In analysis, the fuzzy relational structures computed in the different sized domains or those computed by means of various fuzzy logics can be analyzed using generalized morphism; namely (global) structure embedding, or structure preserving as well as meta analysis of fuzzy relational structures.
Data will be analyzed using generalized estimating equation, a special generalized linear models technique.
Longitudinal data will be analyzed using generalized estimating equation (GEE) models.
Continuous outcome measures will be analyzed using generalized linear models including treatment allocation, test of interaction term between allocation and the vignette used, as well as variables associated with attrition or baseline imbalances between the groups, if any.
Differences between the two treatment arms regarding the primary outcomes will be analyzed using generalized linear models with gamma distribution and log-link function controlling for confounders (e.g. baseline costs and PDHD, age, gender, co-morbidity, EQ-5D scores).
Virologic suppression will be analyzed using generalized estimating equation log-binomial models [ 44, 45] to estimate 12 month mean (95% CI) prevalence by arm and relative risk between arms, adjusted for the stratification factor (newly diagnosed in past 12 months vs. earlier), while accounting for correlation among clustered binary responses at 6 and 12 month follow up.
Data were analyzed using generalized linear mixed models.
The data were analyzed using generalized linear mixed models with SPSS®, version 22.0.
Repeated measurements were analyzed using generalized estimating equations controlling for baseline characteristics and accounting for correlations.
The hysteresis loops were analyzed using generalized statistical theory of the hysteresis loop.
Loading time was analyzed using mixed model procedures, and stress responses were analyzed using generalized linear mixed model methodology.
