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.
Spring 2023
Data Sudio: Wednesday, 7 June 2023
TITLE: Eyelid Elasticity—Molecular Basis and Exploration of Stiffening Techniques
INVESTIGATORS:
Andrea K. M. Ross (1)
Albert Y. Wu (1)
- Department of Ophthalmology
DATE: Wednesday, 7 June 2023
TIME: 1:30–3:00 PM
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
Connective tissue disorders of the eyelid such as ectropion, entropion, and Floppy Eyelid Syndrome have a high prevalence in the general population. Patients suffer from chronic eye irritation and inflammation, and corneal scarring is associated with vision loss and the risk of blindness. Eyelid laxity is based on age-related collagen and elastin degradation in the tarsal plate. These conditions are commonly treated with surgery as less invasive therapy options are limited. We aim to characterize the biomechanical properties of the eyelid and its associated connective tissue. Further, we intend to explore alternative and less invasive techniques to stiffen the affected tissues. Riboflavin-based UVA crosslinking therapy is the clinically established standard treatment for halting disease progression of keratoconus and other corneal ectasias. The conventional Dresden protocol was introduced by Wollensak et al. and consists of UVA-light irradiation with a wavelength of 370 nm and a power of 3 mW/cm2 for 30 minutes to the de-epithelialized cornea. First laboratory attempts have shown a promising stiffening effect in other collagenous tissues such as the tarsal plate.
HYPOTHESIS & AIM
We intend to investigate the biomechanical effect of various crosslinking protocols on the tarsal plate ex vivo and subsequently transition the ex vivo crosslinking technique to an in vivo approach. This represents the decisive step towards establishing a new alternative and non-invasive treatment method for eyelid laxity syndromes. Regarding this study question, we additionally aim to analyze different pathways of riboflavin penetration into the tarsal plate. Further, we will study the biomechanical effect of banking/storage of tarsal plate in different media.
PLANNED EXPERIMENTS
The following individual projects will be conducted:
- Tarsal Plate Banking
- Riboflavin Penetration of Tarsal Plate
- Tarsal Plate Crosslinking - ex vivo
- Part I (Aim: Determine best protocol based on previous study results in literature)
- Part II (Aim: Narrow down intensity intervals to determine best protocol from Part I)
- Part III (Aim: Does the Bunsen law of reciprocity apply for tarsal plate crosslinking?)
- Tarsal Plate Crosslinking - in vivo
STATISTICAL MODELS
We aim to determine the most effective procedure (banking method, riboflavin penetration, CXL procedure) for all individual experiments. Therefore, we are searching for suitable statistical tests to compare the results between the different study groups. Further, we would like to discuss adequate group sizes in order to calculate the tissues needed in advance.
STATISTICAL QUESTIONS
(1) What are adequate group sizes for each individual project?
(2) How should we perform the statistical evaluation regarding statistical tests (and models)?
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Data Studio Wednesday, 31 May 2023
TITLE: Signal Processing and Analysis of Noisy Eye Position Sensor Data
INVESTIGATORS:
Jennifer Raymond (1)
Brian Angeles (1)
Sriram Jayabal (1)
Department of Neurobiology
TIME: 1:30–3:00 PM
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
Our lab measures eye velocity responses to visual and vestibular stimuli, their modification via training and learning, and the neural underpinnings thereof. There were great ideas (at the previous workshop) regarding methods to characterize the timing of our eye velocity cycle averages. Unfortunately, time constraints prevented us from presenting our questions and challenges regarding the pre-processing of the eye position data to handle noise and artifacts in the eye position recordings and differentiate to obtain eye velocity.
BACKGROUND
Our lab is interested in understanding the algorithms that the brain uses to learn. To do so, we use oculomotor learning (learned changes in the eye movement responses to visual and vestibular sensory stimuli) as an experimental behavioral model owing to its simplicity, experimental and analytical tractability. We collect eye position data from mice using a magnetic sensing method developed in the lab, as they track a moving visual stimulus or counter-rotate their eyes during head rotation (a vestibular stimulus). We can train the mice to alter the amplitude or timing of the eye movement responses. We would like to optimize the methods we use to pre-process the raw eye position data and the amplitude and timing of the eye movement responses.
METHODOLOGY
Horizontal eye position time-series data is acquired from magnetic sensors at a sampling rate of 1000 Hz, which then undergoes multiple processing steps:
- From the raw position data, 1 ms (single sample) transient noise artifact spikes are removed by applying Laplace smoothing (i.e. linearly interpolating the center point of the spike with the average of its nearest neighbors).
- A 9th order lowpass (zero-phase) Butterworth filter is applied with a cutoff frequency between 15 and 30 Hz on a mouse-by-mouse basis.
- The corresponding eye velocity trace (first derivative) of each block is approximated using a Savitzky-Golay filter over a 30-ms (i.e., 30 sample point) window.
- Saccades (brief, discrete high velocity/acceleration eye movement events, which we exclude from the analysis) and other unwanted artifacts in the eye position recordings (caused by electrical noise or body movements/vibrations) are removed by using velocity thresholding; which involves computing the squared differences between the velocity trace and its corresponding 1 Hz sinusoidal fit, and removing the sample points where its corresponding squared difference exceed some set threshold value.
- Velocity cycle averages are then computed over a single sinusoidal stimulus cycle.
- We typically average the eye velocity responses across stimulus repetitions, and then calculate the differences between velocity averages post- vs pre- behavioral training to calculate the learned change in the eye movement behavior in each session/mouse, and then conduct statistical tests comparing different populations of mice, and/or different kinds of training.
