Ariel Williams

Research Fellow, National Institutes of Health (NIH)

11 active projects

Heart_disease

The scientific question I intend to answer is to determine if being covid positive increases the risk of having a heart event.

Scientific Questions Being Studied

The scientific question I intend to answer is to determine if being covid positive increases the risk of having a heart event.

Project Purpose(s)

  • Disease Focused Research (heart disease)

Scientific Approaches

I plan to perform a survival analysis, COX proportional hazard model and running a logistic regression model (PheWAS).

Anticipated Findings

I intend to find that being covid positive increase the risk of having a heart event. We also expected to see differences in the phenome wide associations analysis between people who had a heart event after covid and the people who didn't.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Ariel Williams - Research Fellow, National Institutes of Health (NIH)

Rheumatoid Arthritis Analysis 2

Prevalence of Rheumatoid Arthritis in AoU and the phenotypes associated after conditioning for different co-morbidities, medications, gender, race, etc.

Scientific Questions Being Studied

Prevalence of Rheumatoid Arthritis in AoU and the phenotypes associated after conditioning for different co-morbidities, medications, gender, race, etc.

Project Purpose(s)

  • Disease Focused Research (rheumatoid arthritis)

Scientific Approaches

Mainly statistical and machine learning modeling and PheWAS software for determining RA diagnosis and associated diagnoses as well as outcomes. The goal of this research is to compare findings across other datasets and especially the differences in phenotypes across different sites, as explained by the models.

Anticipated Findings

We expect that on average our models will find consistent markers for RA diagnoses across sites, however there will likely be large outliers. RA phenotypes will not likely be a surprise, since this is a commonly researched disease area.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Covid

This workspace is designed to investigate covid patients in All of Us and compare them to the covid positive patients in National Covid Cohort Collaborative. I want to learn the differences in demographic, disease risk and mortality.

Scientific Questions Being Studied

This workspace is designed to investigate covid patients in All of Us and compare them to the covid positive patients in National Covid Cohort Collaborative. I want to learn the differences in demographic, disease risk and mortality.

Project Purpose(s)

  • Disease Focused Research (Covid)

Scientific Approaches

I plan to run a logistic regression to uncover phenotypes associated with covid. I will be using the PheWAS tool for comparison.

Anticipated Findings

I anticipate the N3C will have more phenotypes associated with covid than All of Us. N3C is a databased designed to study Covid and All of Us is a disease neutral database. I except to see a wider range of phenotypes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Duplicate of Hypertension_Analysis

What disease phenotypes are associated with hypertension? I intend to use electronic health record to extract phenotypes not normally associated with hypertension.

Scientific Questions Being Studied

What disease phenotypes are associated with hypertension? I intend to use electronic health record to extract phenotypes not normally associated with hypertension.

Project Purpose(s)

  • Disease Focused Research (hypertension)

Scientific Approaches

I intend to build dataset that include people with and with hypertension and make comparisons between the two cohorts. I will uses the phewas package to compare diseases associated with these cohorts.

Anticipated Findings

I anticipate finding different correlations of disease between the case and control cohorts. These findings will show how we can use information contained in electronic health records.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Ariel Williams - Research Fellow, National Institutes of Health (NIH)

Duplicate of Erwin Update: DJS: Duplicate of JAMA PheWAS Final Review 07-06-2021

As a demonstration project, this study will present the results of Phenome-Wide Association Studies (PheWAS) to show how the various sources of data contained within All of Us research dataset can be used to inform scientific discovery. We will perform…

Scientific Questions Being Studied

As a demonstration project, this study will present the results of Phenome-Wide Association Studies (PheWAS) to show how the various sources of data contained within All of Us research dataset can be used to inform scientific discovery. We will perform separate PheWAS studies with smoking status as the independent variable. Specific questions include:

1. How can one implement a PheWAS within the All of Us Researcher Workbench?
2. How can one use heterogeneous data sources within the All of Us dataset to explore disease associations using self-reported exposures (Participant Provided Information, or “PPI”) and exposures captured in the electronic medical record (EHR).”

There is no pre-specified hypothesis. It is important to determine if PheWAS can be conducted within the All of Us workbench

Project Purpose(s)

  • Methods Development
  • Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use.)

Scientific Approaches

As a demonstration project, this study will present the results of Phenome-Wide Association Studies (PheWAS) to show how the various sources of data contained within All of Us research dataset can be used to inform scientific discovery. We will perform separate PheWAS studies with smoking status as the independent variable. Specific questions include:

1. How can one implement a PheWAS within the All of Us Researcher Workbench?
2. How can one use heterogeneous data sources within the All of Us dataset to explore disease associations using self-reported exposures (Participant Provided Information, or “PPI”) and exposures captured in the electronic medical record (EHR).”

There is no pre-specified hypothesis. It is important to determine if PheWAS can be conducted within the All of Us workbench

Anticipated Findings

For this study, we anticipate that we will be able to replicate known disease associations with smoking exposure. This will serve to demonstrate the quality, utility, and diversity of the All of Us data and tools and the power of gathering multiple data sources for a single phenotype, providing researchers options for study design and validation. Importantly the entire PheWAS package is made available for reuse by researchers in the Workbench, for new hypothesis generation.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Fitbit Data Obesity Project

We intend on exploring how continuously monitored measurements correlate with obesity measures in the AoU cohort.

