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Jun 2024

EJ Daza. Once Upon a Time Series.

I repeatedly get this question from colleagues. And it’s important — foundational, in fact!

The real question they’re asking is: Why would we use anything other than a randomized controlled trial (RCT)* to find real effects that generalize beyond our study sample?

(NCBI Publications: click here)

Jul 2024

JR Rochester, CF Kwiatkowski, I Neveux, S Dabe, KM Hatcher, MK Lathrop, EJ Daza, B Eskenazi, JJ Grzymski, J Hua. Journal of Environmental Research and Public Health.

Background: Interventions are needed to help people reduce exposure to harmful chemicals from everyday products and lifestyle habits. Report-back of individual exposures is a potential pathway to increasing environmental health literacy (EHL) and readiness to reduce exposures.


Objectives: Our objective was to determine if report-back of endocrine-disrupting chemicals (EDCs) can reduce EDC exposure, increase EHL, and increase readiness to change (i.e., to implement EDC exposure-reduction behaviors).


Methods: Participants in the Healthy Nevada Project completed EHL and readiness-to-change surveys before (n = 424) and after (n = 174) a report-back intervention. Participants used mail-in kits to measure urinary biomarkers of EDCs. The report-back of results included urinary levels, information about health effects, sources of exposure, and personalized recommendations to reduce exposure.


Results: EHL was generally very high at baseline, especially for questions related to the general pollution. For questions related to chemical exposures, responses varied across several demographics. Statistically reliable improvements in EHL responses were seen after report-back. For readiness to change, 72% were already or planning to change their behaviors. Post-intervention, women increased their readiness (p = 0.053), while men decreased (p = 0.007). When asked what challenges they faced in reducing exposure, 79% cited not knowing what to do. This dropped to 35% after report-back. Participants with higher propylparaben were younger (p = 0.03) and women and participants who rated themselves in better health had higher levels of some phthalates (p = 0.02–0.003 and p = 0.001–0.003, respectively). After report-back, monobutyl phthalate decreased among the 48 participants who had valid urine tests before and after the intervention (p < 0.001).


Conclusions: The report-back intervention was successful as evidenced by increased EHL behaviors, increased readiness to change among women, and a decrease in monobutyl phthalate. An EHL questionnaire more sensitive to chemical exposures would help differentiate high and low literacy. Future research will focus on understanding why men decreased their readiness to change and how the intervention can be improved for all participants.

Feb 2024

United States Patent Application 20240047042

Provided herein are systems, methods, computer-readable media, and techniques for generating a personalized recommended intervention for a subject based on causal inference, including: obtaining a first set of time series data and a second set of time series data, the first set of time series data relating to a first variable indicative of a health behavior of the subject, and the second set of time series data relating to a second variable indicative of a health condition of the subject; determining a causal effect of the first variable on the second variable by estimating an average treatment effect, wherein the average treatment effect is estimated by processing the first set of time series data and the second set of time series data using a model-twin randomization method; and generating a personalized treatment or intervention recommendation for the subject to change the health condition based on the causal effect.

Nov 2023

E Benito-Garcia, J Vega, EJ Daza, W-N Lee, A Kennedy, J-M Chantelot. JMIR Formative Research.

Background

Collaboration between people with type 2 diabetes (T2DM) and their health care teams is important for optimal control of the disease and outcomes. Digital technologies could potentially tie together several health care-related devices and platforms into connected ecosystems (CES), but attitudes about CES are unknown.


Objective

We surveyed convenience samples of patients and physicians to better understand which patient characteristics are associated with higher likelihoods of (1) participating in a potential CES program, as self-reported by patients with T2DM and (2) clinical benefit from participation in a potential CES program, as reported by physicians.

Methods

Adults self-reporting a diagnosis of T2DM and current insulin use (n=197), and 33 physicians whose practices included ≥20% of such patients, were enrolled in the United States, France, and Germany. We surveyed both groups about the likelihood of patient participation in a CES. We then examined the associations between patients’ clinical and sociodemographic characteristics and this likelihood. We also described characteristics of patients likely to clinically benefit from CES use, according to physicians.


Results

Compared with patients in Germany and France, US patients were younger (mean age 45.3 [SD 11.9] years vs 61.9 [SD 9.2] and 65.8 [SD 9.4] years, respectively), more often female, more highly educated, and more often working full-time. In all, 51 (44.7%) US patients, 16 (36.4%) German patients, and 18 (46.3%) French patients indicated strong interest in a CES program, and 115 (78.7%) reported currently using ≥1 connected device or app. However, physicians believed that only 11.3%-19.2% of their patients were using connected devices or apps to manage their disease. Physicians also reported infrequently recommending or prescribing connected devices to their patients, although ≥80% (n=28) of them thought that a CES could help support their patients in managing their disease. The factors most predictive of patient likelihood of participating in a CES program were cost, inclusion of medication reminders, and linking blood glucose levels to behaviors such as eating and exercise. In all countries, the most common patient expectations for a CES program were that it could help them eat more healthfully, increase their physical activity, increase their understanding of how blood glucose relates to behavior such as exercise and eating, and reduce stress. Physicians thought that newly diagnosed patients, sicker patients—those who had been hospitalized for diabetes, were currently using insulin, or who had any comorbid condition—and patients who were nonadherent to treatment were most likely to benefit from CES use.