STATISTICAL ISSUES
We would like advice regarding several aspects of the pre-processing steps used to compute eye velocity from noisy positional data.
- Recommendations regarding the elimination of the 1ms high frequency transient noise found in our raw position data which we currently remove via interpolation.
- Importance of the order of pre-processing steps (e.g., application of a lowpass filter on the raw position signal before or after its differentiation).
- Methods for filtering and differentiation of raw eye position-related signals to remove the noise without affecting the eye movement signal.
- Best approaches for the detection and removal of eye saccades and unwanted motion artifacts from the eye velocity data.
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Data Studio, Wednesday, May 17, 2023
TITLE: Signal Processing and Analysis of Noisy Eye Position Sensor Data
INVESTIGATORS:
Jennifer Raymond (1)
Brian Angeles (1)
Sriram Jayabal (1)
- Department of Neurobiology
DATE: Wednesday, 17 May 2023
TIME: 1:30–3:00 PM
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
Our lab measures eye velocity responses to visual and vestibular stimuli and their modification by learning, and the neural underpinnings thereof.
BACKGROUND
A key function of the brain is to learn about the statistical relationships between events in the world. A mechanism of this learning is associative neural plasticity, controlled by the timing between neural events. Here, we show that experience can dramatically alter the timing rules governing associative plasticity to match the constraints of a particular circuit and behavior, thereby improving learning. In normal mice, the timing requirements for associative plasticity in the oculomotor cerebellum are precisely matched to the 120 ms delay for visual feedback about behavioral errors. This task-specific specialization of the timing rules for plasticity is acquired through experience; in dark-reared mice that had never experienced visual feedback about oculomotor errors, plasticity defaulted to a coincidence-based rule. Computational modeling suggests two broad strategies for implementing this Adaptive Tuning of the Timing Rules for Associative Plasticity (ATTRAP), which tune plasticity to different features of the statistics of neural activity. The modeling predicts a critical role of this process in optimizing the accuracy of temporal credit assignment during learning; consistent with this, behavioral experiments revealed a delay in the timing of learned eye movements in mice lacking experience-dependent tuning of the timing rules for plasticity. ATTRAP provides a powerful mechanism for matching the timing contingencies for associative plasticity to the functional requirements of a particular circuit and learning task, thereby providing a candidate neural mechanism for meta-learning.
METHODOLOGY
We have previously collected eye position data at a sampling rate of 1 kHz. The data corresponds to the eye movement of an animal either being sinusoidally rotated 180 degrees clockwise and counterclockwise at a rate of 1 Hz. Particular training protocols are conducted to either increase or decrease the magnitude of the eye's sinusoidal motion. We then differentiate our eye position signals to extract the corresponding velocity traces, and using the stimulus signal data as a reference, we would like to compute the average eye velocity trace over a single 1 Hz period of the stimulus oscillation.
STATISTICAL ISSUES
- Is there a better and more principled way to characterize the timing of the eye movement response to sinusoidal stimuli? In much of our previous work, we have fit the eye velocity responses with a sinusoid and reported the amplitude and phase of the fit. But now we are seeing interesting timing effects that are not captured by the sinusoidal fits. In bottom of Fig 2H of the bioRxiv preprint, we just plotted the time (ms) of the absolute peak of the learned eye movement trace for each mouse (calculated by average eye velocity response across ~40 stimulus repetitions post-training minus avg of pre-training eye velocity response).
- Unfortunately, our processed and filtered data is still quite noisy, and differentiating the noisy data only makes it worse. We typically apply a lowpass Butterworth filter on our positional data, use a windowed Savitzky-Golay filter to get the corresponding velocity trace, and then apply a custom saccade detection/removal algorithm. We now would like to explore other possible methods to get a cleaner velocity trace from our noisy positional data that has minimal effect on the temporal and amplitude information within the data.
- As time allows, we would also appreciate advice about several aspects of the pre-processing steps used to compute eye velocity.
- methods for digital differentiation and filtering of raw eye position-related signals to obtain eye velocity
- identification of eye saccades (brief, discrete high velocity/acceleration eye movement events, which we exclude from the analysis) vs. lower frequency continuous "smooth" eye movements and noise
- elimination of very high frequency noise, which appears in the raw data as a single, occasional wayward 1ms sample in an otherwise smoother raw trace of eye position as a function of time.
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Data Studio, Wednesday, May 10, 2023
TITLE: Career Trajectory of Academic General Surgery Residency Program Graduates: Do Academic Programs Graduate Academic Surgeons?
INVESTIGATORS:
Allen Green (1)
Jeff Choi (1)
(1) Department of Surgery
DATE: Wednesday, 10 May 2023
TIME: 1:30–3:00 PM
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:
Many general surgery residency programs emphasize their ability to produce academic surgeons. However, the proportion of academic general surgery residency graduates who become academic surgeons remains unclear.
HYPOTHESIS & AIM:
We aimed to quantify the contemporary prevalence of US academic general surgery residency graduates who become academic surgeons, and elucidate factors associated with pursuing a career in academic surgery.
