Causal Inference in Pregnancy: Real World Data when Randomized Trials are Impractical
December 15, 2023 | 1:00pm – 5:00pm | Snyder Auditorium, Kresge G1
Program:
Welcome – Sonia Hernadez-Diaz
Why is Causal Inference Harder During Pregnancy? – Sonia Hernandez-Diaz (HSPH, CAUSALab, H4P)
Before Treatments are Approved: Challenges of Randomized Trials in Pregnancy – Anne Drapkin Lyerly (Center for Bioethics at University of North Carolina, Chapel Hill)
After Treatments are Approved: Real World Evidence for Regulation of Treatments during Pregnancy – Leyla Sahin (Food & Drug Administration)
Case Series and Case-Control Studies – Isotretinoin Teratogenicity – Sonja A. Rasmussen (Johns Hopkins School of Medicine)
Pregnancy Registries — Risk-benefit of Psychotropics – Lee S. Cohen (Massachusetts General Hospital)
Healthcare Databases: The Opioids Epidemic Spills into Pregnancy – Krista F. Huybrechts (Harvard Medical School, Brigham and Women’s Hospital, H4P)
Expert Panel: Where do we go from here? Moderated by Dr. Brian Bateman.
Panelists: Lee S. Cohen (MGH), Anne Drapkin (University of North Carolina, Chapel Hill), Sonia Hernandez-Diaz (HSPH, CAUSALab, HSPH), Krista F. Huybrechts (Harvard Medical School, BWH, H4P), Sonja A. Rasmussen (Johns Hopkins School of Medicine), Leyla Sahin (FDA).
Real World Data & Randomized Trials Working Together: Causal Inference for Cardiovascular Devices
April 14, 2023 | 10:30am – 2:45pm | Snyder Auditorium, Kresge G1
Program:
Welcome – Albert Hofman
The Special Case of Cardiovascular Device Evaluation – William Maisel (FDA)
Cardiovascular Device Evaluations Using Real World Data – Chaired by Dhruv Kazi (BIDMC, Smith Center) and Miguel Hernan (CAUSALab)
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- Using Real-World Data in the Absence of Randomized Trials: The case of abdominal aortic endografts – Eric Secemsky (BIDMC, Smith Center)
- Using Real-World Data after Randomized Trials: The case of paclitaxel coated balloons – Laura Mauri (Medtronic)
- When Real World Data are Insufficient for Causal Inference: The case of insurance claims to study mechanical circulatory support devices – Robert Yeh (BIDMC, Smith Center)
- When Trials Don’t Tell the Whole Story: Transporting Effects from Transcatheter Aortic Valve Replacement Trials to Understudied Populations – Issa Dahabreh (CAUSALab)
Evaluating Cardiovascular Devices Using Real World Data: Where the Field Needs to Move – Bob Harrington (Stanford)
Expert Panel: Where do we go from here? – Chaired by Robert Yeh (BIDMC, Smith Center)
Panelists: Florence Bourgeois (Harvard University), Bob Harrington (Stanford), William Maisel (FDA), Laura Mauri (Medtronic), and Bram Zuckerman (FDA)
Suicide Prevention: How Do We Know What We Know, And How Can We Know More?
November 4, 2022 | 10:30am-3:00pm | Snyder Auditorium, Kresge G1
Program:
Welcome – Albert Hofman
Suicide as a preventable public health problem – Katherine Keyes
Interventions to prevent suicide? – Chaired by Barbra Dickerman
- Firearm interventions – Matthew Miller
- Psychosocial interventions – Kate Bentley
- Just-in-time interventions – Matthew Nock
- Medications – Gonzalo Martínez-Alés and Alejandro Szmulewicz
Suicide prevention: What’s next and why it hasn’t been done yet? – Philip Wang
Expert panel: Where do we go from here? Research priorities and challenges for suicide prevention – Chaired by Miguel Hernán
Panelists: Kate Bentley, Katherine Keyes, Mathew Miller, Matthew Nock, Philip Wang
The CAUSALab Inaugural Event: Causal Inference from Real World Data in the Era of Covid-19
November 5, 2021 | 10:30am-3:00pm | Snyder Auditorium, Kresge G1
Program:
Welcome – Albert Hofman
The CAUSALab: A Center to Learn What Works – Miguel Hernán
The CAUSALab’s Response to Covid-19: Data science for decision making, chaired by Sonia Hernandez-Diaz
- Vaccines for Covid-19 prevention: Comparative effectiveness and safety – Barbra Dickerman
- Drug repurposing for COVID-19 prevention: The effects of old drugs on a new disease – Katherine Li
- Nonpharmacological interventions for the treatment and prevention of Covid-19 – Arin Madenci / José Zubizarreta
Causal inference without randomized experiments: How do we know we are right? – James Robins
Panel Discussion – Issa Dahabreh, Miguel Hernán, Sara Lodi, and James Robins (moderated by Andrew Beam)
Spring 2020
Weighting for Causal Inference: Estimating the impact of a natural disaster on health outcomes?