Conclusions

In this study, there was a high degree of interest in the future use of CES, although additional education is needed among both patients with T2DM and their physicians to achieve the full potential of such systems to improve self-management and clinical care for the disease.

Aug 2022

I Matias, EJ Daza, K Wac. Digital Health.

Background

Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual.

Objective

Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population.


Methods

We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life.


Results

We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate.

Conclusions

This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

Aug 2022 (In Preparation)

EJ Daza, L Schneider. arXiv.

Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person's own observational time series data. In this paper, we estimate within-individual average treatment effects of physical activity on sleep duration, and vice-versa. We introduce the model twin randomization (MoTR; "motor") method for analyzing an individual's intensive longitudinal data. Formally, MoTR is an application of the g-formula (i.e., standardization, back-door adjustment) under serial interference. It estimates stable recurring effects, as is done in n-of-1 trials and single case experimental designs. We compare our approach to standard methods (with possible confounding) to show how to use causal inference to make better personalized recommendations for health behavior change, and analyze 222 days of Fitbit sleep and steps data for one of the authors.

May 2022

A Mezlini, A Shapiro, EJ Daza, E Caddigan, E Ramirez, T Althoff, L Foschini. JAMA Network Open.​

Question: How can the true burden of influenza-like illnesses (ILIs) be estimated given that most cases of ILIs are mild and go undocumented?

Findings: This cohort study of 15 122 adults who reported ILI symptoms and had data from wearable sensors at symptom onset found an overall reduction in mobility equivalent to 15% of the active US population becoming completely immobilized for 1 day. More than 60% of this reduction occurred among persons who had sought no medical care.


Meaning: This study suggests that the burden of ILIs is much greater than had previously been understood.

Feb 2021

Editors: J Nikles, EJ Daza, S McDonald, E Hekler, NJ Schork. Frontiers in Psychiatry, Psychology, Digital Health, Neurology, Public Health, and Sociology.


N-of-1 randomized controlled trials (RCTs) provide an opportunity to evaluate individual patient response to interventions, by randomly allocating different time periods within an individual to repeated intervention and control conditions and comparing responses. N-of-1 observational studies involve the repeated measurement of an outcome (e.g. pain) in a patient over time, but with no intervention implemented, in order to draw conclusions about naturally-occurring patterns and predictors of outcomes over time.


Both N-of-1 RCTs and observational studies can have a ‘self-study’ design, where an individual conducts the study on themselves, to answer research questions they have generated themselves. N-of-1 RCTs and observational studies provide individualized findings that can be aggregated to produce results equivalent to those found in traditional group-based RCTs and population-level epidemiological studies, respectively, but requiring fewer patients for the same power. N-of-1 RCTs and observational studies are well-suited to complement, strengthen, and generate advances in precision medicine, patient-centred healthcare, and personalised health. Since 2015, the number of N-of-1 articles has doubled annually.


Similarly, digital health is an exploding field, with over 1,000 studies registered on clinicaltrials.gov. Digital health, and digital therapeutics in particular, complement N-of-1 RCTs and observational studies by providing relevant individualized health data from, for example, worn sensors, implants, regular lab assays, or -omics sequencing. Such data can be compared to population-health databases to target a patient’s strongest possible treatment option (as in cancer-risk studies) and, in turn, inform the design of an N-of-1 RCT to evaluate it. Digital health data can also be continuously monitored during the study itself and used to help tailor a treatment to the needs and preferences of patients in real time.

This Research Topic will cover digital health applications, delivery, and analysis of N-of-1 RCTs and observational studies (including self-studies) in any health discipline. The focus is on:

  • mobile health (mHealth) and applications (apps)

  • wearable devices, sensors and implants,

  • real-time tracking, data analytics and online registries,

  • patient experience of digital health and mobile health, patients as collaborators in personalised medicine, self-tracking in citizen science, etc.


The articles can be original research, methodology pieces, opinion pieces, reviews, systematic reviews, protocols, short reports, or case studies.

Jan 2020

EJ Daza, K Wac, M Oppezzo. Healthcare.


