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

Acknowledgements Funded in part by Grant R13 AG 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

Collaborative Science Friday Harbor Psychometrics, 2013

Latent Variable Modeling Friday Harbor Psychometrics, 2013

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

Neuropathology Friday Harbor Psychometrics, 2013

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

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

Distribution Issues Friday Harbor Psychometrics, 2013

Sophisticated Tools for Item Scaling

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

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

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 Friday Harbor Psychometrics, 2013

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

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  2 = 124.9, df = 39 4 Dimension Measurement Model – AD Neuropathology Religious Order Study Friday Harbor Psychometrics, 2013

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

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

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

Study 2 - MAS Study 1 - ROS

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

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 Model Fit: CFI: RMSEA:.022 Friday Harbor Psychometrics, 2013

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

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

Estimated CASI at Death EffectCoef.S.E.p Intercept NPL NFT-NC NFT-MT Brain Atrophy Friday Harbor Psychometrics, 2013

Estimated CASI Change EffectCoef.S.E.p Intercept NPL NFT-NC NFT-MT Brain Atrophy Friday Harbor Psychometrics, 2013

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

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

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