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Important advantages of our repeated measures models are that they are straightforward to apply and able to discriminate between high and low risk patients.
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To account for possible effects introduced through repeated measures, models were fit using standard least-squares method as well as mixed-effects method [17].
General linear repeated measures models were used to test for effects of training day on reaching performance including group, baseline performance, and the interaction of group×time (session) as independent variables.
EBV accuracy was higher when repeated measures models were used.
Repeated measures models were used to test for linear changes over follow-up.
Repeated measures models were used to compare the symptom scores at different time points.
Two separate repeated measures models were constructed: (1) global HRQL for all children based on parent proxy report ("all children" model) and (2) global HRQL for adolescents based on adolescent self-report and parent proxy report ("adolescent only" model).
Repeated measures models were used to evaluate the association of phthalate metabolites measured in third-trimester urine samples with each group of target genes, accounting for correlation among the genes within a pathway.
Two repeated measures models were built to describe trajectories of (1) global HRQL for all children based on parent proxy report and (2) global HRQL for adolescents based on adolescent self-report and parent proxy report.
The repeated measures model is a powerful statistical model and is especially valuable in a smaller sample.
For the comparison across days a repeated measures model was specified, using the linear mixed models module of SPSS.
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