an introduction to latent variable models - departmentskbroche/aging - pdf/intro to latent...
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![Page 1: An Introduction to Latent Variable Models - Departmentskbroche/Aging - PDF/Intro to Latent Variable... · An Introduction to Latent Variable Models Karen Bandeen-Roche ABACUS Seminar](https://reader031.vdocuments.net/reader031/viewer/2022013104/5a938b027f8b9a451b8bacc7/html5/thumbnails/1.jpg)
An Introduction to Latent Variable Models
Karen Bandeen-Roche ABACUS Seminar Series
November 28, 2007
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Objectives For you to leave here knowing…
• What is a latent variable? • What are some common latent variable
models? • What is the role of assumptions in latent
variable models? • Why should I consider using—or decide
against using—latent variable models?
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Ordinary Linear Regression Residual as Latent Variable
X
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.
. . . .
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. .
. . . .
.
Y . ε Y X ε
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Mixed effect / Multi-level models Random effects as Latent Variables
time
vital
non-vital
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0
β0 + β1
β0
β2
β2 + β3
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Mixed effect / Multi-level models Random effects as Latent Variables
• b0i = random intercept b2i = random slope (could define more)
• Population heterogeneity captured by spread in intercepts, slopes time
vital
non-vital
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0
β0 + β1
β0
β2
β2 + β3
+ b0i slope: - |b2i|
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Mixed effect / Multi-level models Random effects as Latent Variables
time
vital
non-vital
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.
. .
. .
.
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.
. .
. .
.
.
.
. .
. .
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. .
. .
.
0
β0 + β1
β0
β2
β2 + β3
+ b0i slope: - |b2i|
Y X ε
t b
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Frailty Latent Variable Illustration
Frailty Adverse outcomes
Y1
Yp
…
Determinants
e1
ep
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Why do people use latent variable models?
• The complexity of my problem demands it • NIH wants me to be sophisticated • Reveal underlying truth (e.g. “discover”
latent types) • Operationalize and test theory • Sensitivity analyses • Acknowledge, study issues with
measurement; correct attenuation; etc.
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Well-used latent variable models Latent variable scale
Observed variable scale
Continuous Discrete
Continuous Factor analysis LISREL
Discrete FA IRT (item response)
Discrete Latent profile Growth mixture
Latent class analysis, regression
General software: MPlus, Latent Gold, WinBugs (Bayesian), NLMIXED (SAS)
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Tailored software: AMOS, LISREL, CALIS (SAS)
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Frailty Latent Variable Illustration
Inflam.
regulation Adverse outcomes
Y1
Yp
…
Determinants
e1
ep
Theory informs
relations (arrows)
ς λ1
λp
Measurement
Structural
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Example: Theory Infusion • Inflammation: central in cellular repair • Hypothesis: dysregulation=key in accel. aging
– Muscle wasting (Ferrucci et al., JAGS 50:1947-54; Cappola et al, J Clin Endocrinol Metab 88:2019-25)
– Receptor inhibition: erythropoetin production / anemia (Ershler, JAGS 51:S18-21)
Stimulus (e.g. muscle damage)
IL-1# TNF
IL-6 CRP
inhibition
up-regulation
# Difficult to measure. IL-1RA = proxy
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Theory infusion InCHIANTI data (Ferrucci et al., JAGS, 48:1618-25) • LV method: factor analysis model
– two independent underlying variables – down-regulation IL-1RA path=0 – conditional independence
Inflammation 2
Down-reg.
IL-6
TNFα
CRP IL-1RA
IL-18
Inflammation 1
Up-reg.
.36
. 59 . 45 . 31
. 31
-.59
-.40
.20
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ANOTHER WELL-USED LATENT VARIABLE MODEL
Motivation: Self-reported Visual functioning • Questionnaires have proliferated
– This talk: Activities of Daily Vision5 (ADV) – “Far vision” subscale: How much difficulty with
reading signs (night, day); seeing steps (day, dim light); watching TV = Y1,...,Y5
• Question of interest: What aspects of vision determine “far vision” function
• One point of view on such “function”: Latent subpopulations
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Analysis of underlying subpopulations Latent class analysis / regression
POPULATION
… P1 PJ
Ci
Y1 YM Y1 YM … …
∏11 ∏1M ∏J1 ∏JM
19-Goodman, 1974; 27-McCutcheon, 1987
Xi
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Analysis of underlying subpopulations Method: Latent class analysis/ regression
• Seeks homogeneous subpopulations
• Features that characterize latent groups – Prevalence in overall population – Proportion reporting each symptom – Number of them
– Assumption: reporting heterogeneity unrelated to measured or unmeasured characteristics
– conditional independence, non differential measurement by covariates of responses within latent groups : partially determine features
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no x
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LCR: Self-reported Visual functioning
• Study: Salisbury Eye Evaluation (SEE; West et al. 19976) – Representative of community-dwelling elders – n=2520; 1/4 African American – This talk: 1643 night drivers
• Analyses control for potential confounders:
– Demographic: age (years), sex (1{female}), race (1{nonwhite}), education (years)
– Cognition: Mini-Mental State Exam score (MMSE; 30-0 points) – Depression: GHQ subscale score (0-6) – Disease burden: # reported comorbidities
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Aspects of vision • Visual acuity: .3 logMAR (about 2 lines)
• Contrast sensitivity: 6 letters
• Glare sensitivity: 6 letters
• Stereoacuity: .3 log arc seconds
• Visual field: root-2 central points missed
– Latter two: span approximately .60 IQR
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MODEL CHECKING IS POSSIBLE!
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Observed (solid) and Predicted (dash) Item Prevalence vs Acuity Plots
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One last issue Identifiability
• Models can be too big / complex • A model is non-identifiable if distinct
parameterizations lead to identical data distributions – i.e. analysis not grounded in data
• Weak identifiability is common too: – Analysis only indirectly grounded in data (via
the model)
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Identifiability
data (ground)
model
analysis
strong
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Identifiability
data (ground)
model
analysis
weak
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Identifiability
data (ground)
model
analysis
non
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Objectives For you to leave here knowing…
• What is a latent variable? • What are some common latent variable
models? • What is the role of assumptions in latent
variable models? • Why should I consider using—or decide
against using—latent variable models?
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