This is a request for continuation of support for a training program in Neurostatistics and Neuroepidemiology launched in 2004 at the Harvard School of Public Health (HSPH). The Grant will be administered through the Department of Biostatistics. It will involve active participation by an accomplished, experienced, and multidisciplinary training faculty, including 11 primary trainers at HSPH and 23 secondary trainers at the affiliated Harvard hospitals. These faculty members have appointments at the Departments of Biostatistics and Epidemiology at HSPH and at Harvard Medical School. The proposed training program aims to serve two pressing needs in neurologic diseases research: the need for well-trained biostatisticians who have an understanding of and commitment to research in neuroscience and neurology and the need for highly trained neurologists who have an understanding of and commitment to utilization of quantitative research methods. To serve these critical needs, this grant proposes to train three pre-doctoral students and one post-doctoral student in biostatistics and two post-doctoral students in epidemiology. The biostatistics trainees will develop methodologic expertise in areas relevant to neurologic diseases research, including survival analysis, longitudinal studies, clinical trials, fMRI analysis, and genetic studies, and substantive expertise in several neurologic diseases and their accompanying measurement and analysis techniques. The epidemiology trainees will be highly qualified MD specialists in neurology (or related field). These trainees will develop expertise in epidemiologic and biostatistical methods, enabling them to pursue successful academic careers in clinical and translational neurologic diseases research. Trainees will spend a minimum of two years in the Program. The Program will capitalize on the strong tradition of training in biostatistical and epidemiologic methods at HSPH and on the rich and varied resources in neurologic diseases research available at Harvard. Trainees will complete coursework in biostatistical and epidemiologic methods and they will actively engage in collaborative research experiences, including many opportunities offered through the Harvard NeuroDiscovery Center.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Special Emphasis Panel (ZNS1-SRB-P (47))
Program Officer
Korn, Stephen J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard University
Biostatistics & Other Math Sci
Schools of Public Health
United States
Zip Code
Wassertheil-Smoller, Sylvia; Qi, Qibin; Dave, Tushar et al. (2018) Polygenic Risk for Depression Increases Risk of Ischemic Stroke: From the Stroke Genetics Network Study. Stroke 49:543-548
Asafu-Adjei, Josephine K; Sampson, Allan R (2018) Covariate adjusted classification trees. Biostatistics 19:42-53
Lee, Catherine; Betensky, Rebecca A; Alzheimer's Disease Neuroimaging Initiative (2018) Time-to-event data with time-varying biomarkers measured only at study entry, with applications to Alzheimer's disease. Stat Med 37:914-932
Ramchandani, Ritesh; Finkelstein, Dianne M; Schoenfeld, David A (2018) Estimation for an accelerated failure time model with intermediate states as auxiliary information. Lifetime Data Anal :
Chiou, Sy Han; Austin, Matthew D; Qian, Jing et al. (2018) Transformation model estimation of survival under dependent truncation and independent censoring. Stat Methods Med Res :962280218817573
Swanson, D M; Anderson, C D; Betensky, R A (2018) Hypothesis Tests for Neyman's Bias in Case-Control Studies. J Appl Stat 45:1956-1977
Qian, Jing; Chiou, Sy Han; Maye, Jacqueline E et al. (2018) Threshold regression to accommodate a censored covariate. Biometrics :
Chiou, Sy Han; Xu, Gongjun; Yan, Jun et al. (2018) Semiparametric estimation of the accelerated mean model with panel count data under informative examination times. Biometrics 74:944-953
Emerson, Sarah C; Waikar, Sushrut S; Fuentes, Claudio et al. (2018) Biomarker validation with an imperfect reference: Issues and bounds. Stat Methods Med Res 27:2933-2945
Weisskopf, Marc G; Seals, Ryan M; Webster, Thomas F (2018) Bias Amplification in Epidemiologic Analysis of Exposure to Mixtures. Environ Health Perspect 126:047003

Showing the most recent 10 out of 96 publications