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Moreover, development of identity over time should be analyzed with longitudinal approaches, as it does not seem to be simply related to age.
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Changes of dietary and lifestyle factors over time will be analyzed with analysis techniques for longitudinal data, since the observations of one individual over time are not independent.
The longitudinal change in number and volume of metastases could be analyzed with the help of MRI specific software.
Data from each individual were analyzed with a prospective longitudinal cohort design where children were essentially matched to themselves over time using SAS software (version 9.1; SAS Institute Inc., Cary, NC, USA).
The importance of distributed heat source on combustion instabilities of longitudinal modes is analyzed with the help of a simplified geometrical configuration of combustor.
Longitudinal data were analyzed with linear mixed models to examine change in grief by condition across the study period and the effect of the intervention on grief through its interaction with psychiatric distress.
Linear longitudinal data were analyzed with repeated-measures ANCOVA.
The longitudinal data were analyzed with the SAS statistical package (version 9.1; SAS Institute Inc., Cary, NC, USA) using additive mixed models with a random patient effect.
Longitudinal changes in patient outcomes were analyzed with repeated measures analysis of variance.
The longitudinal changes in body composition were analyzed with the generalized estimating equation model (using SAS Version 8.1 Proc Genmod) developed by Liang and Zeger (15).
Longitudinal and latitudinal historical patterns of spread were analyzed with linear regression models.
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