather, and latitude and positively by longitude.

However, diagnostics of the mixed model indicated a very nonnormal distribution of residuals.

The linear mixed model indicated that 3 caregiver characteristics, namely caregiver age, money (caregiver's financial situation) and valuation of life as a whole were significant predictors of the patient-by-proxy VAS scores (Table  2).

Results from the linear mixed model indicate a significant difference in morphology between lizards occupying disturbed and natural habitats.

Using data based on 664 respondents from three cities (Denver, Miami, and San Diego), mixed logit models indicated that several variables, particularly travel time, toll, sex, and income, were better predictors of managed lane use than the psychological variables.

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