CAUSALab News

CAUSALab researcher Sophia Rein (et. al.) published a paper in The Lancet HIV on “Integrase strand-transfer inhibitor use and cardiovascular events in adults with HIV: an emulation of target trials in the HIV-CAUSAL Collaboration and the Antiretroviral Therapy Cohort Collaboration.” This research was funded by the National Institute of Allergy and Infectious Diseases and National Institute on Alcohol Abuse and Alcoholism. 
 
A recent observational study suggested that the risk of cardiovascular events could be higher among antiretroviral therapy (ART)-naive individuals with HIV who receive integrase strand-transfer inhibitor (INSTI)-based ART than among those who receive other ART regimens. The team aimed to emulate target trials separately in ART-naive and ART-experienced individuals with HIV to examine the effect of using INSTI-based regimens versus other ART regimens on the 4-year risk of cardiovascular events.
 
You can read the abstract and paper here.
 
Sophia was invited to a podcast from The Lancet HIV. Listen to her talk about the team’s research on Spotify here or on The Lancet HIV‘s website here.

CAUSALab’s Director, Miguel Hernan, is the recipient of the prestigious 2023 Lowell Reed Lectureship Award. This award is in recognition of Dr. Hernan’s groundbreaking work in the field of causal inference and his contribution to shape health policy and research methodology worldwide.
 
Dr. Hernan gave his lecture on “Making decisions is hard, but making decisions without data is even harder: Lessons for the next public health crisis” at the award ceremony of APHA’s Applied Public Health Statistics Section, at the APHA conference in Atlanta, GA on Tuesday, November 14, 2023.

CAUSALab researchers Yu-Han Chiu, Lan Wen, Roger Logan, Issa Dahabreh, Miguel A Hernan along with CAUSALab research collaborators, Lan Wen and Sean McGrath, published a paper in the American Journal of Epidemiology, Evaluating Model Specification When Using the Parametric G-Formula in the Presence of Censoring.

Abstract is shared below and you can access the paper here:

The noniterative conditional expectation (NICE) parametric g-formula can be used to estimate the causal effect of sustained treatment strategies. In addition to identifiability conditions, the validity of the NICE parametric g-formula generally requires the correct specification of models for time-varying outcomes, treatments, and confounders at each follow-up time point. An informal approach for evaluating model specification is to compare the observed distributions of the outcome, treatments, and confounders with their parametric g-formula estimates under the “natural course.” In the presence of loss to follow-up, however, the observed and natural-course risks can differ even if the identifiability conditions of the parametric g-formula hold and there is no model misspecification. Here, we describe 2 approaches for evaluating model specification when using the parametric g-formula in the presence of censoring: 1) comparing factual risks estimated by the g-formula with nonparametric Kaplan-Meier estimates and 2) comparing natural-course risks estimated by inverse probability weighting with those estimated by the g-formula. We also describe how to correctly compute natural-course estimates of time-varying covariate means when using a computationally efficient g-formula algorithm. We evaluate the proposed methods via simulation and implement them to estimate the effects of dietary interventions in 2 cohort studies.

CAUSALab researcher Sophia Rein was selected to attend the 72nd Lindau Nobel Laureate Meeting that was held June 25-30, 2023 in Lindau, Germany. This is a prestigious achievement as this invitation is only extended to “the 600 most qualified scientists from all around the world.”
 
The Lindau Nobel Laureate Meetings – established in 1951 – provide a globally recognized forum for exchange between Nobel Laureates and young scientists. They inspire scientific generations and build sustainable networks of young scientists around the world.
 
You can learn more about the Lindau Nobel Laureate Meetings on their website here. Congrats to Sophia!

CAUSALab researchers Alejandro Szmulewicz and Miguel Hernan published an article in the Journal of Psychopharmacology, Estimating the per-protocol effect of lithium on suicidality in a randomized trial of individuals with depression or bipolar disorder. This research was part of the VA-CAUSAL collaboration, a partnership between CAUSALab and the Veterans Affairs (VA) Boston Healthcare System.

