Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science
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Transcript of Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science
Latent Variable Modeling of Neuropathology Data:
Implications for Collaborative Science
Dan MungasUniversity of California, Davis
Friday Harbor Psychometrics, 2013
Acknowledgements
• Funded in part by Grant R13 AG030995 from the National Institute on Aging
• The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention by trade names, commercial practices, or organizations imply endorsement by the U.S. Government.
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Collaborative Science
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Latent Variable Modeling
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• Now what is the message there? The message is that there are no "knowns." There are things we know that we know. There are known unknowns. That is to say there are things that we now know we don't know. But there are also unknown unknowns. There are things we do not know we don't know. So when we do the best we can and we pull all this information together, and we then say well that's basically what we see as the situation, that is really only the known knowns and the known unknowns. And each year, we discover a few more of those unknown unknowns. ~ D. Rumsfeld, June 6, 2002
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The Essence of Latent Variable Modeling
Neuropathology
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Neurofibrillary tangles and neuritic plaques
Neuritic Plaques
Neurofibrillarytangles
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Measurement Challenges in Neuropathology
• Sampling of brain regions• Reliability and standardization of methods for
quantitation• Distribution of variables• Relation to clinical and cognitive outcomes
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Distribution Issues
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Sophisticated Tools for Item Scaling
Neurofibrillary tangles and neuritic plaques
Neuritic Plaques
Neurofibrillarytangles
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Practical Approaches to Modeling Neuropathology
• Many modeling approaches are based on assumption of multivariate normality
• Modeling neuropathology counts as continuous variables can be problematic Use of robust distribution free estimators does
not solve problem• Latent variable modeling approaches for
categorical/ordinal data can be helpful
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Categorical Variable Modeling Example
Categorical Data Issues
• Recoding of data required to create “manageable” number of categories Does this result in loss of information? Are there other/better approaches?
• Count variables modeled using different distributional assumptions
• Bayesian estimation
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Applications of Latent Variable Modeling to Neuropathology Studies
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CFI = .988TLI = .994RMSEA = .076WRMR =.738
mfrnp
mtmpnp
inparnp
hipponp
entonp
mfrdp
mtmpdp
inpardp
hippodp
entodp
mfrnft
mtmpnft
inparnft
hipponft
entonft
Neur-Plq
Diff-Plq
Cort-NFT
MT-NFT
.89
.87
.92
.75
.87
.83
.83
.91
.94
.91
.89
.93
.85
.89
.73
.80
.58
.71
.68
.48
.77
2 = 124.9, df = 39
4 Dimension Measurement Model – AD NeuropathologyReligious Order Study
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MedialTemporalTangles
NeoCorticalTangles
NeuriticPlaques
ENT
HC MF
IP
MT
ENT
HC
MF
IP
MT
DiffusePlaques
ENT
HC MF
IP
MTFriday Harbor Psychometrics, 2013
MedialTemporalTangles
NeoCorticalTangles
NeuriticPlaques
ENT
HC MF
IP
MT
ENT
HC
MF
IP
MT
DiffusePlaques
ENT
HC MF
IP
MT
Age
APOE
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HC
MF
MedialTemporalTangles
NeoCorticalTangles
NeuriticPlaques
Age
APOE
ENT
HC MF
IP
MT
ENT
IP
MT
DiffusePlaques
ENT
HC MF
IP
MT
0.84
0.58
0.32
0.26
0.40
0.18
0.36
0.77
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Study 2 - MAS
Study 1 - ROS
Neuropathology and Cognition – Religious Order Study & Memory and Aging Project
N = 652, Dowling et al., 2011Friday Harbor Psychometrics, 2013
GWMSUBCMIC
KDPCBRALWM
GWMCMIC
KWMCIMIC
KGMCPAFMI
KGMCUNFMI
KGMCMUFMI
KGMSUBCM
WM_ISCH
KWMPERIV
KGMSUBCL
KWMCICYS
KWMCILAC
GWMSUBCLAC
KGMCMUCIV
KGMCUNCIV
KGMCPACIV
GWMCCYS
White MatterIncomplete Infarction
CorticalInfarcts
MicroInfarcts
Sub-CorticalInfarcts
0.80
0.77
0.84
0.78
0.91
0.64
Model Fit: CFI: 0.994 RMSEA: .022
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Mixed Effects Modeling of Neuropathology Effects on Longitudinal Trajectories
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CASI and NeuropathologyHonolulu Asian Aging Study
• Random Effects Model• Dependent Variable
CASI• Estimated score at death• Rate of change preceding death
• Independent Variables Neuritic Plaque Factor Score Neurofibrillary Tangle - Neocortical Factor Score Neurofibrillary Tangle - Medial Temporal Factor Score Estimated Brain Atrophy
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Estimated CASI at Death
Effect Coef. S.E. p
Intercept 75.92 0.84 .001
NPL -2.01 1.26 .11
NFT-NC -3.10 1.25 .01
NFT-MT -.44 1.07 .68
Brain Atrophy -6.43 .83 .001
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Estimated CASI Change
Effect Coef. S.E. p
Intercept 75.92 0.84 .001
NPL .44 .18 .01
NFT-NC .19 .19 .32
NFT-MT -.35 .15 .02
Brain Atrophy .31 .12 .009
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Braak and Vascular Risk TrajectoriesEpisodic Memory
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Braak and Vascular Risk TrajectoriesExecutive Function
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The Internet
• A global to-do list that anyone in the world can add to, especially Rich Jones.
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