DATASET:
We identified 2015 and 2018 graduates from 97 Accreditation Council for Graduate Medical Education-accredited general surgery residency programs affiliated with US allopathic medical schools. We extracted program and individual-level data using publicly available Doximity, PubMed, residency program, and faculty profiles. We defined academic surgeons as faculty within university-affiliated surgery departments who published two or more papers as the first or senior author in 2020 and 2021. Using a stepwise likelihood ratio test method to identify covariates, a multivariable logistic regression evaluated associations between program and individual-level factors and a career in academic surgery. The threshold for statistical significance was P <0.05.
STATISTICAL MODELS
We hope to build a logistic regression model that provides us inferences on what residency program-level and resident-level factors are associated with pursuing a career in academic surgery. We plan to use this model to first compare between non-academic and academic surgeons. Secondly, we plan to repeat this analysis comparing a highly productive subset of academic surgeons and the rest of the academic surgeon cohort to identify factors associated with being a highly productive academic surgeon.
STATISTICAL QUESTIONS
(1) Given the complex relationships between our variables, what is the best way to select variables to minimize confounders?
(2) Are there any additional analysis methods outside of logistic regression that we should consider?
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Data Studio: Wednesday, 3 May 2023
TITLE: The HIDDEN ASCVD Study
INVESTIGATORS:
Alex Sandhu (1)
Andrew Ambrosy (2)
Fatima Rodriguez (1)
(1) Cardiovascular Medicine, Stanford
(2) Cardiology, Kaiser Medical Center San Francisco
DATE: Wednesday, 3 May 2023
TIME: 1:30–3:00 PM
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.
BACKGROUND
Coronary artery calcium (CAC) is the strongest predictor of myocardial infarction (MI). The knowledge of having CAC is a powerful motivator of preventive behavior; this includes the initiation of statin therapy, which has been shown to reduce the risk of atherosclerotic cardiovascular disease (ASCVD) events by 25%. However, <10% of potentially eligible patients receive gated computed tomography (CT) scans to measure CAC. Furthermore, historically marginalized populations undergo even fewer gated CAC scans. CAC can be identified on the 19 million non-gated chest CTs performed annually. In the NOTIFY-1 pilot trial of 176 patients, notification of patients and primary care clinicians of the presence of incidental CAC increased statin prescription rates to 51% compared with 7% in the standard of care arm.
INTRODUCTION
We are submitting an R01 to the NHLBI on a pragmatic adaptive clinical trial in which we are testing different strategies for notifying patients regarding an incidental finding of coronary calcium. We are hoping to adapt the notification strategy to identify the notification approach that most effectively increases medication rates (statin prescription 3 months after notification) and minimizes patient anxiety. Increasing statin initiation among patients with incidental CAC could prevent approximately 500,000 ASCVD events over a decade. However, there are significant knowledge gaps that must be addressed: insights into the incidental CAC notification design that both effectively increases statin rates and is preferable to patients. We propose answering this question using an adaptive RCT conducted within Kaiser Permanente Northern California (KPNC), an integrated health care delivery system providing care to >4.5 million members with broad age, sex, racial, and ethnic diversity. The HIDDEN ASCVD study has the potential to be paradigm shifting by identifying the optimal behavior theory-driven notification strategy for statin therapy. Following this project, effective scaling of opportunistic CAC screening and notification will lead to reduction in ASCVD burden overall, particularly among historically marginalized populations.
HYPOTHESIS-AIM-OBJECTIVE
Our hypothesis is that certain notification strategies (e.g., based on the messenger, the positive/negative framing, etc.) are more effective than others at increasing statin rates and lead to less patient anxiety. Our aim is to compare the effect of multiple behavioral theory-driven incidental CAC notification strategies on rates of statin therapy initiation via an adaptive RCT. We will prospectively identify individuals with incidental CAC without known ASCVD not receiving statin therapy within KPNC. Individuals will be assigned randomly to notification strategies that are modifications of the initial strategy used in the NOTIFY-1 pilot study. Modifications will be based on the MINDSPACE behavioral change framework. Through an adaptive design, the objective will be to design the most effective notification that promotes statin initiation and is acceptable to patients and primary care clinicians.
STUDY POPULATION
The study cohort will be enriched for historically marginalized racial and ethnic groups.
OUTCOMES
The primary outcome will be statin initiation with the key secondary outcome being anxiety related to the notification based on PROMIS Anxiety Short Form.
STRATEGY-TEAM
The HIDDEN-ASCVD study will apply an adaptive RCT grounded in behavioral change theory to evaluate notification strategies and identify the ideal balance between motivating behavioral change and minimizing unnecessary patient anxiety. Our multidisciplinary team brings together early stage investigators (ESIs) and senior scientific leaders from the KPNC Division of Research and Stanford University with expertise in ASCVD prevention, health equity, predictive analytics, delivery science, and RCTs. The clinical coordination will be centered at Kaiser with the analysis planned at Stanford.
SAMPLE SIZE
We are imagining six (6) phases of 6-months each with approximately 800 patients for a total sample of 4800 individuals.
STATISTICAL QUESTIONS
We have a preliminary design and sample size. We need help thinking through the design and need a co-investigator for the award.
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Data Studio Office Hour, April 26, 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:
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Data Studio Office Hour Wednesday, 19 April 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:
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Password: 130209
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Wednesday, 12 April 2023
TIME: 1:30–3:00 PM
LOCATION: Conference Room X-399, Medical School Office Building, 1265 Welch Road, Stanford, CA
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Note: No, this is not Data Studio Déjà Vu. Dr. Mazhindu needs advice about statistical planning for this complex implementation study. We will convene in the usual place: Conference Room X-399 of the Medical School Office Building. Zoom videoconferencing will be available for those unable to join us in person.
