A special feature of Probability and Statistics Day at UMBC 2013 is that the conference, including the workshop, is open to all statistics graduate students
from UMBC and local universites free of charge; however, REGISTRATION IS REQUIRED! The deadline to register is Friday,
April 12, 2013. // REGISTER NOW
For more information, contact any member of the organizing committee:
Bimal Sinha
Conference Chair
443.538.3012
Kofi Adragni
410.455.2406
Yvonne Huang
410.455.2422
Yaakov Malinovsky
410.455.2968
Thomas Mathew
410.455.2418
Nagaraj Neerchal
410.455.2437
DoHwan Park
410.455.2408
Junyong Park
410.455.2407
Anindya Roy
410.455.2435
Elizabeth Stanwyck
410.455.5731
Participant Information
Sandya Lakkur
Poster: Multiple Imputation Analysis of Cognitive Differences in Schizophrenia Subgroups Compared to Controls
Multiple imputation is a method used in statistical analysis to estimate any missing values from a dataset, using the Markov Chain Monte Carlo method. This process was applied to a dataset examining differences in neuropsychological impairments among two subgroups of schizophrenia patients and a control group. A collection of 26 neuropsychological tests, which were grouped into eight different cognitive domains, were administered on the subjects. Cohen’s D statistics were then calculated to determine the magnitude of difference in neurocognitive scores between the three groups of patients across each domain and individual test scores. The ultimate goal was to compare the potential improvement in estimation of the Cohen’s D statistic upon imputation. Exploratory analysis was conducted in the distribution of standardized imputed values. Variation between the schizophrenia subgroups was largest in processing speed and smallest in working memory. Both subgroups were markedly impaired in comparison to the control. Multiple imputation increased precision on the Cohen’s D statistics and reduced potential bias due to missing data.
