Data Studio

We foster dialogue between data scientists and researchers in clinics and laboratories in order to drive excellence in health care research at Stanford.

About the Data Studio

The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational Research and Education) and the Department of Biomedical Data Science. The Data Studio is open to the Stanford community engaged in biomedical research. We expect it to have educational value for students and postdocs interested in biomedical data science. The Data Studio features DBDS faculty and staff who offer the following services: workshops, office hours, and one-to-one consultations. When you complete the Data Studio request form, our coordinator and consultants will work with you to choose the right service for your research project.

Workshops are an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of our facilitator. During the Data Studio Workshop, the researcher explains the project, goals, and needs. Experts in the related topic from across campus will be invited and contribute to the brainstorming. After the meeting, the facilitator will follow up, helping with immediate action items and summary of the discussion. Ultimately, we strive to pair each PI with a data scientist for long-term collaboration.

Office Hours are brief consultations for Medical School researchers during the last session of each month. DBDS faculty are available to advise about your research questions. Consult the schedule below to complete the Office Hour registration form. Once you have registered, you will receive a calendar invitation with the date, time, and location of the session. Bring any data, prior analyses, or other materials that you have. Our consultants may even recommend your project for a Workshop if it is appropriate.

One-to-one consultations for Medical School researchers are available year-round. Our facilitator assigns each request to a data scientist with the relevant expertise.

Partners

General questions about statistical issues may be brought to the STAT390 Consulting Workshop. This is a class offered by the Department of Statistics during each academic quarter that is staffed by graduate students and directed by a faculty instructor. The service typically consists of a single meeting with the researcher to address a specific concern, such as planning of experiments and data analysis. Appointments may be requested by completing the required form. For more information, consult the STAT390 Consulting Workshop web page.

Researchers who are members of the Stanford Cancer Institute (SCI) conducting research projects related to cancer may request assistance from the SCI Biostatistics Shared Resource.

Schedule

The Data Studio is held each Wednesday from 1:30 until 3:00pm during the fall, winter, and spring quarters of the academic year. Consult the schedule below for the location of each session. Students may participate by enrolling in BIODS 232 for an introduction to the art of statistical consultation and practicum working on projects with a biomedical researcher. All are welcome to attend. Click here to sign up for our mailing list.

Currently scheduled topics are listed below, followed by links to topics and summaries from previous quarters.

Winter 2023

TITLE: Transversus abdominis plane blocks in autologous breast reconstruction

DATE: Wednesday, 1 February 2023
TIME: 1:30–3:00 PM

INVESTIGATORS:
Thomas Johnstone (1)
Gordon Lee (1)
Afaff Shakir (2)
(1) Department of Surgery, Stanford University School of Medicine
(2) Department of Surgery, University of Chicago Medicine
 
LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA
 
WEBPAGE: http://med.stanford.edu/dbds/resources/data-studio.html
 
ABSTRACT
The Data Studio Workshop brings together a biomedical investigator with a group of experts for an in-depth session to solicit advice about statistical and study design issues that arise while planning or conducting a research project. This week, the investigator(s) will discuss the following project with the group.
 
