Statistician, U.S. Department of Veterans Affairs
June ‘20 - Present
Graduate Research Assistant, University of Pittsburgh School of Pharmacy
May ‘20 - Present
Data Analyst, University of Pittsburgh, School of Medicine
Apr ‘18 - May ‘20
Projects, Skills, & Interests
Statistician, U.S. Department of Veterans Affairs
- Big data management and analysis of Veteran data
- Visualization and presentation of data completion, validity
- Statistician on prospective quality improvement programs and retrospective cohort studies
- Creation of training materials for new employee onboarding and data documentation for new data sources
Within the VA, I serve on the quantitative team for the VA’s Safer Aging through Geriatrics-informed Evidence-based practices (SAGE) quality improvement program. As this project is currently in the pre-implementation phase, much of my work involves data management to identify cohorts of eligible Veterans and inform the specific clinics involved in randomization for each included intervention (also referred to as evidence-based practices). Final analysis during and following this project will include the treatment effects of each intervention on mortality rate and the number of “facility-free days”, or days a Veteran is able to spend within their own private residence (i.e. no admitted to a hospital, nursing facility, or other domiciliary care).
I additionally work as a statistician on two research teams who are leading retrospective-cohort studies. The first project assesses the current utilization and effectiveness of outpatient palliative care among Veterans with life-limiting conditions, with a secondary aim of assessing unhealth prescribing in older, end-of-life Veterans. The second project studies the effects of recently implemented eScreening tools for mental health disorders and suicidal ideation, specifically studying Operation Iraqi Freedom/ Operation Enduring Freedom Veterans who are newly enrolled in the VA healthcare system.
Data & Study Management
Beyond my statistician role, I additionally work with Dr. Chantele Mitchell-Miland to design, create, and manage study databases for CHERP-affiliated researchers who are leading prospective research studies and/or small-scale trials. This database management work includes collaboration with investigators as well as study coordinators and research assistants to optimize data entry efficiency during Veteran visits and to report data capture and visit completion rates.
Data Science Education Consultant, School of Pharmacy
The University of Pittsburgh’s School of Pharmacy curriculum includes a PharmacoAnalytics track/concentration as well as a number of data management and analysis related coursework. Dr. Ravi Patel, who leads the course Python for Data Management & Analytics within the school, and I have developed and are continuing to contribute to a public-facing site of education materials related to data science and machine learning.
These materials target audiences with varying levels of comfort with Python, including first-time Python users learning and applying new skills or those with familiarity to practice and demonstrate their skills in relevant use-cases. Applied materials (including use cases and available data, either simulated or publicily hosted) are specifically tailored to business and/or clinical use-cases in pharmacy.
This work specitically first included the curated aggregation of public and/or open-source Python and statistical education resources such as textbooks, lectures, programs, blogs, etc.
Beyond curating these currently-existing materials, Dr. Patel and I met with pharmacists and data scientists in practice to create projects and assignments that mirror data and business problems seen in practice. This involved the simulation of shareable data that mirrors the idiosyncrasies (including random and non-random missingness, seasonal and temporal trends, and the presence of superfluous or incorrect data) seen in each specific use-case.
|Vijapur, S, Vaughan LE, DiSanto D., McKernan G, Wagner AK. “Treelet Transform Analysis to Identify Clusters of Systemic Inflammatory Variance in a Population with Moderate-to-Severe Traumatic Brain Injury” Brain, Behavior, & Immunity – (Unpub. Accepted 2021)|
|Millville, KM, Awan N, DiSanto D., Kumar RG, Wagner AK. “Early chronic systemic inflammation and associations with cognitive performance after moderate-to-severe TBI” Brain, Behavior, & Immunity – Health, Volume 1, 12021, 100185, ISSN 2666-3546. https://doi.org/10.1016/j.bbih.2020.100185|
|Kumar, RG, DiSanto D. et al. “Temporal Acute Serum Estradiol and Tumor Necrosis Factor-α Associations and Mortality Risk After Severe Traumatic Brain Injury”, Journal of Neurotrauma. , Volume 37, Issue 20, Pages 2189-2210, https://doi.org/10.1089/neu.2019.6577|
|Awan, N, DiSanto D. et al. “ Evaluating the Cross-Sectional and Longitudinal Relationships Predicting Suicidal Ideation Following Traumatic Brain Injury”, The Journal of Head Trauma & Rehabilitation, Volume 36, Issue 1, 2020, Pages E18-E29, http://doi.org/10.1097/HTR.0000000000000588|
|Awan, N, DiSanto D. et al. “Interrelationships Between Post-TBI Employment and Substance Abuse: A Cross-lagged Structural Equation Modeling Analysis”, Archives of Physical Medicine and Rehabilitation, Volume 101, Issue 5, 2020, Pages 797-806, https://doi.org/10.1016/j.apmr.2019.10.189|
|DiSanto D. et al. “Employment Stability in the First 5 Years After Moderate-to-Severe Traumatic Brain Injury”, Archives of Physical Medicine and Rehabilitation, Volume 100, Issue 3, 2019, Pages 412-421, ISSN 0003-9993 https://doi.org/10.1016/j.apmr.2018.06.022.|