New methods for statistical analysis of neuropsychological (NP) assessments for mental health services research are being proposed which can provide accurate and precise information about cognitive deficits. The potential benefits of extracting richer and more precise cognitive information from NP data sets are very significant: This analytic approach has the potential to (1) unveil specific relationships between NP deficits and disability; (2) enhance our ability to select treatment and rehabilitative methods that target specific cognitive deficiencies; (3) enhance our sensitivity to detect treatment responses, and to detect differences in cognitive processing between different clinical groups; and (4) save clinical assessment time and assessment costs through sequential testing. The proposed analytic approach includes sequential testing techniques that can drastically shorten the length of NP assessment testing. This project has potentially broad applicability. The techniques and models can be used in other clinical applications when it is of interest to measure cognitive functioning. ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
1R01MH065538-01A1
Application #
6581706
Study Section
Special Emphasis Panel (ZMH1-CRB-C (01))
Program Officer
Hohmann, Ann A
Project Start
2003-02-03
Project End
2005-12-31
Budget Start
2003-02-03
Budget End
2003-12-31
Support Year
1
Fiscal Year
2003
Total Cost
$252,910
Indirect Cost
Name
George Washington University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
043990498
City
Washington
State
DC
Country
United States
Zip Code
20052
Jaeger, Judith; Tatsuoka, Curtis; Berns, Stefanie M et al. (2006) Distinguishing neurocognitive functions in schizophrenia using partially ordered classification models. Schizophr Bull 32:679-91
Jaeger, Judith; Tatsuoka, Curtis; Berns, Stefanie et al. (2006) Associating functional recovery with neurocognitive profiles identified using partially ordered classification models. Schizophr Res 85:40-8