 
Because most randomized trials looking at the effect of lithium on the risk of suicidality (attempts, deaths, hospitalizations) have high rates of non-adherence, it is possible that a beneficial effect of lithium may have been diluted by the non-adherence and thus undetected by the intention-to-treat analysis. They reanalyzed the data from the CSP590 trial, a recent randomized trial conducted in the VA, to estimate the per-protocol effect of lithium use on recurrent suicidality among patients with affective disorders.

You can read the abstract and study here.

CAUSALab researcher and Associate Professor Issa Dahabreh and BIDMC Smith Center for Outcomes Research Associate Director Dhruv Kazi published an editorial in JAMA Network on their insights on the study, Heterogeneous Treatment Effects of Therapeutic-Dose Heparin in Patients Hospitalized for COVID-19.
 
Dahabreh and Kazi highlighted in the abstract of their editorial, “Toward Personalizing Care: Assessing Heterogeneity of Treatment Effects in Randomized Trials” the following:
 

Clinicians know that individual patients may respond differently to a given treatment and that the overall treatment effect reported in a randomized trial of the treatment may not be directly applicable to all patients in clinical practice.¹ Determining the treatment effect for an individual patient involves a comparison of the outcome when that patient is exposed to the treatment vs the outcome of the same patient exposed to a control treatment at the same time, a comparison impossible to make in conventional parallel-group trial designs. A practical alternative is to examine heterogeneity of (variation in) treatment effects across groups of patients, categorized by baseline demographic or clinical characteristics, such as age or risk factors for the outcome.²

To view the editorial piece, check it out on JAMA Network here.

CAUSALab researcher, Andrew Beam, is a Founding Editor of a new exciting journal, NEJM AI. This new journal comes from NEJM Group, the publisher of New England Journal of Medicine (NEJM) with the purpose to “identify and evaluate state-of-the-art applications of artificial intelligence to clinical medicine. In addition to original research, NEJM AI will provide reviews, policy perspectives, and accessible education material targeted at practicing physicians and clinician leaders interested in applying AI, computer scientists seeking to translate algorithmic advances to clinical practice, and policy makers and regulators.” Learn more on NEJM AI’s site here.

 
NEJM will be published on a monthly basis online. Additionally, you can catch new monthly podcast of NEJM AI Ground Rounds Podcast and sign up for a twice-monthly newsletter to stay up to date on NEJM AI news.

Observational studies are often the only option to estimate effects of interventions during pregnancy. Causal inference from observational data can be conceptualized as an attempt to emulate a hypothetical pragmatic randomized trial: the target trial. CAUSALab researchers Sonia Hernandez-Diaz, Yu-Han Chiu, and Miguel Hernan (et al.) dove into this research more with their recently published study, “Emulating a Target Trial of Interventions Initiated During Pregnancy with Healthcare Databases: The Example of COVID-19 Vaccination”. You can read the abstract and study in Epidemiology here.

CAUSALab researcher Emma McGee was selected to present at a Dana-Farber/Harvard Cancer Center symposium highlighting promising early career investigators in cancer research! She will present work conducted in collaboration with Drs. Barbra Dickerman and Miguel Hernan which discusses methodological challenges in estimating the long-term effects of lifestyle interventions for cancer survivors. The symposium will be held this Friday, February 10, 2023, online and in-person at Dana-Farber. For more details and to RSVP:

Celebration of Junior Investigators – DF/HCC (harvard.edu)

Third doses of Moderna and Pfizer COVID-19 mRNA vaccines were found to be effective in protecting against SARS-COV-2 infection and severe COVID-19 outcomes based on a large study conducted by the VA-CAUSAL collaboration, a partnership between CAUSALab and the Veterans Affairs (VA) Boston Healthcare System.
 