TITLE: Clinical Implementation Study of Feasibility and Effectiveness of Pharmacogenomically-Guided Treatment in Gastrointestinal Cancer Patients
INVESTIGATORS:
Tinashe A. Mazhindu (1, 2, 3, 4)
Collen Masimirembwa (2, 4)
Ntokozo Ndlovu (3)
Margaret Borok (3)
(1) Chemical & Systems Biology Department, Stanford University School of Medicine
(2) African Institute of Biomedical Sciences & Technology (AiBST)
(3) Department of Oncology, University of Zimbabwe
(4) Consortium for Genomics & Therapeutics in Africa (CTGA) - iPROTECTA PROJECT
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:
Pharmacogenomics (PGx) is the study of how genetic variations determine drug response and efficacy. The goal of PGx is to have molecular, genetic, and external phenotypic characteristics jointly guide prescribing the right drug, at the right dose, to the right patient, and have favourable outcomes with minimal toxicity. The clinical response rates to medicines for some cancers range from 25–80% which means a significant proportion of the cancer patient population may experience drug adverse drug reaction (ADR) with no clinical benefit whatsoever. These ADRs result in resource utilization in patient care through blood transfusion, use of colony-stimulating factors, hospitalizations, additional tests, and further treatment delays, a situation that is best kept to a minimum especially in resource-limited developing countries like Zimbabwe.
STUDY BASIS:
Gastrointestinal (GIT) cancer accounts for approximately 20% of all new cancer cases in Zimbabwe and hence represents a significant disease burden. PGx recommendations are ranked according to strength of the evidence; 5-fluorouracil (5-FU), irinotecan, and analgesic have strong rankings. These drugs form the backbone of most first- and second-line therapies used in GIT cancers. In a newly published Pan-European study by Swen et al (2023), implementation of PGx guidelines by the Dutch Pharmacogenetics Working Group (DPWG) reduced the occurrence of adverse drug effects by 30%. Additionally, the study showed that the effect size of PGx guidelines differed among countries and different drug-gene pairs. However, only 1% of the participants in this study were Africans. Resource limitations call in to question the ability of African cancer treatment sites to implement PGx biomarker-guided therapy. Furthermore, such guidelines have limitations in an African setting because the populations possess distinct PGx biomarkers not found in Europeans.
STUDY DESIGN:
This study is a single centre, PGx biomarker-guided implementation study to investigate feasibility and clinical effectiveness in gastrointestinal cancer patients. We will use reactive pharmacogenomic testing for DPYD, UGT1A1, CYP2D6, and CYP2C9 to guide therapy. Genotyping will be done using locally available next generation sequencing (NGS) capacity provided by the study sponsor. Patients will be enrolled into the study if they have an indication to receive chemotherapy inclusive of irinotecan and 5-FU (or its oral prodrug capecitabine) based on National Comprehensive Cancer Network (NCCN) guideline recommendations. Dosing will be based on the DPWG guidelines for their specific variants. Patients will be monitored through therapeutic drug measurements, NCI CTAE for toxicity, and disease response using RECIST criteria for up to 12 months with clinical reviews that include routine CT scans.
PROPOSED ENDPOINTS
- Primary
Dose deviation rate due to pharmacogenomics biomarker guidance and resultant drug/drug metabolite concentration at Tmax
Turnover time for pharmacogenomics results availability to clinicians to guide intervention decision making
- Secondary
Numbers and proportion of patients with ≥ grade 3 toxicity (NCI CTAE v5)
Turnover time therapeutic drug monitoring results availability to clinicians to guide intervention decision
Disease-free and overall survival of study participants at one year
Tumour objective response rate (for neoadjuvant therapy or metastatic stage patients) using the RECIST criteria.
Measure quality of life (QoL) scores among study participants using QoL questionnaires
Number of samples bio-banked out of the total planned per patient.
Cost-effectiveness of implementing PGx guided therapy
- Exploratory
PGx polymorphism impact of cancer supportive therapy outcomes- (analgesia and emesis)
Cancer care biomarker and genomic mutation assessment for GI cancer patients including mapping the mutation trends
STATISTICAL ISSUES
The primary assistance I need is on statistical planning for the study.
- What is the best study design for this implementation question and are these endpoints suited for such a study?
- Retrospective data on adverse drug effects (ADR) before PGx usage is obtainable. Would this be a valid control for ADR and cost-effectiveness evaluation?
- How do you calculate a sample size for such an implementation study?
- How do you evaluate safety and dose deviation in such a study?
ZOOM MEETING INFORMATION
Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/91706399349?pwd=UXFlclNkakpmZC9WVWwrK244T2FwUT09
Password: 130209
Or iPhone one-tap (US Toll):
+18333021536,,91706399349# or
+16507249799,,91706399349#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or
+1 833 302 1536 (US, Canada, Caribbean Toll Free)
Meeting ID: 917 0639 9349
Password: 130209
International numbers available: https://stanford.zoom.us/u/abKRNREFBK
Meeting ID: 917 0639 9349
Password: 130209
Password: 130209
Wednesday, 5 April 2023
For this week only, we will meet in a basement classroom of the Center for Clinical Sciences Research: CCSR 0235 (Google Map). Please refer to the attached layout of Floor 0 and room photograph. The room should be accessible either via the elevators or stairway. If possible, please attend in person. I will try to enable the Zoom videoconference for those unable to attend in person.