February 7, 2020 | 3:45-5:15pm | TMEC 227 Amphitheater
Host: José R. Zubizarreta
Discussant: Jamie Robins
Moderator: Miguel Hernan
Fall 2019
Bugs and drugs: How (and how much) does changing antibiotic use, change antibiotic resistance?
November 1, 2019 | 3:45-5:15pm | 503, Ballard Room
Hosts: Marc Lipsitch and Yonatan Grad
Moderator: Mats J. Stensrud
Mobile Health: Which type of in-the-moment support is best provided to someone who is struggling with suicidal thoughts?
October 4, 2019 | 3:45-5:15pm | 503, Ballard Room
Co-Hosts: Susan Murphy and Matt Nock
Moderator: Miguel Hernán
Spring 2019
Machine Learning for Health: How can we better leverage machine learning to change the future of healthcare?
Friday May 3, 2019 | 3:45-5:15pm | Waterhouse Faculty Room, 106 Gordon Hall
Hosts: Andrew Beam, Sherri Rose, and David Sontag
Introduction by Michael Oberst
Moderator: Miguel Hernán
Click here to view the May 2019 flyer.
What Problems Can Electronic Health Records Solve? The strengths and pitfalls of EHR data
Friday April 5, 2019 | 3:45-5:15pm | Ballard Room, Countway Library
Hosts: James Floyd, Jessica Chubak, and Goodarz Danaei
Moderator: Miguel Hernán
Click here to view the April 2019 flyer.
Do the results from a trial apply to my target population? Transporting causal inferences from randomized trials to new populations
Friday March 8, 2019 | 3:45-5:15pm | 106 Gordon Hall
Presentations by: Elvira D’Andrea, Irina Degtiar, and Sarah Robertson
Hosts: Issa Dahabreh and Mehdi Najafzadeh
Moderator: Miguel Hernán
Co-sponsored by the Center for Evidence Synthesis in Health at Brown University and the Division of Pharmacoepidemiology & Pharmacoeconomics at Brigham and Women’s Hospital.
Using Real-World Data to Emulate Randomized Trials – Target Trial Design and Implementation
Friday February 1, 2019 | 3:45-5:15pm | 106 Gordon Hall
Heard about target trials and curious to learn more? Join us for a collaborative group exercise in designing target trials! Using examples from pharmacoepidemiology and clinical epidemiology, this workshop will provide a practical tutorial on conceiving and designing target trials, and avoiding common pitfalls encountered when using that target trial to guide study design and analyses in observational data.
Facilitators: Barbra Dickerman, Lucia Petito, & Kerollos Wanis
Hosts: Sara Lodi & Miguel Hernan
Co-sponsored by the Boston University School of Public Health Department of Biostatistics
Click here to view the February 2019 flyer.
Fall 2018
Workshop: Crowdsourcing Causal Graphs – A hackathon for causal DAGs
Friday December 7, 2018 | 3:45-5:15pm | 261 Tosteson Medical Education Center – 2nd Floor Atrium
Heard about causal graphs or DAGs and curious to know more? Join us for a collaborative, hands-on exercise in building causal graphs! Using an example from psychiatric epidemiology, this workshop will provide a practical tutorial on summarizing expert knowledge into a causal graph and using that graph to think about study design and analyses.
Facilitators: Eleanor Murray, Jaimie Gradus, & Matthew Fox
Presenters: Louisa Hills Smith & Laura Ann Sampson
Host: Sonia Hernandez-Diaz
Co-sponsored by Boston University School of Public Health Department of Epidemiology
Click here to view the December 2018 flyer.
Workshop: Causal Survival Analysis in Follow-Up Studies
Friday November 2, 2018 | 3:45-5:30pm | Ballard Room, Countway Library
Do you work with survival data? Are you wary of the hazards of hazard ratios? Learn how to estimate survival outcomes adjusted for baseline and time-varying confounders! This practical, hands-on workshop will cover estimating survival curves, cumulative incidence differences & ratios, and inverse probability-weighted hazard ratios for intention-to-treat effects and per-protocol treatment effects for randomized trials and observational data. Bring a laptop with R or SAS, we’ll provide the code and real data!
Facilitated by Lucia Petito & Eleanor Murray
Host: Miguel Hernán
Click here to view the November 2018 flyer.
Should Women Use Acetaminophen during Pregnancy? A discussion of analytic choices and improbable study findings.
