# Leveraging biomarkers for personalized treatment of alcohol use disorder comorbid with PTSD

> **NIH NIH P01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $100,578

## Abstract

Project Summary/Abstract
The Analytics and Biostatistics Core (ABC) will provide cost efficient, responsive and integrative data
management and statistical and analytic support for Center projects. It will provide easy access to data,
targeted consultations and facilitate collaboration for cross disciplinary and integrative research. It will utilize,
adapt and/or develop novel and efficient analytic methods and designs that allow the study of biological
mechanisms leading to the facilitation of personalized medicine. It will play a strong role in integrating findings
across projects using both quantitative and qualitative approaches. Dr. Eugene Laska will be the director and
Dr. Carole Siegel the deputy director of the Core staffed by an additional statistical/data scientist, Dr. Meng
Qian and a data management expert. The specific aims of the ABC are to provide: 1) consultation on the
details of the experimental designs of Center projects, data collection, data quality control and to maintain
centralized documentation of these efforts; 2) data management and storage services enabling efficient and
secured data sharing, data integration and data visualization; 3) analysis of data from each and across Center
research projects applying as appropriate state-of-the-art bioinformatics, statistical modeling, machine learning,
and causal analysis tools and algorithms. 4) new analytic methodologies to further inform personalized
medicine. Novel computational and analytical approaches will be applied or developed including a new
paradigm for the analysis of clinical trial data based on biomarkers for causal modelling of the probability of
treatment response that can serve in its application to move the field towards more personalized medicine.
ABC staff are well versed in state of the art statistical methods for analyzing data including ANOVAs, mixed
models and the application of regression models, trajectory analysis to examine variation over time, latent
variables in their use in factor analysis and survival time methods in their use to examine onset and relapse.
They have experience in handling unbalanced samples in terms of potentially prognostic measures, adjusting
for batch effects, controlling for confounding variables including demographics, comorbidities and health
conditions. ABC staff has proficiency in the use of data analytic/mining techniques for classification and
clustering most particularly random forests and in methods for identifying important variables in in regression
such as lasso, ridge regression and elastic net regression.

## Key facts

- **NIH application ID:** 10237284
- **Project number:** 5P01AA027057-04
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** EUGENE M LASKA
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $100,578
- **Award type:** 5
- **Project period:** 2018-09-20 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10237284

## Citation

> US National Institutes of Health, RePORTER application 10237284, Leveraging biomarkers for personalized treatment of alcohol use disorder comorbid with PTSD (5P01AA027057-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10237284. Licensed CC0.

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