TITLE: Clinical Implementation Study of Feasibility and Effectiveness of Pharmacogenomically-Guided Treatment in Gastrointestinal Cancer Patients
INVESTIGATORS:
Tinashe A. Mazhindu (1, 2, 3, 4)
Collen Masimirembwa (2, 4)
Ntokozo Ndlovu (3)
Margaret Borok (3)
(1) Chemical & Systems Biology Department, Stanford University School of Medicine
(2) African Institute of Biomedical Sciences & Technology (AiBST)
(3) Department of Oncology, University of Zimbabwe
(4) Consortium for Genomics & Therapeutics in Africa (CTGA) - iPROTECTA PROJECT
DATE: Wednesday, 5 April 2023
TIME: 1:30–3:00 PM
LOCATION: Room 0235, Center for Clinical Sciences Research (CCSR), 269 Campus Drive, Stanford, CA(http://maps.stanford.edu/ada/building-ada.cfm?FACIL_ID=07-590 )
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.
Pharmacogenomics (PGx) is the study of how genetic variations determine drug response and efficacy. The goal of PGx is to have molecular, genetic, and external phenotypic characteristics jointly guide prescribing the right drug, at the right dose, to the right patient, and have favourable outcomes with minimal toxicity. The clinical response rates to medicines for some cancers range from 25–80% which means a significant proportion of the cancer patient population may experience drug adverse drug reaction (ADR) with no clinical benefit whatsoever. These ADRs result in resource utilization in patient care through blood transfusion, use of colony-stimulating factors, hospitalizations, additional tests, and further treatment delays, a situation that is best kept to a minimum especially in resource-limited developing countries like Zimbabwe.
STUDY BASIS
Gastrointestinal (GIT) cancer accounts for approximately 20% of all new cancer cases in Zimbabwe and hence represents a significant disease burden. PGx recommendations are ranked according to strength of the evidence; 5-fluorouracil (5-FU), irinotecan, and analgesic have strong rankings. These drugs form the backbone of most first- and second-line therapies used in GIT cancers. In a newly published Pan-European study by Swen et al (2023), implementation of PGx guidelines by the Dutch Pharmacogenetics Working Group (DPWG) reduced the occurrence of adverse drug effects by 30%. Additionally, the study showed that the effect size of PGx guidelines differed among countries and different drug-gene pairs. However, only 1% of the participants in this study were Africans. Resource limitations call in to question the ability of African cancer treatment sites to implement PGx biomarker-guided therapy. Furthermore, such guidelines have limitations in an African setting because the populations possess distinct PGx biomarkers not found in Europeans.
STUDY DESIGN
This study is a single centre, PGx biomarker-guided implementation study to investigate feasibility and clinical effectiveness in gastrointestinal cancer patients. We will use reactive pharmacogenomic testing for DPYD, UGT1A1, CYP2D6, and CYP2C9 to guide therapy. Genotyping will be done using locally available next generation sequencing (NGS) capacity provided by the study sponsor. Patients will be enrolled into the study if they have an indication to receive chemotherapy inclusive of irinotecan and 5-FU (or its oral prodrug capecitabine) based on National Comprehensive Cancer Network (NCCN) guideline recommendations. Dosing will be based on the DPWG guidelines for their specific variants. Patients will be monitored through therapeutic drug measurements, NCI CTAE for toxicity, and disease response using RECIST criteria for up to 12 months with clinical reviews that include routine CT scans.
PROPOSED ENDPOINTS
Primary
- Dose deviation rate due to pharmacogenomics biomarker guidance and resultant drug/drug metabolite concentration at Tmax
- Turnover time for pharmacogenomics results availability to clinicians to guide intervention decision making
Secondary
- Numbers and proportion of patients with ≥ grade 3 toxicity (NCI CTAE v5)
- Turnover time therapeutic drug monitoring results availability to clinicians to guide intervention decision
- Disease-free and overall survival of study participants at one year
- Tumour objective response rate (for neoadjuvant therapy or metastatic stage patients) using the RECIST criteria.
- Measure quality of life (QoL) scores among study participants using QoL questionnaires
- Number of samples bio-banked out of the total planned per patient.
- Cost-effectiveness of implementing PGx guided therapy
Exploratory
- PGx polymorphism impact of cancer supportive therapy outcomes- (analgesia and emesis)
- Cancer care biomarker and genomic mutation assessment for GI cancer patients including mapping the mutation trends
STATISTICAL ISSUES
The primary assistance I need is on statistical planning for the study.
- What is the best study design for this implementation question and are these endpoints suited for such a study?
- Retrospective data on adverse drug effects (ADR) before PGx usage is obtainable. Would this be a valid control for ADR and cost-effectiveness evaluation?
- How do you calculate a sample size for such an implementation study?
- How do you evaluate safety and dose deviation in such a study?