INTRODUCTION: Breast cancer is the most frequently diagnosed cancer in American women and the third leading cause of death. The mainstay of breast cancer treatment centers on resection of neoplastic tissue: 36% of women with early-stage breast cancer and 60% of women with late-stage breast cancer undergo mastectomy. Depending on age, socioeconomic status, location, and other patient factors, 20% to 40% of these women will choose to undergo breast reconstruction with a plastic surgeon. Previous published works have evaluated transversus abdominis plane (TAP) blocks and found an improvement in post-operative narcotic usage in a variety of abdominal surgeries including abdominoplasty, laparotomy, and cesarean section. In autologous breast reconstruction, the results have not been as robust. A study by Hivelin et al. evaluated bilateral ultrasound-guided TAP blocks in patients undergoing unilateral deep inferior epigastric perforator (DIEP) flap reconstruction and found patients reported lower pain scores post-operatively and demonstrated lower narcotic usage in the first 24 hours after surgery, but not in subsequent time periods out to 48 hours after surgery.
STUDY DESIGN: We have conducted a clinical trial to evaluate prospectively the effect of TAP blocks in breast reconstruction patients. The design of this interventional clinical trial is classified as efficacy with double-blind masking and two-group parallel assignment via randomized allocation to either bupivacaine TAP block or sham saline for the purpose of treatment. Female patients scheduled to undergo breast reconstruction with abdominal free flap were enrolled. This includes patients undergoing deep inferior epigastric perforator (DIEP) flap or free muscle-sparing transverse rectus abdominis muscle (MS-TRAM) flap reconstruction. An a priori power analysis based on data from previous publications indicated that a target total patient population of 128 patients was required to detect a difference of 10 mg in narcotic usage with 80% power. Due to COVID-19, the trial concluded prematurely with 109 patients enrolled and thus fell short of its recruitment goal.
DATASET: Beginning on the day following each patient’s breast reconstruction, the postoperative narcotic consumption in oral morphine equivalents was measured and recorded every hour for the next two to three postoperative days. The length of narcotic consumption recording for each patient was determined by when they were discharged from the hospital. Therefore, the dataset consists of the patient’s demographic, reconstructive, and postoperative narcotic use information.
STATISTICAL MODELS: All analyses were conducted in R. A threshold of 0.05 was used to assess statistical significance. Mixed Analysis of Variance (MANOVA) was used to assess differences between the treatment and sham groups. Postoperative narcotic use was the continuous dependent variable measured in oral morphine equivalents. The between-subjects independent variable was treatment with bupivacaine TAP block or sham saline. The within-subjects independent variable was time. There were no outliers in either the between or within-subjects independent variables. Levene’s test was used to assess homogeneity of variances. Mauchly’s test was used to assess sphericity. The dependent variable was non-normally distributed on studentized residuals and Shapiro-Wilk test. Therefore, a Yeo-Johnson power transformation was applied to the dependent variable to satisfy the statistical assumptions of MANOVA to determine the primary outcome. Subsequent analysis removed the Yeo-Johnson transformation and considered untransformed postoperative narcotic usage. MANOVA with the Yeo-Johnson transformed outcome met all assumptions for statistical validity, while MANOVA with the untransformed outcome violated statistical assumptions. When a statistically significant difference in postoperative narcotic usage between treatment and sham groups is mentioned, it is in reference to the difference in Yeo-Johnson transformed narcotic usage, which is a statistically valid conclusion. By contrast, subsequent, untransformed analysis is presented to describe practically meaningful results that are friendly to the clinician. Subsequent subanalysis to investigate two-way interactions between variables was conducted with ANOVA. Differences in means were assessed by paired and unpaired two-sided t-tests. In subanalysis, Bonferroni correction was applied based on the context in which multiple testing occurred.
 
STATISTICAL ISSUES
(1) Are the assumptions of MANOVA adequately satisfied for the analysis presented?
(2) Is the Yeo-Johnson transformed outcome valid? Currently, we report untransformed results for the sake of interpretability. Is this appropriate?
(3) How should we handle the fact that different patients had different lengths of postoperative hospital stays, and thus different lengths of postoperative narcotic use monitoring?
(4) What issues or study limitations arise from the premature termination of this clinical trial?
 
ZOOM MEETING INFORMATION
Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/97196061848?pwd=ajY3MmJOUU9oYitMdFZXL3NQYmFEZz09
    Password: 571460
 
Or iPhone one-tap (US Toll): +18333021536,,97196061848# or +16507249799,,97196061848#
 
Or Telephone:
    Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
          
    Meeting ID: 971 9606 1848
    Password: 571460
    International numbers available: https://stanford.zoom.us/u/aeA1opIz3O
 
    Meeting ID: 971 9606 1848
    Password: 571460
    SIP: 97196061848@zoomcrc.com
    Password: 571460
 

TITLE: Thyroid eye disease and COVID-19 vaccination

DATE: Wednesday, 25 January 2023

TIME: 1:30–3:00 PMINVESTIGATORS:

Patcharaporn Chandraparnik (1,2)

Andrea Kossler (1)

  1. Ophthalmology Department, Byers Eye Institute
  2. Ophthalmology Department, Phramongkutklao Hospital/College of Medicine, Bangkok, Thailand

LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA

WEBPAGE: http://med.stanford.edu/dbds/resources/data-studio.html

ABSTRACT

The Data Studio Workshop brings together a biomedical investigator with a group of experts for an in-depth session to solicit advice about statistical and study design issues that arise while planning or conducting a research project. This week, the investigator(s) will discuss the following project with the group.