Sleep deprivation is a prevalent and rising health concern, one with known effects on blood glucose (BG) levels, mood, and calorie consumption. However, the mechanisms by which sleep deprivation affects calorie consumption (e.g., measured via self-reported types craved food) are unclear, and may be highly idiographic (i.e., individual specific). Single-case or “n-of-1” randomized trials (N1RT) are useful in exploring such effects by exposing each subject to both sleep deprivation and baseline conditions, thereby characterizing effects specific to that individual. We had two objectives: (1) To test and generate individual-specific N1RT hypotheses of the effects of sleep deprivation on next-day BG level, mood, and food cravings in two non-diabetic individuals; (2) To refine and guide a future n-of-1 study design for testing and generating such idiographic hypotheses for personalized management of sleep behavior in particular, and for chronic health conditions more broadly. We initially did not find evidence for an idiographic effect of sleep deprivation, but better-refined post hoc findings indicate that sleep deprivation may have increased BG fluctuations, cravings, and negative emotions. We also introduce an application of mixed-effects models and pancit plots to assess idiographic effects over time.

Jan 2019 (In Preparation)

EJ Daza. arXiv.


Single-subject health data are becoming increasingly available thanks to advances in self-tracking technology (e.g., mobile devices, apps, sensors, implants). Many users and health caregivers seek to use such observational time series data to recommend changing health practices in order to achieve desired health outcomes. However, there are few available causal inference approaches that are flexible enough to analyze such idiographic data. We develop a recently introduced framework, and implement a flexible random-forests g-formula approach to estimating a recurring individualized effect called the "average period treatment effect". In the process, we argue that our approach essentially resembles that of a longitudinal study by partitioning a single time series into periods taking on binary treatment levels. We analyze six years of the author's own self-tracked physical activity and weight data to demonstrate our approach, and compare the results of our analysis to one that does not properly account for confounding.

Feb 2018

EJ Daza. Methods of Information in Medicine.

I'm very proud of this piece. It's clunky, lumbering, and overwrought. Still, I hope I did the source material justice in my first true (impostor-syndromic) attempt at telling an honest story of a single person's health habits through the language of doubt.

Conclusions. Causal analysis of an individual's time series data can be facilitated by an n-of-1 randomized trial counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful.

Sep 2017

ML Nguyen, J Hu, K Hastings, E Daza, M Cullen, L Orloff, L Palaniappan. Cancer.


Conclusions. Negative prognostic factors for thyroid cancer traditionally include age >45 years and male sex. The results of the current study demonstrate that Filipinos die of thyroid cancer at higher rates than NFA and NHW individuals of similar ages. Highly educated Filipinos and Filipino women may be especially at risk of poor thyroid cancer outcomes. Filipino ethnicity should be factored into clinical decision making in the management of patients with thyroid cancer.

Jul 2017

EJ Daza, MG Hudgens, AH Herring. The Stata Journal.


Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipwcommand can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study.

Jan 2017

AP Keil, EJ Daza, SM Engel, JP Buckley, JK Edwards. Statistical Methods in Medical Research.


Epidemiologists often wish to estimate quantities that are easy to communicate and correspond to the results of realistic public health interventions. Methods from causal inference can answer these questions. We adopt the language of potential outcomes under Rubin’s original Bayesian framework and show that the parametric g-formula is easily amenable to a Bayesian approach. We show that the frequentist properties of the Bayesian g-formula suggest it improves the accuracy of estimates of causal effects in small samples or when data are sparse. We demonstrate an approach to estimate the effect of environmental tobacco smoke on body mass index among children aged 4–9 years who were enrolled in a longitudinal birth cohort in New York, USA. We provide an algorithm and supply SAS and Stan code that can be adopted to implement this computational approach more generally.

Dec 2016

CG Brown-Johnson, A Burbank, EJ Daza, A Wassmann, A Chieng, GW Rutledge, JJ Prochaska. American Journal of Preventive Medicine.


Conclusions. Examination of online patient–provider communications provides insight into consumer health experience with emerging alternative tobacco products. Patient concerns largely related to harms and safety, and patients preferred provider responses positively inclined toward e-cigarettes. Lacking conclusive evidence of e-cigarette safety or efficacy, healthcare providers encouraged smoking cessation and recommended first-line cessation treatment approaches.

May 2016

JJ Prochaska, AK Michalek, C Brown-Johnson, EJ Daza, M Baiocchi, N Anzai, A Rogers, M Grigg, A Chieng. JAMA Internal Medicine.


Conclusions and Relevance. To our knowledge, this is the first study to prospectively track reemployment success by smoking status. Smokers had a lower likelihood of reemployment at 1 year and were paid significantly less than nonsmokers when reemployed. Treatment of tobacco use in unemployment service settings is worth testing for increasing reemployment success and financial well-being.

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