ZOOM MEETING INFORMATION
Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/91706399349?pwd=UXFlclNkakpmZC9WVWwrK244T2FwUT09
Password: 130209
Or iPhone one-tap (US Toll):
+18333021536,,91706399349# or
+16507249799,,91706399349#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or
+1 833 302 1536 (US, Canada, Caribbean Toll Free)
Meeting ID: 917 0639 9349
Password: 130209
International numbers available: https://stanford.zoom.us/u/abKRNREFBK
Meeting ID: 917 0639 9349
Password: 130209
Password: 130209
Winter 2023
TITLE: Buprenorphine Use in the Perioperative Period
INVESTIGATOR: Sesh Mudumbai (1, 2)
(1) Department of Anesthesiology, Perioperative, and Pain Medicine
(2) VA Palo Alto HCS
DATE: Wednesday, 15 March 2023
TIME: 1:30–3:00 PM
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
The prevalence of opioid use disorders (OUD) in surgical patients has increased over the past two decades. The treatment guidelines for OUD recommend Opioid Agonist Therapy (OAT) such as buprenorphine to decrease illicit opioid use and overdose. However, the use of buprenorphine in acute care, particularly surgery, has conflicting management approaches (continue vs. interrupt) due to varying emphasis on postoperative pain vs. OUD-related risks. Comparative safety and effectiveness data for perioperative management strategies for buprenorphine are limited, and no randomized controlled trials exist. This study aims to evaluate the comparative safety and effectiveness of perioperative buprenorphine utilizing state-of-the-art epidemiological methods and identify optimal strategies for individual management. At the end of this study, we will have substantial evidence to guide clinical practice guidelines regarding the continuation or interruption of buprenorphine prior to surgical procedures among COU.
Hypothesis & Aim
The overall goal of this study is to generate unbiased or minimally biased, population-level estimates of the comparative safety and effectiveness of perioperative buprenorphine and identify optimal strategies for individual management.. The study aims are to evaluate (1) the comparative safety of current perioperative regimens (interruption vs. continuation) for buprenorphine on OUD-related outcomes, (2) the comparative effectiveness of current perioperative regimens (interruption vs. continuation) and formulations for buprenorphine on pain-related outcomes, on opioid-related adverse drug events (ORADES) and healthcare utilization; and (3) identify subgroups and factors to identify the optimal strategy of interruption vs. continuation for individual level decision making.
Dataset
Our strategy involves four phases. First, we will utilize a national-level Veterans Health Affairs (VHA) cohort of VA surgical patients who received buprenorphine preoperatively. Then, the cohort will be enriched with Centers for Medicare & Medicaid Services (CMS) and Social Determinants of Health (SDoH) data to better track risk factors and outcomes. Second, we will evaluate the relationship between regimens and post-discharge safety outcomes, including opioid-related emergency visits and hospitalizations. Third, we will look at how prescribing patterns affect pain-related outcomes. Finally, we’ll evaluate the impact of these regiments on ORADES and healthcare utilization, taking into account a variety of SDoH. Using OMOP’s common data model of a few domains and concept IDs, we identified an initial population of 5,146 patients satisfying the study criteria.
Statistical models
The study will use a cohort design and a variety of statistical methods. For our first two aims, we will plan to use logistic regression, to evaluate the effect of buprenorphine on clinical outcomes, pain-related outcomes, and ORADES ,and healthcare utilization. The analysis will adjust for potential confounding variables and use propensity scores and inverse probability of treatment weighting to balance covariates. We will use high-dimensional propensity scores to account for proxies of unmeasured confounders, and a doubly robust estimator to mitigate the problems of estimating models with many variables. The potential confounders will include socio-economic descriptors, clinical measures, and SDoH variables. A variety of sensitivity analyses will address residual confounding, exposure misclassification, outcome misclassification, and selection bias. For our third aim, we will plan to use machine learning methods including clustering approaches k-means clustering and others.
- What is the optimal approach to set up doubly robust estimators and the impact on the Average Treatment Effect of the Treated (ATT) vs Average Treatment Effect (ATE)
- What are the set of sensitivity analyses? The role of negative control methods?
- What sensitivity analyses are needed?
- How should we validate and present our preliminary data and algorithms?
- What types of machine learning approaches should we use and why ? prediction of receipt of
ZOOM MEETING INFORMATION
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Meeting ID: 971 9606 1848
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Meeting ID: 971 9606 1848
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Password: 571460TITLE: Data Studio Office Hour
DATE: Wednesday, 8 March 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:
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
Password: 57146
TITLE: Capillary Adaptation in Right Heart Failure
DATE: Wednesday, 1 March 2023
TIME: 1:30–3:00 PM
INVESTIGATORS:
Kenzo Ichimura (1, 2, 3)
Ross Metzger (3)
Edda Spiekerkoetter (1, 2, 3)
(1) Pulmonary, Allergy and Critical Care Medicine
(2) Cardiovascular Institute
(3) Vera Moulton Wall Center for Pulmonary Vascular Disease
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
Right ventricular (RV) function is the primary determinant of functional status and survival in various cardiovascular diseases characterized by an increased RV afterload. Despite the importance of the RV function in diseases such as pulmonary hypertension (PH), the mechanism of RV failure is not well understood which limits the development of RV-targeted therapies.
Capillary rarefaction due to vessel loss or insufficient growth of the capillaries is one of the proposed structural hallmarks of RV failure. Multiple studies using rodent models of PH have shown that capillary numbers and density decrease at the stage of decompensated RV failure. Furthermore, some human studies corroborated these findings and reported that the capillary density was decreased in PH patients with end-stage RV failure. However, most of the studies documenting capillary rarefaction in the RV were performed on thin sections using conventional two-dimensional (2D) imaging which limits the evaluation of the architectural changes of the three-dimensional (3D) microvascular network of the heart.
Developments in 3D deep tissue imaging have added significant depth to our knowledge of complex microstructures such as the central nervous system and blood vessels of the heart. Deep tissue imaging can be used not only to visualize 3D tubular networks such as the vasculature, but allows for quantification of network properties such as length, diameter, orientation angle, straightness, number of branching points, number of segments, and capillary volume.
