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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).

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14 Jun 2021

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19 Apr 2021

How will value-based care drive digital health companies to show outcomes and improve evidence generation?

13 Apr 2021

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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

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For smokers, finding a job is harder

11 Apr 2016


Cigarette smoking could burn your job prospects

11 Apr 2016

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Smokers Less Likely to Get Hired and Earn Less: Study

11 Apr 2016

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