Senior Director, Biostatistics Strategy
Senior Director, Biostatistics Strategy
For more than 15 years, Agustin Calatroni has specialized in the statistical design, implementation, analysis, and reporting of clinical trials and observational and mechanistic studies related to asthma and allergy.
Mr. Calatroni has more than 10 years of experience with the analysis of data from asthma and allergy studies, as well as propensity score methods for causal inference, linear mixed models, nonlinear mixed models, Bayesian hierarchical models, multiple imputations, multivariate regression (partial least squares, recursive partitioning), and data visualization
He has extensive experience in the measurement and calculations of predicted values for spirometry for the inner-city and National Health and Nutrition Examination Survey (NHANES) studies. And one of his special interests is the analysis of semiparametric hierarchical models to understand the relationship between environmental exposure and asthma morbidity and lung function.
Mr. Calatroni is an active participant at statistical meetings, attending oral presentations, poster presentations, and continuing education courses. He has presented results of original statistical research from the Asthma Consortium at the Joint Statistical Meetings and the Society for Clinical Trials. He also has presented at the Academic Academy of Allergy Asthma & Immunology annual meeting as invited course faculty for NHLBI, NIAID, and NIEHS. Courses presented include “Clinical Trial Designs to Predict Asthma Exacerbations,” which discussed clinical trial designs that have identified predictive biomarkers for asthma medications and methods to identify prognostic predictors for asthma exacerbations; as well as a course titled, “Getting to Grips with the Big Data,” which discussed the role that allergen/endotoxin exposures and allergic sensitization play in allergic diseases, along with strategies to apply new, standardized methods in indoor allergen assessment.
Along with Mr. Calatroni’s extensive experience with standard statistical software (SAS, R, and Stan), he also has excellent foreign language skills (Spanish and French) and is currently a member of the American Statistical Association.
Mr. Calatroni’s academic background includes a master’s degree in economics from the Université Paris 1 Panthéon-Sorbonne and a master’s degree in statistics from North Carolina State University.
“From allergies to air pollution, from bacterial and viral infections to indoor exposures, respiratory diseases can have a plethora of causes. The importance of exploring all options and understanding their causes and best treatments requires extreme attention to all facets of the disease and quite frankly, exactly why I love my work in respiratory.”
This is what drives Agustin:
“Over the last 15 years, I have had the opportunity to contribute to many areas of research encompassing a plethora of domains. Because of the multifaceted character of respiratory disease, for a statistician, it too is an area of research providing enormous opportunities. From geographic information systems (GIS) to understanding pollution effects to omics approaches, I’m driven by my passion to understand how the internal (gut) or external (environment) microbiome causes and affects respiratory disease.”
Content by Agustin Calatroni
At Rho, we are proud of our commitment to supporting education and fostering innovative problem-solving for the next generation of scientists, researchers, and statisticians. One way we enjoy promoting innovation is by participating in the annual Industrial Math/Stat Modeling Workshop for Graduate Students (IMSM) hosted by the National Science Foundation-supported Statistical and Applied Mathematical Sciences Institute (SAMSI).
Artificial Intelligence apps use machine learning algorithms to improve the user experience. Machine learning (ML) algorithms make predictions and, in turn, learn from their own predictions resulting in improved performance over time. What does all this mean for clinical research?