380 likes | 502 Views
Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science. Dan Mungas University of California, Davis. Acknowledgements. Funded in part by Grant R13 AG030995 from the National Institute on Aging
E N D
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 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
Collaborative Science Friday Harbor Psychometrics, 2013
Latent Variable Modeling Friday Harbor Psychometrics, 2013
The Essence of 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
Neuropathology Friday Harbor Psychometrics, 2013
Neurofibrillary tangles and neuritic plaques Neurofibrillary tangles Neuritic Plaques 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 Friday Harbor Psychometrics, 2013
Neurofibrillary tangles and neuritic plaques Neurofibrillary tangles Neuritic Plaques 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 Friday Harbor Psychometrics, 2013
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
mfrnp mtmpnp Neur-Plq inparnp hipponp entonp mfrdp mtmpdp Diff-Plq inpardp hippodp entodp mfrnft Cort-NFT mtmpnft 2 c inparnft hipponft entonft 4 Dimension Measurement Model – AD Neuropathology Religious Order Study .89 .93 .89 .85 .87 .80 .92 .89 .77 .91 .68 .73 .75 .58 .87 .48 .94 = 124.9, df = 39 .91 .71 CFI = .988 TLI = .994 RMSEA = .076 WRMR =.738 .83 MT-NFT .83 Friday Harbor Psychometrics, 2013
ENT ENT MT MT MT ENT MF MF MF HC HC HC IP IP Neuritic Plaques Diffuse Plaques NeoCortical Tangles Medial Temporal Tangles IP Friday Harbor Psychometrics, 2013
APOE Age ENT ENT MT MT MT ENT MF MF MF HC HC HC IP IP Diffuse Plaques Neuritic Plaques NeoCortical Tangles Medial Temporal Tangles IP Friday Harbor Psychometrics, 2013
APOE Age ENT ENT MT MT MT ENT MF MF MF HC HC HC IP IP 0.40 0.84 Diffuse Plaques Neuritic Plaques 0.26 0.77 NeoCortical Tangles 0.58 0.32 0.36 Medial Temporal Tangles 0.18 IP Friday Harbor Psychometrics, 2013
Study 1 - ROS Study 2 - MAS Friday Harbor Psychometrics, 2013
Neuropathology and Cognition – Religious Order Study & Memory and Aging Project N = 652, Dowling et al., 2011 Friday Harbor Psychometrics, 2013
Model Fit: CFI: 0.994 RMSEA: .022 0.84 0.77 0.78 0.80 0.91 GWMSUBCLAC GWMSUBCMIC KDPCBRALWM KGMCMUFMI KGMCMUCIV KGMCUNFMI KGMCUNCIV KGMCPAFMI KGMCPACIV KGMSUBCM KGMSUBCL KWMCILAC KWMCICYS KWMPERIV KWMCIMIC GWMCCYS GWMCMIC WM_ISCH 0.64 White Matter Incomplete Infarction Sub-Cortical Infarcts Cortical Infarcts Micro Infarcts Friday Harbor Psychometrics, 2013
Mixed Effects Modeling of Neuropathology Effects on Longitudinal Trajectories Friday Harbor Psychometrics, 2013
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 Friday Harbor Psychometrics, 2013
Estimated CASI at Death Friday Harbor Psychometrics, 2013
Estimated CASI Change Friday Harbor Psychometrics, 2013
Braak and Vascular Risk TrajectoriesEpisodic Memory Friday Harbor Psychometrics, 2013
Braak and Vascular Risk TrajectoriesExecutive 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