Towards a Unified Language of Cognition: Defining Domains, Normality, and
Change 
   Lead Investigator:    Andrew Kiselica
   Institution      :    Baylor Scott and White Health
   E-Mail           :    akiselica@health.missouri.edu
   Proposal ID      :    1189


   Publications:


1  Kiselica AM
   Factor Structure of the Uniform Dataset 3.0 Battery: Specification and Creation of a Normative Calculator
   2019   Abstract  International Neuropsychological Society Annual Meeting , 

   PRESENTED



2  Kiselica AM
   Multivariate Base Rates of Low Scores in the Uniform Data Set 3.0 Neuropsychological Battery: Normative Data and Predictive Validity for Cognitive
   2019   Journal Article  Neuropsychology , 

   SUBMITTED



3  Kiselica AM, Webber TA, Benge JF
   The Uniform Dataset 3.0 Neuropsychological Battery: Factor Structure, Invariance Testing, and Demographically Adjusting Factor Score Calculation
   Journal Article  J Int Neuropsychol Soc,1-11:doi: 10.1017/S1,2020,PMC in process

   PUBLISHED



4  Webber TA, Kiselica AM, Benge JF
   Normative Factor Scores for the Uniform Dataset 3.0 Battery Differentiate Severities and Etiologies of Cognitive Impairment
   2019   Abstract  International Neuropsychological Society Annual Meeting , 

   SUBMITTED





   Proposal Description:
   Tools for indexing cognitive impairment vary widely, leading to inconsistencies across basic research studies and lack of uniformity in clinical trial design. Factor analysis provides the opportunity to identify domains of cognitive impairment that are less susceptible to such variability.  We will objectively identify and validate domains of cognitive impairment in the Uniform Data Set (UDS).  Subsequently, we will use sophisticated statistical methodology to create critically useful and psychometrically robust tools for clinical research based on latent cognitive variables.  We have several specific aims:     1) Test competing models of the factor structure of cognition in the Uniform Data Set.                                                                         2) Calculate robust norms for each identified cognitive domain using longitudinal data.                                                                3) Determine multivariate base rates for each cognitive domain to clearly define abnormal scores in patients who lack neurological disorders.               4) Calculate regression based reliable change indices for each cognitive domain to help determine symptom progression and response to intervention.