Natalia A. Gouskova, PhD

The primary focus of my work is methodological and applied statistical research and promoting more proper applications of analytical tools and methods, particularly in aging research.

My area of statistical expertise is advanced survival analysis, specifically competing risks data which particularly often arise in aging research due the presence of more than one risk factors in aging populations. I have developed the competing risks version of the number needed to treat metric which is commonly used to quantify the effect of a new treatment in clinical trials, and concordance index for competing risks discrete time models. I developed a method to handle missing cause of failure in competing risks data which is a common situation in real-life data. My statistical methods research resulted publications in journals such as Biometrics, Biostatistics, and Statistics in Medicine. Most recently I developed a novel algorithm for practical implementation of risk-benefit analysis with multiple time-to-event endpoints which define a patient outcome profile.  

I serve as the primary statistician on epidemiological and clinical manuscripts, doing the statistical analysis and advocating for the most appropriate statistical methods. The latest analysis which I led was analysis of staff COVID testing rates within a large national nursing homes chain in collaboration with Brown University. I suggested and justified the appropriate statistical model to account for 2-level clustering and correlations between covariates and random effects. Manuscripts with my statistical analyses are published in JAMA, NEJM, PLoS One and PLoS Genetics, American Journal of Cardiology, Circulation, and others.

I actively contribute to projects from grant proposals to awarded grants. I did the power analyses and wrote statistical sections of the grant proposal "Gait Speed as a Functional Vital Sign in Older Adults with Multiple Myeloma," the pilot for which has already been awarded by the Boston Pepper Center (June 2020). I wrote the statistical analysis plan and performed the data analysis for the clinical trial “The Effects of Multi-focal Transcranial Direct Current Stimulation on Freezing of Gait in Patients with Parkinson’s Disease” (NCT02656316). I monitor the data and participate in DSMB meeting for the ongoing “Personalized Brain Activity Modulation to Improve Balance and Cognition in Elderly Fallers” clinical trial (NCT03814304). I served as the lead statistician on the phase II clinical trial of RTB101 as COVID-19 Post Exposure Prophylaxis in Adults Age ≥65 Years (2020-2021).

My other area of excellence and interest is development of analytic tools for clinical studies. Most recently I developed and programmed a novel algorithm for practical implementation of risk-benefit analysis with multiple time-to-event endpoints which define a patient outcome profile. The algorithm was applied to evaluating the net benefit of intensive blood pressure strategy in pre-frail and frail populations.  

I mentor junior colleagues and recently an OPT student, with a particular emphasis on good statistical and analytic practices. I also informally educate colleagues on the topics of my technical expertise.

Overall my work combines theoretical and applied research, both individual and collaborative, with the main goal of promoting the use of the most appropriate statistical methods and analytic tools to facilitate efficient and rigorous research.
 

Dr. Gouskova’s key research focuses on:

Statistical areas: 
Competing risks, recurrent events, multi-state models, model assessment tools, patient-oriented multi-level outcome profiles.

Subject areas: 
Aging, bone and mineral research, brain stimulation, CVD, COPD, balance disorders, AD/ADRD, nursing homes data. 
 

View a complete list of publications on My NCBI Bibliography.