INTRODUCTION

COVID-19 vaccine has been proved for efficacy and safety. Recently, there is emerging evidence of thyroid dysfunction after COVID-19 vaccination. This retrospective cohort study will be the first to examine the association between reactivation of TED and COVID-19 vaccination. Apart from thyroid dysfunction, there are case series reported on reactivation or worsening of thyroid eye disease (TED). There are five case series with a total of 14 patients that report on reactivation or new onset of TED after COVID-19 vaccination. Due to limited study, no conclusion about the association between vaccine and reactivation of disease was established.

The incidence for development of TED in Graves’ disease (GD) patients is approximately 15–30%. The pooled prevalence of TED among GD patients was 46% (CI: 37%–55%) in women and 49% (CI: 39%–60%) in men. The prevalence of active TED among TED patients was 32% (CI: 18%–48%) in women and 42% (CI: 27%–59%) in men. In a single center study of first-visit patients (N=415) with one physician during the period 2006 to 2012, 15.7% (m=65) of inactive TED patients had recurrent active TED with a mean interval of 10.3 years (range 2–56 years) between first and second event. Most patients experienced reactivation within 5 years after their first event (50.8%) and all but 7 patients experienced recurrence within 20 years after their first active phase.

HYPOTHESIS & AIM

Our hypothesis is that COVID-19 vaccination will increase incidence of active TED in inactive TED and Graves’ disease patients. Our aim is to assess the incidence of reactivation of thyroid eye disease (TED) in inactive TED or new onset of TED in Graves’ disease patients who receive COVID-19 vaccine compare to patients who do not receive COVID-19 vaccine.

DATASET

We plan to collect data of pre-existing TED patient from Oculoplastic clinic, Byers Eye Institute.

a. Inactive TED patients without history of vaccination (before January 2018) follow for 1 years whether they develop active TED

b. Inactive TED patients who received vaccine (after January 2021) and follow for 1 year whether they develop active TED

We also plan to collect data of Graves’ disease patients from Endocrine Clinic, Stanford Hospital.

a. Graves’ disease patients without history of vaccination (before January 2018) follow for 1 year whether they develop active TED

b. Graves’ disease patients who received vaccine (after January 2021) follow for 1 year whether they develop active TED

(The first American case of COVID infection -January 2020

COVID-19 vaccines became available in December 2020

Hx vaccine will check from Epic or call the patient)

The primary outcome will be Relative risk between [the incidence of Graves’ disease patient who received vaccine develop active TED] compare to [the incidence of Graves’ disease patient develop active TED before vaccine era] 

STATISTICAL MODEL

Relative risk between [the incidence of Graves’ disease and inactive TED patients who received vaccine develop active TED] compare to [the incidence of Graves’ disease patient and inactive TED develop active TED]. Accepted clinical significance at 20% increase incidence of active TED in vaccinated patient compare to non-vaccinate patient.

STATISTICAL ISSUES

  1. Due to low incidence of active thyroid eye disease each year, how should I design my project? Cohort? Case control? or something else?
  2. Is 1-year follow-up enough to show the significance of this study?
  3. Sample size calculation 
  4. Should I set the data collection as below?
    1. Inactive TED patients without history of vaccination (before January 2018) follow for 1 years whether they develop active TED
    2. I plan to collect the non-vaccinate patient before outbreak of COVID infection which is January 2020 to decrease the confounder of COVID infection-induced reactivation of TED.
  5. How could we manage the confounder, effect modifier? The previous studies of risk factors of active TED still do not have the results in the same way and there are a lot of confounders.

ZOOM MEETING INFORMATION

Join from PC, Mac, Linux, iOS or Android:

https://stanford.zoom.us/j/97196061848pwd=ajY3MmJOUU9oYitMdFZXL3NQYmFEZz09

Password: 571460

Or iPhone one-tap (US Toll): +18333021536,,97196061848# or +16507249799,,97196061848#

Or Telephone:

Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)    

    Meeting ID: 971 9606 1848

    Password: 571460

    International numbers available: https://stanford.zoom.us/u/aeA1opIz3O

    Meeting ID: 971 9606 1848

    Password: 571460

    SIP: 97196061848@zoomcrc.com

    Password: 571460

 

Data Studio Office Hour: Wednesday, 18 January 2023

TIME: 1:30–3:00 PM

LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA

REGISTRATION FORM: https://redcap.stanford.edu/surveys/?s=WMH74XCX33

DESCRIPTION:

The Data Studio Office Hour brings together a series of biomedical investigators with a group of experts for brief individualized sessions to solicit advice about a statistical and study design issue that arises while planning or conducting a research project.

