This application requests support for a new 5-year NIH NRSA Institutional Research Training Grant (T32) in Biostatistics as it relates to psychiatric disorders. This grant will support a rigorous doctoral training program in both biostatistical theory and applications, with the opportunity for trainees to apply advanced statistical techniques to data collected from the many different types of studies involving psychiatric disorders. Studies will include clinical trials for comparison of treatment regimens, genetic studies of psychiatric traits, analysis of large-scale gene expression (microarray) data, mental health services research and implementation studies, and neuroimaging studies to better understand brain dysfunction in psychiatric disorders. The goal of the proposed training program is to train biostatisticians for academic careers in psychiatric clinical research. The proposed training will equip these biostatisticians with an arsenal of tools to analyze challenging data, and to identify and solve important and outstanding research problems. The Department of Biostatistics will provide the administrative leadership for this program. Two faculty members, one academic advisor from the Department of Biostatistics and one faculty member from the Department of Psychiatry, will share the task of mentoring each trainee. A steering committee will govern the overall functioning of the program, including the selection of trainees. Doctoral students will enter the training program after completing the first year course requirements for the biostatistics PhD degree. Students with advanced standing or adequate research experience can enter the program in their first year of study. Trainees will receive a maximum of three years of support from the training program. The trainees must satisfy the requirements of the PhD program, including the required courses in their chosen areas of concentration. The areas of concentration are defined by two tracks: 1) intervention research, and 2) psychiatric genetics. The intervention research track emphasizes advanced statistical methodology, methodology for analyzing psychiatric neuroimaging data (to understand better treatment - response variability) and mental health services research methodology. The psychiatric genetics track emphasizes statistical methodology for deciphering genetics psychiatric traits. Issues of responsible conduct are integrated into the entire program. The Training Grant Steering Committee will meet every trimester to review trainees' progress, the overall progress of the program and selection of new trainees. This application is for awards spanning a five-year training period. Two doctoral awards will be given in the first year and four doctoral awards will be given in each of the next four years, totaling 18 awards over a five-year period. We expect these 18 awards to support six or seven students over five years. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Institutional National Research Service Award (T32)
Project #
1T32MH073451-01
Application #
6894448
Study Section
Special Emphasis Panel (ZMH1-ERB-I (02))
Program Officer
Wynne, Debra K
Project Start
2005-08-01
Project End
2010-07-31
Budget Start
2005-08-01
Budget End
2006-07-31
Support Year
1
Fiscal Year
2005
Total Cost
$98,936
Indirect Cost
Name
University of Pittsburgh
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Wallace, Meredith L; Anderson, Stewart J; Mazumdar, Sati et al. (2012) Incorporating temporal features of repeatedly measured covariates into tree-structured survival models. Biom J 54:181-96
Wallace, Meredith L; Anderson, Stewart J; Mazumdar, Sati (2010) A stochastic multiple imputation algorithm for missing covariate data in tree-structured survival analysis. Stat Med 29:3004-16
Blakesley, Richard E; Mazumdar, Sati; Dew, Mary Amanda et al. (2009) Comparisons of methods for multiple hypothesis testing in neuropsychological research. Neuropsychology 23:255-64
Perkins, Kenneth A; Scott, John (2008) Sex differences in long-term smoking cessation rates due to nicotine patch. Nicotine Tob Res 10:1245-50
Brock, Guy N; Shaffer, John R; Blakesley, Richard E et al. (2008) Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes. BMC Bioinformatics 9:12
Mazumdar, Sati; Tang, Gong; Houck, Patricia R et al. (2007) Statistical analysis of longitudinal psychiatric data with dropouts. J Psychiatr Res 41:1032-41
Begley, Amy E; Tang, Gong; Mazumdar, Sati et al. (2007) Use of OSWALD for analyzing longitudinal data with informative dropout. Comput Methods Programs Biomed 85:109-14