The DSMC maintains a central data base, ensures its quality and security, arranges linkage of data from the various cores, facilitates linkage with projects, identifies potential subjects for new and ongoing studies based on eligibility criteria, compiles analytic datasets, and carries out statistical analysis of the data. Core statisticians develop new methodology to meet the challenges of the longitudinal studies of the UCD ADC and to support new applications. The Core also provides intellectual leadership through collaboration with investigators, analysis and reporting of data, and mentoring and training in biostatistics. We have developed a state-of-the-art centralized web-accessed database that integrates data and tracking from all cores. DSMC leadership organized a strategic plan and process for developing publications that has been highly successful in disseminating results from our unique clinical sample and longitudinal data resources. We have developed innovative analytic methodology for the challenges of longitudinal data and complex multidimensional information about participants. We play an ongoing role in training new investigators in the psychometric and statistical issues for cognitive research and aging. Our goal for the renewal period is to build on this substantial track record to advance the goals of the UCD ADC through 1) continued oversight of data management, 2) providing statistical design and analysis support, 3) fostering development of new statistical methodology for specific ADC analysis challenges, and 4) working with the EITC to train students and new researchers in the area of AD.
The DSMC is central to UCD ADC research. The principal research theme is the study of the trajectory of cognitive decline and dementia in aging across a wide spectrum of diverse participants. Our Core provides the critical expertise in integrated databases, measurement of cognitive function in a diverse cohort across the full spectrum of performance, and statistical analysis of multivariate and longitudinal data.
|Tadayon, Sayed H; Vaziri-Pashkam, Maryam; Kahali, Pegah et al. (2016) Common Genetic Variant in VIT Is Associated with Human Brain Asymmetry. Front Hum Neurosci 10:236|
|Lai, Dongbing; Xu, Huiping; Koller, Daniel et al. (2016) A MULTIVARIATE FINITE MIXTURE LATENT TRAJECTORY MODEL WITH APPLICATION TO DEMENTIA STUDIES. J Appl Stat 43:2503-2523|
|Day, Gregory S; Musiek, Erik S; Roe, Catherine M et al. (2016) Phenotypic Similarities Between Late-Onset Autosomal Dominant and Sporadic Alzheimer Disease: A Single-Family Case-Control Study. JAMA Neurol 73:1125-32|
|Ronquillo, Jay Geronimo; Baer, Merritt Rachel; Lester, William T (2016) Sex-specific patterns and differences in dementia and Alzheimer's disease using informatics approaches. J Women Aging 28:403-11|
|Ridge, Perry G; Hoyt, Kaitlyn B; Boehme, Kevin et al. (2016) Assessment of the genetic variance of late-onset Alzheimer's disease. Neurobiol Aging 41:200.e13-20|
|Tosto, Giuseppe; Monsell, Sarah E; Hawes, Stephen E et al. (2016) Progression of Extrapyramidal Signs in Alzheimer's Disease: Clinical and Neuropathological Correlates. J Alzheimers Dis 49:1085-93|
|Ringman, John M; Monsell, Sarah; Ng, Denise W et al. (2016) Neuropathology of Autosomal Dominant Alzheimer Disease in the National Alzheimer Coordinating Center Database. J Neuropathol Exp Neurol 75:284-90|
|Chapman, Kimberly R; Bing-Canar, Hanaan; Alosco, Michael L et al. (2016) Mini Mental State Examination and Logical Memory scores for entry into Alzheimer's disease trials. Alzheimers Res Ther 8:9|
|Besser, Lilah M; Alosco, Michael L; Ramirez Gomez, Liliana et al. (2016) Late-Life Vascular Risk Factors and Alzheimer Disease Neuropathology in Individuals with Normal Cognition. J Neuropathol Exp Neurol 75:955-962|
|Thung, Kim-Han; Wee, Chong-Yaw; Yap, Pew-Thian et al. (2016) Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans. Brain Struct Funct 221:3979-3995|
Showing the most recent 10 out of 990 publications