This week, Data Studio holds office hours for your data science needs. Biomedical Data Science faculty are available to provide assistance with your research questions. If you need help with bioinformatics software and pipelines, check out the Computational Services and Bioinformatics Facility (http://cmgm-new.stanford.edu/) and the Genetics Bioinformatics Service Center (http://med.stanford.edu/gbsc.html).

Reserve a Data Studio Office Hour session by completing the Registration Form. Sessions are about 30 minutes long but might be extended at the discretion of the coordinator. If you register for a session, please be present at the start time on Wednesday.

If you are not able to register for a session, you are welcome to complete our Data Studio Consultation services form for a free one-hour meeting with one of our statisticians. You will find a link to the Consultation services form on our Data Studio web page (http://med.stanford.edu/dbds/resources/data-studio.html).

ZOOM MEETING INFORMATION:

Topic: BIODS 232: Data Studio

Time: This is a recurring meeting Meet anytime

Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/97196061848?pwd=ajY3MmJOUU9oYitMdFZXL3NQYmFEZz09

Password: 571460

Or iPhone one-tap (US Toll): +18333021536,,97196061848# or +16507249799,,97196061848#

Or Telephone:

 Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)   

    Meeting ID: 971 9606 1848

    Password: 571460

    International numbers available: https://stanford.zoom.us/u/aeA1opIz3O

    Meeting ID: 971 9606 1848

    Password: 571460

    SIP: 97196061848@zoomcrc.com

    Password: 571460

 

Data Studio Office Hour Wednesday, 11 January 2023

TIME: 1:30–3:00 PM

LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA

REGISTRATION FORM: https://redcap.stanford.edu/surveys/?s=WMH74XCX33

DESCRIPTION

The Data Studio Office Hour brings together a series of biomedical investigators with a group of experts for brief individualized sessions to solicit advice about a statistical and study design issue that arises while planning or conducting a research project.

This week, Data Studio holds office hours for your data science needs. Biomedical Data Science faculty are available to provide assistance with your research questions. If you need help with bioinformatics software and pipelines, check out the Computational Services and Bioinformatics Facility (http://cmgm-new.stanford.edu/) and the Genetics Bioinformatics Service Center (http://med.stanford.edu/gbsc.html).

Reserve a Data Studio Office Hour session by completing the Registration Form. Sessions are about 15 to 30 minutes long but might be extended at the discretion of the coordinator. If you register for a session, please be present at the start time on Wednesday.

If you are not able to register for a session, you are welcome to complete our Data Studio Consultation services form for a free one-hour meeting with one of our statisticians. You will find a link to the Consultation services form on our Data Studio web page (http://med.stanford.edu/dbds/resources/data-studio.html).

ZOOM MEETING INFORMATION:

Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/97196061848?pwd=ajY3MmJOUU9oYitMdFZXL3NQYmFEZz09

    Password: 571460

Or iPhone one-tap (US Toll): +18333021536,,97196061848# or +16507249799,,97196061848#

Or Telephone:

    Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)

    Meeting ID: 971 9606 1848

    Password: 571460

    International numbers available: https://stanford.zoom.us/u/aeA1opIz3O

    Meeting ID: 971 9606 1848

    Password: 571460

    SIP: 97196061848@zoomcrc.com

    Password: 571460Course Title: BIODS 232, Data Studio

DBDS on Diversity

We are committed to our historical and ongoing mission to use biomedical data science to improve human health. A cornerstone of this mission is diversity, reflected in embracing a breadth of complementary research interests, research styles, and a diverse and inclusive community. DBDS recognizes that we have significant work to do in shaping our future as we work towards achieving justice, equity, diversity and inclusion throughout our work and operations, our research and activities, and our professional relationships and partnerships.


Stanford's Land Acknowledgment Statement

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This acknowledgment has been developed in collaboration with the Muwekma Ohlone Tribe.