ERIC J. DAZA

Eric J. Daza (pronouns: he/him/siya) created Stats-of-1, a digital health newsletter for promoting the expanded use of n-of-1 trials, single-case designs, switchback experiments, and other individual-focused (personalized/precision) statistical approaches—for health and medicine in particular. These approaches can truly personalize health insights, diagnosis, and treatment in a way traditional clinical and biomedical statistics—and even many precision medicine, machine learning, and AI approaches—fundamentally cannot. For this innovative work, he was recognized by Forbes Magazine and Fortune Magazine in 2022, and by the American Statistical Association in 2023. He is also a General Member of the International Collaborative Network for N-of-1 Clinical Trials and Single-Case Experimental Designs.

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Dr. Daza is a biostatistician and health data scientist. He has worked for 21+ years in industry and academia, in pharma clinical trials, survey sampling, nutrition, maternal/child health, global/international health, health promotion and disease prevention, healthtech, digital health, and behavioral medicine.

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Daza is the 2024 Chairperson-Elect of the Justice, Equity, Diversity, and Inclusion (JEDI) Outreach Group at the American Statistical Association. In 2022 and 2023, he served as the Professional Development Committee (PDC) Chairperson. He has been an ASA JEDI PDC Member since 2021.

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As a privileged middle-class Brown Asian immigrant from the Philippines, and as a neurodivergent person with ADHD, Daza earned both his BA in Neurobiology / Cognitive Studies and MPS in Applied Statistics at Cornell University, followed by his DrPH in Biostatistics at the University of North Carolina at Chapel Hill. He then trained as a postdoc at the Stanford Prevention Research Center. He is also Jesuit-trained.

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Daza created the first complete n-of-1 causal inference framework: the average period treatment effect (APTE) n-of-1 counterfactual framework for understanding each person’s own health causes and effects using their own self-tracked wearable/sensor data, patient-reported outcomes, clinical outcomes, and biomarkers (Daza, 2018; clearer LaTeX notation here). He then used his framework to introduce the patent-pending MoTR ("motor") method for estimating such individual-specific causal effects (Daza, Matias, and Schneider, 2024 under review; 2-min explainer video here).​

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The APTE framework for "Granger causal inference" is grounded in n-of-1 trials and single-case designs. It accommodates both experimental (RCTs, A/B tests, experimentation) and observational (real-world) partitioned multivariate time series, heterogeneous treatment effects, functional data analysis, micro-randomized trials, dynamic treatment regimes, and multilevel/hierarchical/ mixed-effects models.

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Here's a fun video by Daza's brilliantly witty UNC friends in which he plays a singing statistics professor: A Statistical New World (see Videos page for more clips)

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• "Uncertain times call for certain measurements." (Science, Apr 2017)​

• "Significance of evidence is not evidence of significance."  (Towards Data Science, Jun 2021)

• "Significance does not imply importance."

• "Significance of evidence is not evidence of significance."​

• "Empathize exactly, communicate concisely."

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(photo by Monica Semergiu)