Eric J. Daza, DrPH, MPS

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I help people appreciate data, science, and each other.

Videos

Pivot the Randomized Control Trial - One Participant at a Time with Eric J. Daza, DrPH, MPS | E216

2024 Oct 22 | The Public Health Millenial

Highlights from this episode:

  1. How a data scientist and biostatistician merged his experience with public health education
  2. Why language in stats can significantly impact understanding and communication of health information
  3. Learn more about p-value and statistical significance
  4. What is the concept of N of 1 studies and why the use of these personalized health interventions can have tremendous impact in public health
  5. How he realized that grades are not the only measure of success in education (and why you should too)
  6. How contract roles can lead to full time opportunities

PAASE Webinar: Discovering Your Own Health Behavior Causal Effects Using Wearables and Apps

2024 Sep 06 | Philippine-American Academy of Science and Engineering (PAASE)

Temporally dense single-person “small data” are widely available from mobile apps (patient-reported outcomes) and wearable sensors. Caregivers and self-trackers want to use these intensive longitudinal data to guide person-specific behavior change. Ideally, this involves discerning possible causes from correlations using that person’s own observational time series data. In paper one, we estimate within-individual recurring average treatment effects of physical activity on sleep duration. We introduce the model-twin randomization (MoTR; “motor”) and propensity score twin (PSTn; “piston”) methods. MoTR is a Monte Carlo implementation of the g-formula (i.e., back-door adjustment); PSTn implements propensity score inverse probability weighting. Both estimate stable recurring idiographic effects, as done in n-of-1 trials and single case experimental designs. We apply both methods to the authors’ own data to show how to use causal inference to make truly personalized recommendations for health behavior change. In paper two, we show examples of how suggested effects for one individual differ greatly from those of others, and provide a guide for using MoTR to investigate your own recurring health conditions.

Distinguished Speaker Series: Discovering Your Own Health Behavior Causal Effects Using Wearables and Apps

2024 Feb 02 | University of South Carolina: Research Center for Child Well-Being (RCCWB)

Building Successful Mentor/Mentee Relationships in the Hybrid Work Era

2023 Oct 16 | Building Successful Mentor/Mentee Relationships in the Hybrid Work Era

It is no secret that many institutions are embracing remote and hybrid working environments. This change has far-reaching implications, including for how statisticians and data scientists initiate and build their careers. It begs the following questions: How can those in our field support statisticians and data scientists in this new work era? How can we continue to embrace JEDI principles in our support?

To begin exploring this topic, the JEDI Outreach Group held a webinar on October 16, 2023, titled “Building Successful Mentor/Mentee Relationships in the Hybrid Work Era.” Michael Dumelle from the US Environmental Protection Agency and Therri Usher from the US Food and Drug Administration moderated the webinar featuring the following panelists:

The Power of Biostatistics: Eric J. Daza Holds the Key to Evolving the Healthcare Industry

2023 Mar 02 | ASA This Is Statistics Interview

We caught up with Eric at the 2022 Joint Statistical Meetings (JSM), the world’s largest annual gathering of statisticians and data scientists, to learn more about his passion for biostatistics, how his work makes a difference in healthcare, and advocacy for justice, diversity, equity and inclusion in the field.

PhD Panel Discussion: Pitfalls in Leaving Academia

2023 Jan 19 | Victoria Wobber Coaching and Consulting

Curious about pitfalls to avoid in leaving academia? Learn the challenges panellists faced in leaving academia and hear their advice for you.

Eric Daza | Important Ideas in Causal Inference

2022 Jul 11 | Data & Science with Glen Wright Colopy (podcast)

Andrew Gelman and Aki Vehtari wrote a paper titled, “What are the most important statistical ideas of the past 50 years?”. The first idea in the list is “counterfactual causal inference”. Eric Daza (Evidation Health) walks us through the main ideas of the Gelman & Vehtari paper, drawing examples from several fields, including medical & healthcare statistics.