By using a mouse model of pressure overload-induced RV failure (i.e., pulmonary artery banding, PAB), we developed a 3D deep-tissue imaging and analysis method to visualize and quantify the capillary network in the RV and its relation with the cardiomyocytes (capillary-cardiomyocyte contact area). We also applied this method to human heart tissue which was obtained either from patients undergoing heart transplantation or from control cases without RV failure.
HYPOTHESES
- In PAB model, the capillary architecture changes over the disease time course.
- In PAB model, the capillary-cardiomyocyte contact area is preserved throughout the disease time course, except for the areas of fibrosis.
- In PAB, males and females have different capillary properties at baseline and in response to RV pressure overload.
- In patients with end-stage RV failure, capillary architectural change is associated with etiology and disease duration.
- ) cases have a different capillary architecture compared to other PAH cases.
DATASET
This study has two parts, an analysis of a mouse model of RV failure (i.e., PAB), and an analysis of samples obtained from patients with end-stage RV failure due to PH.
For the PAB mouse model, parameters of the capillary structure (length, diameter, orientation angle, straightness, number of branching points, number of segments, capillary volume), fibrosis (fibrotic tissue volume), RV tissue volume, as well as cardiomyocytes (cardiomyocyte size, capillary-cardiomyocyte contact area) were obtained from several timepoints after PAB (week 1, week 4, week 7) from male and female mice, and compared to Sham animals. N=5–7 each.
For the patient samples, parameters of the capillary structure (length, diameter, orientation angle, straightness, number of branching points, number of segments, capillary volume), fibrosis (fibrotic tissue volume), RV tissue volume, as well as clinical information (etiology, hemodynamic parameters, disease duration) were obtained. N=7 PAH cases (all female), N=4 control cases (3 males and 1 female).
For the capillary parameters, each sample has 10,000–15,000 observations, as all capillary segments have their own measurements (i.e., length, diameter, orientation angle, and straightness). Likewise, all cardiomyocytes have their own measurements (cardiomyocyte size, capillary-cardiomyocyte contact area) of around 30 observations/sample. Other parameters are unique to each sample (i.e., only one observation for “numbers of branching points” as it is the number of branches/sample).
STATISTICAL ISSUES
(1) How to do a power calculation for multiple endpoints (length, diameter, etc.) that have different SD.
(2) What is the best statistical method to compare the parameters of the capillary structures and cardiomyocytes in PAB mice? Two-way ANOVA? Mixed-effect model?
(3) How can we associate the changes in the capillary structure with etiology, hemodynamic parameters, and disease duration in patient samples? Principal component analysis? tSNE?
ZOOM MEETING INFORMATION
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Meeting ID: 971 9606 1848
Password: 571460
International numbers available: https://stanford.zoom.us/u/aeA1opIz3O
Meeting ID: 971 9606 1848
Password: 571460
Password: 571460
TITLE: Data Studio Office Hour
DATE: Wednesday, 22 February 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:
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
Password: 571460
TITLE: Clinical Implementation Study of Feasibility and Effectiveness of Pharmacogenomically-Guided Treatment in Gastrointestinal Cancer Patients
DATE: Wednesday, 15 February 2023
TIME: 1:30–3:00 PM
LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA
INVESTIGATORS:
Tinashe A. Mazhindu (1, 2, 3, 4)
Collen Masimirembwa (2, 4)
Ntokozo Ndlovu (3)
Margaret Borok (3)
(1) Chemical & Systems Biology Department, Stanford University School of Medicine
(2) African Institute of Biomedical Sciences & Technology (AiBST)
(3) Department of Oncology, University of Zimbabwe
(4) Consortium for Genomics & Therapeutics in Africa (CTGA) - iPROTECTA PROJECT
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.
Pharmacogenomics (PGx) is the study of how genetic variations determine drug response and efficacy. The goal of PGx is to have molecular, genetic, and external phenotypic characteristics jointly guide prescribing the right drug, at the right dose, to the right patient, and have favourable outcomes with minimal toxicity. The clinical response rates to medicines for some cancers range from 25–80% which means a significant proportion of the cancer patient population may experience drug adverse drug reaction (ADR) with no clinical benefit whatsoever. These ADRs result in resource utilization in patient care through blood transfusion, use of colony-stimulating factors, hospitalizations, additional tests, and further treatment delays, a situation that is best kept to a minimum especially in resource-limited developing countries like Zimbabwe.
STUDY BASIS
Gastrointestinal (GIT) cancer accounts for approximately 20% of all new cancer cases in Zimbabwe and hence represents a significant disease burden. PGx recommendations are ranked according to strength of the evidence; 5-fluorouracil (5-FU), irinotecan, and analgesic have strong rankings. These drugs form the backbone of most first- and second-line therapies used in GIT cancers. In a newly published Pan-European study by Swen et al (2023), implementation of PGx guidelines by the Dutch Pharmacogenetics Working Group (DPWG) reduced the occurrence of adverse drug effects by 30%. Additionally, the study showed that the effect size of PGx guidelines differed among countries and different drug-gene pairs. However, only 1% of the participants in this study were Africans. Resource limitations call in to question the ability of African cancer treatment sites to implement PGx biomarker-guided therapy. Furthermore, such guidelines have limitations in an African setting because the populations possess distinct PGx biomarkers not found in Europeans.
