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Mixed-effects Models for Repeated Measures.
This chapter provides models for repeated measures and multivariate data.
Models for repeated measures cross-over designs are defined in terms of growth curve models.
Linear regression models for repeated measurements, controlling for confounding factors, were applied.
Multilevel logistic regression models for repeated measures were used to evaluate effects of the standardized footbath protocol in preventing active DD lesions.
Analysis of mixed models for repeated measures revealed significant positive effects on the BDI and SDQ total scores, as well as less Conduct problems and Hyperactivity in SDQ measures from the experimental group mothers, comparing pre with post-test.
In order to identify the condition assuring the highest immunoglobulin recovery, two separated linear regression models for repeated measurement - one for IgA and one for IgG (response variable) - were fitted to compare different levels of concentration under different experimental conditions.
Statistical analyses were performed using mixed models for repeated measures.
Efficacy analyses utilized mixed models for repeated measures.
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Differences in the use of rescue NSAIDs were evaluated with subject specific cumulative logit-models for repeated measures.
Mixed regression models for repeated-measures were used.
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