Big Data Principles by Eric J. Daza

2022 Mar 22 | Teach in Two

Learn Big Data Principles and its Effect On Data Collection with Eric Daza in under two minutes.

StoRies from Biostatistics to Health Data Science

2021 Sep 30 | noRth 2021

This talk surveys various R-related activities in biostatistics and health data science.

Data Science in the Biomedical Industry

2021 Sep 14 | Academic Data Science Alliance (ADSA)

This career symposium is designed to offer attendees the opportunity to hear from diverse and experienced data scientists about their education and career paths, the skills expected of these positions, where and how to seek these types of positions, and what to expect when working in these fields.

Statistical considerations for successful digital health innovation

2021 Aug 26 | Digital Medicine Society (DiMe)

Why should you report your modeling plan or statistical analysis plan before seeing any data? Why should we all ditch the term ‘statistical significance’ but keep statistical evidence? And how? A fantastic discussion with Eric Daza, Lead Statistician for Digital Health Outcomes at Evidation Health, as he dives into key themes from his recent pieces: Artifice or intelligence? and Ditch ‘statistical significance’.

N-of-1 Science & Causal Inference | Philosophy of Data Science

2021 Jun 14 | Data & Science with Glen Wright Colopy (podcast)

Much of our scientific inference revolves around the identification and replication of patterns in data. So what can be done when N=1? Eric Daza gives us a statistician’s perspective on the ideas behind N-of-1 studies, its best examples, and strongest critiques.

Epidemiology-of-1: Causal Inference via Single-case Observational Design for Sleep and Physical Activity Wearables Data

2021 Apr | Society of Behavioral Medicine (SBM) 42nd Annual Meeting

2-minute non-technical video explainer of Daza EJ, Matias I, Schneider L. Model-Twin Randomization (MoTR) for Estimating One’s Own Recurring Individual Treatment Effect. Statistics in Medicine. 2024 Oct 1 under review. arxiv.org/abs/2208.00739

Tracking activity patterns in those recently infected by COVID-19

2021 Jan-Jun | Central Coast Data Science Partnership

The aim of this project is to discover methods of pre-symptomatic COVID-19 detection using data collected from wearables. Using a recent study, “Pre-symptomatic detection of COVID-19 from smartwatch data” (Mishra et al), as the baseline for research and the source of data for analysis, the Evidation team will use the described outlier detection algorithms to determine the likelihood of COVID-19 infection in an individual given their heart rate, step count, and sleep data.

FilipinxAms in Healthtech + Safety During COVID19

2020 Jun 30 | FASTER-STEAM

Charity will discuss her background as a health and safety expert, the Return to Work Guidelines and her current work with various tech companies on re-opening during COVID19 and its impact on communities and communities of color.

Eric will discuss his professional background in healthtech and technical and creative contributions in STEAM in general and also by communities of color during COVID19.

Design Trumps Analysis: Drawing Causal Conclusions using Big Data

2017 Oct 11 | Data Science Philippines, Asian Institute of Management

This talk provides a high-level, fairly non-technical introduction to causal discovery using big data; i.e., how to carefully draw causal conclusions from big data analyses. Two general, complementary approaches for causal discovery will briefly be illustrated in the context of big data analysis: 1.) mechanism-focused and structural approaches using causal graphs, and 2.) the effect-focused statistical framework of potential outcomes (emphasis on the latter).

Three Statistically Significant Principles

2017 Mar 15 | Quantified Self Article

This is a short presentation I gave at the Quantified Self Bay Area Meetup event titled “Show & Tell #41” on March 15, 2017. Summary:

  1. Big data does not imply big accuracy. (S)
  2. Significance does not imply importance. (S)
  3. Correlation does not imply causation. (P)
  4. Causation can imply correlation. (P)

A Statistical New World

2011 Aug | UNC Biostatistics Article

Some excellent grad school friends of mine created this fun musical take (based on Disney’s “A Whole New World”) on what it’s like to be a statistician—and asked me to perform and handle music production! Here’s our winning music video from the 2011 American Statistical Association “Promoting the Practice and Profession of Statistics” Video Competition.