Presentations
05 Dec 2022
HOST: School of Public Health | Department of Biostatistics
TITLE: Using Wearables and Apps to Characterize Your Own Recurring Average Treatment Effects
10 Aug 2022
TITLE: Beyond Precision Medicine: Making It Personal with N-of-1 and Single Case Methods for Medicine, Rare Diseases, Digital Health, Behavior, and Wearables — Topic Contributed Papers
23-25 May 2022
HOST: University of California, Berkeley | School of Public Health | Center for Targeted Machine Learning and Causal Inference
TITLE: Model-Twin Randomization (MoTR): A Monte Carlo Method for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors
28 Jan 2022
HOST: Bloomberg School of Public Health | Department of Biostatistics | Wearable and Implantable Technology (WIT) Research Group
TITLE: Model-Twin Randomization (MoTR): A Monte Carlo Method for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors
3rd Annual Health Data Science Symposium at Harvard: Smartphones, Wearables, and Health | Boston, MA, USA — Talk
05 Nov 2021
HOST: Brigham & Women's Hospital / Harvard Medical School Department of Neurosurgery's Computational Neuroscience Outcomes Center (CNOC) + Harvard School of Public Health Onnela Lab
TITLE: MoTR + PSTn: Building a Causal Engine For Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors
20 Oct 2021
HOST: School of Public Health | Division of Biostatistics
TITLE: MoTR and PSTn: Building a Causal Engine for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors
30 Sep 2021
TITLE: StoRies from Biostatistics to Health Data Science
ABSTRACT: This talk surveys various R-related activities in biostatistics and health data science.
10 Aug 2021
TITLE: MoTR and PSTn: Building a Causal Engine for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors
12-16 Apr 2021
TITLE: Epidemiology-of-1: Causal Inference via Single-case Observational Design for Sleep and Physical Activity Wearables Data
ABSTRACT: Temporally rich single-subject health data have become increasingly available thanks to wearable devices, mobile apps, sensors, and implants. Many health caregivers and “self-trackers” want to use such information to help a specific person figure out how to change their behavior to achieve desired health outcomes. However, this requires an approach for discerning possible causes from correlations using that person’s own observational time series data. In this paper, we posit and estimate some plausible idiographic average treatment effects of sleep duration on physical activity. We use a recently developed causal inference framework based on n-of-1 randomized trials to analyze one year of the lead author’s Fitbit sleep duration and step count data. We then compare our findings to those of standard methods that do not account for confounding to show that causal inference is needed to make realistic recommendations for personal behavior change.
13 Nov 2020
TITLE: Making it Count: Statistics and Data Science in Public Health
ABSTRACT: Understanding and shaping the health of populations requires both qualitative and quantitative scientific methods. Statistical concepts are used to structure and manage the uncertainty inherent in the scientific study of human life, behavior, and society. This workshop presents an overview of this quantitative discipline’s role in the field of public health—in biostatistics and epidemiology in particular.
28 Jul 2019
TITLE: Person as Population: a Longitudinal View of Single-Subject Causal Inference for Analyzing Self-Tracked Health Data
11 Oct 2017
HOST: Data Science Philippines
TITLE: Design Trumps Analysis: Drawing Causal Conclusions using Big Data
ABSTRACT: 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).
Science Communication
The Power of Biostatistics: Eric J. Daza Holds the Key to Evolving the Healthcare Industry
02 Mar 2023
FiT Feature: Eric Daza
28 Feb 2023
Eric Daza | Important Ideas in Causal Inference
11 Jul 2022
21 Jun 2022
Big Data Principles by Eric J. Daza
22 Mar 2022
Learn Big Data Principles and its Effect On Data Collection with Eric Daza in under two minutes.
A Moment with Eric Daza: On N-of-1 Trials and Precision Medicine
02 Dec 2021
Why You Should Think of the Enterprise of Data Science More Like a Business, Less Like Science
22 Sep 2021
#MemeMedianMode Contest Winner!
16 Sep 2021
ADSA Career Development Network Career Panel: Data Science in the Biomedical Industry
14 Sep 2021
Statistical considerations for successful digital health innovation
26 Aug 2021
Eric Daza | N-of-1 Science & Causal Inference | Philosophy of Data Science
14 Jun 2021
Get to know Evidation: Eric J. Daza, ReAL Team
19 Apr 2021
How will value-based care drive digital health companies to show outcomes and improve evidence generation?
13 Apr 2021
Jan-Jun 2021
Central Coast Data Science Partnership: Mentoring Project led by the University of California Santa Barbara and Evidation Health
FASTER Live AMA: FilipinxAms in Healthtech + Safety
30 June 2020
Pinoy Scientists Instagram Weeklong Takeover
12-18 Jan 2020
Three Statistically Significant Principles
15 Mar 2017
Physician advice to patients on e-cigarettes varies, reveals knowledge gaps, study shows
26 Aug 2016
Science Daily
For smokers, finding a job is harder
11 Apr 2016
Reuters
Cigarette smoking could burn your job prospects
11 Apr 2016
CBS News
Smokers Less Likely to Get Hired and Earn Less: Study
11 Apr 2016
NBC News