Scientific Questions Being Studied

We intend on exploring how continuously monitored measurements correlate with obesity measures in the AoU cohort.

Project Purpose(s)

  • Disease Focused Research (morbid obesity)

Scientific Approaches

We first intend on extracting fitbit data as obesity phenotypes and define appropriate cohorts accordingly. Next we will use statistical analyses to explore the relationships between physical activity and obesity.

Anticipated Findings

We expect to see significant correlation between fitbit usage and obesity. We also expect to see fluctuations in measurement to be reflected in BMI measurement.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Duplicate of Hypertension_Analysis

What disease phenotypes are associated with hypertension? I intend to use electronic health record to extract phenotypes not normally associated with hypertension.

Scientific Questions Being Studied

What disease phenotypes are associated with hypertension? I intend to use electronic health record to extract phenotypes not normally associated with hypertension.

Project Purpose(s)

  • Disease Focused Research (hypertension)

Scientific Approaches

I intend to build dataset that include people with and with hypertension and make comparisons between the two cohorts. I will uses the phewas package to compare diseases associated with these cohorts.

Anticipated Findings

I anticipate finding different correlations of disease between the case and control cohorts. These findings will show how we can use information contained in electronic health records.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Ariel Williams - Research Fellow, National Institutes of Health (NIH)

Hypertension_Analysis

What disease phenotypes are associated with hypertension? I intend to use electronic health record to extract phenotypes not normally associated with hypertension.

Scientific Questions Being Studied

What disease phenotypes are associated with hypertension? I intend to use electronic health record to extract phenotypes not normally associated with hypertension.

Project Purpose(s)

  • Disease Focused Research (hypertension)

Scientific Approaches

I intend to build dataset that include people with and with hypertension and make comparisons between the two cohorts. I will uses the phewas package to compare diseases associated with these cohorts.

Anticipated Findings

I anticipate finding different correlations of disease between the case and control cohorts. These findings will show how we can use information contained in electronic health records.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Ariel Williams - Research Fellow, National Institutes of Health (NIH)

Diabetes Comparison Workspace

What are the differences in A1c levels between diabetic and control populations and how do these comparisons vary when controlling for other covariates (age, gender, race, demographic information).

Scientific Questions Being Studied

What are the differences in A1c levels between diabetic and control populations and how do these comparisons vary when controlling for other covariates (age, gender, race, demographic information).

Project Purpose(s)

  • Other Purpose (This workspace's main purpose will be to provide a place to learn first hand how to create and analyze data from All of Us. The "research aim" of this project will be to compare diabetes patients and control patients, however this is only meant as a directive for the ultimate purpose of better understanding workspace creation and analysis in AoU. )

Scientific Approaches

We plan to use simple comparative statistical analyses such as t-tests and Bayesian analyses to explore group differences. Linear regression and more advanced modeling techniques (regularized regression, tree based methods) may be used to further define differences between the groups. Most of the analysis will be conducted in R.

Anticipated Findings

Anticipated findings are that A1c levels are higher among diabetes and prediabetes patients than controls.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Collaborators:

  • Josh Denny - Other, All of Us Program Operational Use

COPE survey analysis

We are interested in using the COPE survey questions and answers from respondents to see how differences may occur by geographic location and demographic information. Specifically, we are interested in social distancing and mental health questions.

Scientific Questions Being Studied

We are interested in using the COPE survey questions and answers from respondents to see how differences may occur by geographic location and demographic information. Specifically, we are interested in social distancing and mental health questions.

Project Purpose(s)

  • Disease Focused Research (COVID-19)

Scientific Approaches

We plan to use the COPE survey data, linked patient information, geographic information for each site, (possibly) medication data, and condition occurrence data. We plan to use phenome wide association studies (PheWAS) in order to determine likely phenotypes associated with outcomes of interest, such as social distancing measures.

Anticipated Findings

We anticipate to find those with debilitating diseases to be more concerned with social distancing, however we are unsure which diseases will have greater association. We also suspect that measures of depression and loss due to covid to be highly associated with more social distancing. We propose that these PheWAS analyses and investigations into differences of social distancing and mental health will heavily contribute to the field of study on COVID-19 and sociological and behavioral health effects.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

N3C Comparison

We plan to provide phecode counts and percentages across COVID-19 diagnosis, both of enrolled participants and EHR observations for AoU and N3C. We plan to stratify by demographics of interest and age and compare measures of relative risk and odds…

Scientific Questions Being Studied

We plan to provide phecode counts and percentages across COVID-19 diagnosis, both of enrolled participants and EHR observations for AoU and N3C. We plan to stratify by demographics of interest and age and compare measures of relative risk and odds ratios between AoU and N3C COVID-19 positive and negative participants. This analysis is at a high level, but provides some valuable measures to compare common diagnoses and comorbidities associated with COVID-19. It also provides a good measure for differences in conditions between AoU and N3C participants.

Project Purpose(s)

  • Disease Focused Research (COVID-19)

Scientific Approaches

We plan to use N3C OMOP compliant datasets in order to compare diagnostic level counts with AoU. Mainly will be working with SQL, R, and Python programming languages to merge this information and conduct the broad analysis.

Anticipated Findings

We anticipate that some common respiratory, disease, and heart complications will be more common in the COVID-19 positive patients from N3C when compared to phenotypes from our AoU cohort. We do not have great expectations when comparing the patients not exhibiting COVID-19 in N3C with the AoU population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

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