Friday October 5, 2018 | 3:45-5:30pm | Ballard Room, Countway Library
Currently, acetaminophen is the only over-the-counter pharmaceutical drug approved by the Food and Drug Administration (FDA) for women to use during pregnancy for pain relief or fever reduction. However, recent studies done in cohorts around the world have found an increased risk of neurocognitive outcomes (e.g. ADHD diagnosis, slower language development, lower IQ) in children of women who used acetaminophen during pregnancy. In 2014, the FDA reviewed this research and deemed the level of evidence not high enough to change the current recommendation. In this symposium, we will discuss possible sources of bias in these analyses of cohort data that may have influenced study findings (unmeasured confounding, measurement error, selection bias), what the regulatory implications of these findings are, and how to proceed when study findings polarize the scientific community.
Presentations by: Samantha Parker, Mollie Wood, & Yanmin Zhu
Panel members: Sonia Hernandez-Diaz, Samantha Parker, Martha Werler, Mollie Wood, & Yanmin Zhu
Host: Miguel Hernán
Click here to view the October 2018 flyer.
Spring 2018
The Data Science of Implementation Science
Friday, May 4, 2018 | 3:45-5:30pm | Ballard Room, Countway Library
Implementation science aims to study the process by which original research becomes widely disseminated. It has been described as “a systematic, scientific approach to ask and answer questions about how to get ‘what works’ to people who need it with greater speed, fidelity, efficiency, quality, and relevant coverage.” However, there are many analytic challenges to consider when assessing the effectiveness of the implementation of a public health intervention, which may, at many stages, not be what the original research recommended. In this symposium, we will 1) describe the basic principles of implementation science, 2) provide a concrete example of the public health significance of an implementation science study, and 3) demonstrate some of the methodological work underway to address such challenges.
Presentations by: Dale Barnhart, John Naslund, & Daniel Nevo
Panel members: Dale Barnhart, Miguel Hernán, John Naslund, Daniel Nevo, & Donna Spiegelman
Host: Lucia Petito
Click here to view the May 2018 flyer
Did your mother’s prenatal diet affect you? Using lifecourse data to answer intergenerational questions
Friday, April 6, 2018 | 3:45-5:30pm | Ballard Room, Countway Library
Co-hosted by the Division of Chronic Disease Research Across the Lifecourse (CoRAL) and the Program on Causal Inference
Many chronic childhood diseases are thought to originate in the early stages of human development. Prenatal diet is thought to be a modifiable risk factor that directly impacts child growth in utero. In this symposium, we plan to discuss 1) the evidence that supports the effect of prenatal diet on offspring childhood health outcomes, 2) how researchers use complex longitudinal surveys to conduct intergenerational research, and 3) how to incorporate causal inference ideas into future analyses.
Presentations by: Izzuddin Aris, Yu-Han Chiu, & Karen Switkowski
Panel members: Izzuddin Aris, Yu-Han Chiu, Miguel Hernán, Emily Oken, Karen Switkowski.
Host: Jessica Young
Evidence-Based Policy: How can we use health data to build better microsimulation models?
Friday, March 2, 2018 | 3:45-5:30pm | Ballard Room, Countway Library
Co-hosted by the Center for Health Decision Science and the Program on Causal Inference
Many health policy questions cannot be answered by conducting randomized trials or analyzing observational data. To address these questions, researchers have turned to microsimulation models, which incorporate data from varying sources. In this symposium, we aim to better understand: 1) the types of questions microsimulation models are used to answer, 2) the evidence used to create an individual-level microsimulation model, and 3) the connections between microsimulation models and the parametric g-formula, a causal inference technique. We will discuss recommendations for practical implementation of both methodologies.
Presentations by: Nicole Campos, Vidit Munshi, & Eleanor Murray
Panel members: Nicole Campos, Miguel Hernán, Jane Kim, Vidit Munshi, Eleanor Murray.
Host: Steve Resch
Click here to view the for March 2018 flyer
Do statins increase the survival of cancer patients?
Friday, February 2, 2018 | 3:45-5:30pm | Ballard Room, Countway Library
Co-hosted by the Program on Causal Inference, the Cancer Epidemiology Program, and the Harvard Chan Student Club on CANcer: Learn, Interact, Translate (CAN-LIT)
Over 25 million Americans take statins. While the cardiovascular benefits of statins are not under discussion, there is a debate as to whether statins increase the overall survival of cancer patients. This protective effect is biologically plausible and, in fact, reports from several observational studies have found an inverse association between statins and mortality among cancers patients. However, some of these studies did not explicitly emulate a randomized trial, which might have introduced selection bias and immortal time bias in the estimates. So do statins increase survival in cancer patients or is the inverse association due to bias? In this symposium we will address this question by discussing the available evidence on statins and mortality in cancer patients and will chart a research path forward.
Presentations by: Louise Emilsson, Hari Iyer, & Claire Pernar
Panel members: Barbra Dickerman, Louise Emilsson, Miguel Hernán, Hari Iyer, Lorelei Mucci, Claire Pernar.
Hosts: Joy Shi, Kevin Kensler
Click here to view the February 2018 flyer
