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Data was analysed with a multilevel linear repeated measures regression model.
For all outcome measures, a mixed-effects (repeated measures) regression model was applied.
Table 3 shows the results of a repeated measures regression model predicting standardized thymus volume as a function of PCB concentration, adjusting for potential confounding variables.
A multilevel linear repeated measures regression model with random slopes was used for analyses in STATA 11 for windows (Stata Corp., College Station, TX).
Then a multilevel linear repeated measures regression model was conducted with adjustment for diagnosis (schizoaffective diagnosis, schizophrenia or other diagnosis), age as a continuous variable and sex [ 17].
We used a repeated measures regression model to compare changes in bacterial load over time between the larvae and hydrogel groups.
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Repeated measures regression models were used to determine factors associated with xerostomia and salivary gland hypofunction.
In addition, we modelled longitudinal variation in headache day frequency per month using negative binomial repeated measures regression models (NBRMR).
Using repeated measures regression modeling, we describe variation in HA days within/between individuals, and evaluate potential variation predictors.
We modelled longitudinal transitions between EM and CM and, separately, headache day frequency per month using negative binomial repeated measures regression models (NBRMR).
Unbalanced repeated measures regression models with structured covariance matrices were applied (module 5V in BMDP) to assess the impact of treatment duration on the yearly MI (within subject measure).
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