Data visualisation is one of my favorite topics and today, I’ll be interviewing someone who has helped me understand about data visualisation for years.
Stay tuned while we dive into the following interesting points:
- How he got interested in data visualisation
- What were the key things he wanted to communicate about the mixed model
- What him to submit so regularly the data visualisations to the Wonderful Wednesday
- Which submission is he most proud of and why
- What has he learned from the Wonderful Wednesday Webinars so far
- What does he think about the future of data visualisations in our industry
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Agustin Calatroni
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.
He 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.
He 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.
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This group was set up to help each other to become more effective statisticians. We’ll run challenges in this group, e.g. around writing abstracts for conferences or other projects. I’ll also post into this group further content.
I want to help the community of statisticians, data scientists, programmers and other quantitative scientists to be more influential, innovative, and effective. I believe that as a community we can help our research, our regulatory and payer systems, and ultimately physicians and patients take better decisions based on better evidence.
I work to achieve a future in which everyone can access the right evidence in the right format at the right time to make sound decisions.
When my kids are sick, I want to have good evidence to discuss with the physician about the different therapy choices.
When my mother is sick, I want her to understand the evidence and being able to understand it.
When I get sick, I want to find evidence that I can trust and that helps me to have meaningful discussions with my healthcare professionals.
I want to live in a world, where the media reports correctly about medical evidence and in which society distinguishes between fake evidence and real evidence.
Let’s work together to achieve this.