STUDY DESIGN
This study is a single centre, PGx biomarker-guided implementation study to investigate feasibility and clinical effectiveness in gastrointestinal cancer patients. We will use reactive pharmacogenomic testing for DPYD, UGT1A1, CYP2D6, and CYP2C9 to guide therapy. Genotyping will be done using locally available next generation sequencing (NGS) capacity provided by the study sponsor. Patients will be enrolled into the study if they have an indication to receive chemotherapy inclusive of irinotecan and 5-FU (or its oral prodrug capecitabine) based on National Comprehensive Cancer Network (NCCN) guideline recommendations. Dosing will be based on the DPWG guidelines for their specific variants. Patients will be monitored through therapeutic drug measurements, NCI CTAE for toxicity, and disease response using RECIST criteria for up to 12 months with clinical reviews that include routine CT scans.
PROPOSED ENDPOINTS
Primary
- Dose deviation rate due to pharmacogenomics biomarker guidance and resultant drug/drug metabolite concentration at Tmax
- Turnover time for pharmacogenomics results availability to clinicians to guide intervention decision making
Secondary
- Numbers and proportion of patients with ≥ grade 3 toxicity (NCI CTAE v5)
- Turnover time therapeutic drug monitoring results availability to clinicians to guide intervention decision
- Disease-free and overall survival of study participants at one year
- Tumour objective response rate (for neoadjuvant therapy or metastatic stage patients) using the RECIST criteria.
- Measure quality of life (QoL) scores among study participants using QoL questionnaires
- Number of samples bio-banked out of the total planned per patient.
- Cost-effectiveness of implementing PGx guided therapy
Exploratory
- PGx polymorphism impact of cancer supportive therapy outcomes- (analgesia and emesis)
- Cancer care biomarker and genomic mutation assessment for GI cancer patients including mapping the mutation trends
STATISTICAL ISSUES
- What is the best study design for this implementation question and are these endpoints suited for such a study?
- Retrospective data on adverse drug effects (ADR) before PGx usage is obtainable. Would this be a valid control for ADR and cost-effectiveness evaluation?
- How do you calculate a sample size for such an implementation study?
- How do you evaluate safety and dose deviation in such a study?
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
Password: 571460
TITLE: The novel rabbit model of corneal reinnervation surgery in denervated eye
INVESTIGATORS:
Patcharaporn Chandraparnik (1,2)
Andrea Kossler (1)
Cigdem Yasar (1)
David Myung (1)
- Ophthalmology Department, Byers Eye Institute, Stanford University, California, USA
- Ophthalmology Department, Phramongkutklao Hospital/College of Medicine, Bangkok, Thailand
DATE: Wednesday, 8 February 2023
TIME: 1:30–3:00 PM
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
Neurotrophic keratopathy (NK) is a rare and underdiagnosed degenerative disease of the cornea that is caused by damage to the ophthalmic branch of the trigeminal nerve. Epithelial breakdown, corneal ulceration, corneal melting (thinning), perforation, and loss of vision may occur over time. Currently, corneal reinnervation surgery is the only method to regrow the sensory nerve in the cornea to treat NK.
STUDY DESIGN
We wish to create the rabbit model of corneal reinnervation. Corneal reinnervation rabbit models will be utilized in further studies to evaluate the efficacy of each vehicle that delivers growth factors to accelerate nerve regeneration after the surgery. We plan to conduct the study in 2 phases with all rabbits undergoing both phases.
Phase 1: Denervation surgery by using radio-frequency thermocoagulation to create lesion at trigeminal ganglion. Corneal wound will be created on the same side of trigeminal ganglion lesion.
Phase 2: Reinnervation surgery by using infraorbital nerve graft connect to limbus of cornea. Corneal wound will be created on the same side.
HYPOTHESIS & AIM
Our aim is to assess the rate of corneal abrasion wound closure in reinnervated eye in rabbit. We hypothesize that the rate of corneal abrasion wound closure will be increased in reinnervated eye compared to denervated eye.
DATASET
We will collect the following data from each rabbit: size of corneal wound, blink rate, tear meniscus, and survival of rabbit. The primary outcome will be the rate of corneal wound healing in each group. The secondary outcomes are corneal sensation, blink rate, blink reflex, tear meniscus, and survival of rabbit.
STATISTICAL ISSUES
- Sample size calculation for number of rabbits needed to show statistical significance in denervation group?
- How many treatment groups (i.e., number of vehicles that deliver growth factors to accelerate nerve regeneration after the surgery) can we accommodate?
- Should we have a single control group or one for each treatment group?
- Given the general constraint of minimizing the number of study animals, how should we deal with missing data (e.g., death of the rabbit, failure to collect data from a rabbit, etc.)?
- What is the appropriate design for this experimental study?
- Do we need to control for multiple comparisons?
- Should we use parametric or nonparametric methods for the hypothesis tests in this study?
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Meeting ID: 971 9606 1848
Password: 571460TITLE: 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?
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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)
- Ophthalmology Department, Byers Eye Institute
- 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
- Due to low incidence of active thyroid eye disease each year, how should I design my project? Cohort? Case control? or something else?
- Is 1-year follow-up enough to show the significance of this study?
- Sample size calculation
- Should I set the data collection as below?
- Inactive TED patients without history of vaccination (before January 2018) follow for 1 years whether they develop active TED
- 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.
- 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.
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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
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Meeting ID: 971 9606 1848
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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:
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Meeting ID: 971 9606 1848
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Password: 571460Course Title: BIODS 232, Data Studio