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Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science Dan Mungas University of California, Davis Friday Harbor Psychometrics,

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Presentation on theme: "Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science Dan Mungas University of California, Davis Friday Harbor Psychometrics,"— Presentation transcript:

1 Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science Dan Mungas University of California, Davis Friday Harbor Psychometrics, 2013

2 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. Friday Harbor Psychometrics, 2013

3 Collaborative Science Friday Harbor Psychometrics, 2013

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6 Latent Variable Modeling Friday Harbor Psychometrics, 2013

7 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 Friday Harbor Psychometrics, 2013 The Essence of Latent Variable Modeling

8 Neuropathology Friday Harbor Psychometrics, 2013

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12 Neurofibrillary tangles and neuritic plaques Neuritic Plaques Neurofibrillary tangles Friday Harbor Psychometrics, 2013

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14 Measurement Challenges in Neuropathology Sampling of brain regions Reliability and standardization of methods for quantitation Distribution of variables Relation to clinical and cognitive outcomes Friday Harbor Psychometrics, 2013

15 Distribution Issues Friday Harbor Psychometrics, 2013

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17 Sophisticated Tools for Item Scaling

18 Neurofibrillary tangles and neuritic plaques Neuritic Plaques Neurofibrillary tangles Friday Harbor Psychometrics, 2013

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20 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 Friday Harbor Psychometrics, 2013

21 Categorical Variable Modeling Example

22 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 Friday Harbor Psychometrics, 2013

23 Applications of Latent Variable Modeling to Neuropathology Studies Friday Harbor Psychometrics, 2013

24 CFI =.988 TLI =.994 RMSEA =.076 WRMR =.738 mfrnp mtmpnp inparnp hipponp entonp mfrdp mtmpdp inpardp hippodp entodp mfrnft mtmpnft inparnft hipponft entonft Neur- Plq Diff-Plq Cort-NFT MT-NFT.89.87.92.75.87.83.91.94.91.89.93.85.89.73.80.58.71.68.48.77  2 = 124.9, df = 39 4 Dimension Measurement Model – AD Neuropathology Religious Order Study Friday Harbor Psychometrics, 2013

25 Medial Temporal Tangles NeoCortical Tangles Neuritic Plaques ENT HCMF IP MT ENT HC MF IP MT Diffuse Plaques ENT HCMF IP MT Friday Harbor Psychometrics, 2013

26 Medial Temporal Tangles NeoCortical Tangles Neuritic Plaques ENT HCMF IP MT ENT HC MF IP MT Diffuse Plaques ENT HCMF IP MT Age APOE Friday Harbor Psychometrics, 2013

27 HCMF Medial Temporal Tangles NeoCortical Tangles Neuritic Plaques Age APOE ENT HCMF IP MT ENT IP MT Diffuse Plaques ENT HCMF IP MT 0.84 0.58 0.32 0.26 0.40 0.18 0.36 0.77 Friday Harbor Psychometrics, 2013

28 Study 2 - MAS Study 1 - ROS

29 Neuropathology and Cognition – Religious Order Study & Memory and Aging Project N = 652, Dowling et al., 2011 Friday Harbor Psychometrics, 2013

30 GWMSUBCMIC KDPCBRALWM GWMCMIC KWMCIMIC KGMCPAFMI KGMCUNFMI KGMCMUFMI KGMSUBCM WM_ISCH KWMPERIV KGMSUBCL KWMCICYS KWMCILAC GWMSUBCLAC KGMCMUCIV KGMCUNCIV KGMCPACIV GWMCCYS White Matter Incomplete Infarction Cortical Infarcts Micro Infarcts Sub-Cortical Infarcts 0.80 0.77 0.84 0.78 0.91 0.64 Model Fit: CFI: 0.994 RMSEA:.022 Friday Harbor Psychometrics, 2013

31 Mixed Effects Modeling of Neuropathology Effects on Longitudinal Trajectories Friday Harbor Psychometrics, 2013

32 CASI and Neuropathology Honolulu 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 Friday Harbor Psychometrics, 2013

33 Estimated CASI at Death EffectCoef.S.E.p Intercept75.920.84.001 NPL-2.011.26.11 NFT-NC-3.101.25.01 NFT-MT-.441.07.68 Brain Atrophy-6.43.83.001 Friday Harbor Psychometrics, 2013

34 Estimated CASI Change EffectCoef.S.E.p Intercept75.920.84.001 NPL.44.18.01 NFT-NC.19.32 NFT-MT-.35.15.02 Brain Atrophy.31.12.009 Friday Harbor Psychometrics, 2013

35 Braak and Vascular Risk Trajectories Episodic Memory Friday Harbor Psychometrics, 2013

36 Braak and Vascular Risk Trajectories Executive Function Friday Harbor Psychometrics, 2013

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38 The Internet A global to-do list that anyone in the world can add to, especially Rich Jones. Friday Harbor Psychometrics, 2013

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