Neuropsychiatric disorders pose an immense burden on patients, families, and health care systems, thus underscoring the urgent need to develop disease-modifying treatment. Research on neuropsychiatric disorders (e.g., Alzheimer?s disease, Parkinson?s disease) faces unique challenges, including the fact that these disorders typically have a late onset and slow progression, the diagnostic criteria are based on subjective clinical symptoms, and there is substantial disease and subject heterogeneity. In the proposed work, we aim to tackle these challenges by leveraging complementary contributions from multiple biomarkers, including genome-wide polymorphisms, whole brain neuroimaging, biofluids, and comprehensive neuropsychiatric assessments. We develop sophisticated analytic tools with higher resolution and improved accuracy by accounting for biological mechanisms of disease, synthesizing dynamic system-wide information, and integrating multiple sources of biomarkers. These methods are applied to clinical data collected by the investigative team or available from large international consortia in order to model the earliest pathological changes of neurodegenerative disease, assess treatment responses, and inform the design of early-intervention clinical trials and the discovery of optimal personalized therapies. Specifically, in Aim 1, we develop efficient methods for multi-level semiparametric transformation models to estimate and test the risk of genetic variants on various types of complex phenotypes to inform genetic counseling and improve clinical trial efficiency. Our methods do not rely on full pedigree genotyping and provide family-specific substructure, in addition to population substructure, to better control confounding and reduce false discovery rates in genome-wide association studies.
In Aim 2, we develop large-scale nonlinear dynamic systems through ordinary differential equations with random inflections to understand early pathological changes and identify subjects with preclinical signs. Our method provides multi-domain integration of ensembles of biomarker dynamics.
In Aim 3, we develop dynamic hazards models and incorporate dynamic network structures to estimate biomarker profiles that evolve smoothly with disease progression for earlier disease diagnosis. We account for irregularly measured biomarkers and biological network dependence among biomarkers.
In Aim 4, we develop doubly robust and efficient machine learning methods to identify predictive markers, estimate optimal individualized therapies, and identify subgroups who may receive the greatest benefit from therapy, with minimal risk. In each aim, we will validate the proposed methods through extensive simulation studies and demonstrate their practical value via application to real-world clinical studies. We establish theoretical properties of the proposed methods using modern empirical process theory and statistical learning theory. Together, the state-of-the-art analytic methods proposed here will substantially improve analytic accuracy, and our combined statistical and clinical expertise will ensure that our methods are translated directly back to the clinical and translational research community.

Public Health Relevance

The ultimate goal of neuropsychiatric research is to develop experimental therapeutics to delay disease on- set, slow disease progression, and provide effective treatment at each stage of disease. This proposal aims to develop new statistical approaches to integrate complementary sources of information from genomic measures, brain imaging biomarkers, and early clinical signs to characterize disease mechanism, progression, and treatment responses, and thereby inform the design of clinical trials and the discovery of optimal personalized therapies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS073671-08
Application #
9927686
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Miller, Daniel L
Project Start
2011-07-15
Project End
2021-04-30
Budget Start
2020-05-01
Budget End
2021-04-30
Support Year
8
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Wang, Yuanjia; Fu, Haoda; Zeng, Donglin (2018) Learning Optimal Personalized Treatment Rules in Consideration of Benefit and Risk: with an Application to Treating Type 2 Diabetes Patients with Insulin Therapies. J Am Stat Assoc 113:1-13
Liang, Liang; Carroll, Raymond; Ma, Yanyuan (2018) Dimension reduction and estimation in the secondary analysis of case-control studies. Electron J Stat 12:1782-1821
Li, Xiang; Xie, Shanghong; Zeng, Donglin et al. (2018) Efficient ?0 -norm feature selection based on augmented and penalized minimization. Stat Med 37:473-486
Qiu, Xin; Zeng, Donglin; Wang, Yuanjia (2018) Estimation and evaluation of linear individualized treatment rules to guarantee performance. Biometrics 74:517-528
Liu, Jianxuan; Ma, Yanyuan; Wang, Lan (2018) An alternative robust estimator of average treatment effect in causal inference. Biometrics 74:910-923
Liu, Ying; Wang, Yuanjia; Kosorok, Michael R et al. (2018) Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens. Stat Med 37:3776-3788
Liu, Ying; Wang, Yuanjia; Zeng, Donglin (2017) Sequential multiple assignment randomization trials with enrichment design. Biometrics 73:378-390
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Garcia, Tanya P; Ma, Yanyuan; Marder, Karen et al. (2017) ROBUST MIXED EFFECTS MODEL FOR CLUSTERED FAILURE TIME DATA: APPLICATION TO HUNTINGTON'S DISEASE EVENT MEASURES. Ann Appl Stat 11:1085-1116
Chen, Huaihou; Zeng, Donglin; Wang, Yuanjia (2017) Penalized nonlinear mixed effects model to identify biomarkers that predict disease progression. Biometrics 73:1343-1354

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