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1
A
latent
class
growth model and multivariate logistic regression analyses were used.
2
Similar banding patterns were grouped into homogeneous classes using
latent
class
analysis.
3
Test performance was determined by conventional methods and Bayesian
latent
class
modeling.
4
These group differences were also supported by data-driven
latent
class
analyses.
5
These results were replicated with an affected status phenotype derived from
latent
class
clusters.
6
Using
latent
class
analysis, we evaluated whether criteria composing the measure aggregate into a syndrome.
7
A multinomial logit and a
latent
class
logit model was used for the data analyses.
8
Change trajectories were measured using
latent
class
growth modelling.
9
Patient-level and
latent
class
analyses between groups were performed.
10
BMI trajectories were identified using
latent
class
trajectory modeling.
11
The
latent
class
model identified 2 classes of respondents.
12
We used
latent
class
growth modeling to classify groups of individuals based on trajectories of suicidal ideation.
13
Conclusions: The
latent
class
model did not successfully predict treatment failure, despite taking all variables into account.
14
Five weight growth trajectories of the 1957 term SGA babies were grouped by a
latent
class
model.
15
Logistic regression was used to measure associations between
latent
class
membership and HIV-related sexual and health-seeking behaviors.
16
The letter also places the monotonicity assumption in the context of the method of
latent
class
instrumental variables.
latent
class
latent