CAUSALab researcher Barbra Dickerman was first co-author of this study along with Hanna Gerlovin of the Massachusetts Veterans Epidemiology Research and Information Center, VA Boston.

This study is published in the journal Nature Microbiology and can be found here.

Researchers often use random-effects or fixed-effects meta-analysis to combine findings from multiple populations. However, the causal interpretation of these models is not always clear, and they do not easily translate to settings where bounds, rather than point estimates, are computed. CAUSALab researchers Sonja Swanson and Elizabeth Diemer investigated this by pooling bounds on the average causal effect of prenatal alcohol exposure on attention deficit-hyperactivity disorder symptoms, computed in two European cohorts and under multiple sets of assumptions in Mendelian randomization (MR) analyses. They concluded that all pooled bounds computed in their application covered the null, illustrating how strongly point estimates from prior MR studies of this effect rely on within-study homogeneity assumptions.
 
This research was part of the HARVEST collaboration, supported by the Research Council of Norway (#229624).
 
You can read more about their study and results in the Journal of Epidemiology here.

Results from our research on Effectiveness of 17-OHP for Prevention of Recurrent Preterm Birth: A Retrospective Cohort Study failed to identify a beneficial effect of 17-OHP for the prevention of spontaneous recurrent preterm birth from our observational, U.S. based cohort. This study was led by CAUSALab researchers Andrew Beam, Joe Hakim and Sonia Hernandez-Diaz, along with researchers, Jessica Hart, Blair Wylie and Amy Zhou and was published in American Journal of Perinatology in December 2021. The Food and Drug Administration (FDA) cited this research as evidence when they recommended removing a preterm drug from the market. You can learn more about the FDA’s decision here.

CAUSALab researchers Andrew Beam, David Bellamy, Thomas Kolokotrones, Benjamin Kompa, and James Robins presented their new study at NeurIPS’s Thirty-Sixth Conference on Neural Information Processing Systems on November 30, 2022 in New Orleans, LA. The study has been published in ArxIV. Abstract is shared below:

The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identification results that succeed even in the presence of unobserved confounders, provided that one has measured a sufficiently rich set of proxy variables, satisfying specific structural conditions. However, proximal inference requires solving an ill-posed integral equation. Previous approaches have used a variety of machine learning techniques to estimate a solution to this integral equation, commonly referred to as the bridge function. However, prior work has often been limited by relying on pre-specified kernel functions, which are not data adaptive and struggle to scale to large datasets. In this work, we introduce a flexible and scalable method based on a deep neural network to estimate causal effects in the presence of unmeasured confounding using proximal inference. Our method achieves state of the art performance on two well-established proximal inference benchmarks. Finally, we provide theoretical consistency guarantees for our method. 

In this new target trial emulation using data from the STAR*D randomized trial, CAUSALab researchers Alejandro Szmulewicz, Sonia Hernandez-Diaz, Kerollos Wanis, and Miguel Hernan compared three treatment strategies in patients with major depressive disorder for whom citalopram failed as initial therapy: (1) continuously switching antidepressants, (2) continuously combining/augmenting, and (3) to follow guidelines recommendations: switch if no response or intolerability and combine/augment if partial response. The estimated 9-month probability of remission was 43.5% for continuously switching, 47.6% for continuously combining/augmenting, and 53.2% for the guidelines-based. The 9-month relative risk of serious events comparing guidelines-based strategy to continuously switching was 0.62 (0.33, 1.00). In conclusion, using the guidelines-based strategy was associated with an increased probability of remission and a lower risk of serious adverse events. The potential implications are substantial given the large number of patients experiencing treatment failure to antidepressants.

American Journal of Epidemiology and the Society for Epidemiologic Research has named the article, “Study designs for extending causal inferences from a randomized trial to a target population” as one of their 2021 Articles of the Year. Congratulations to CAUSALab authors, Issa Dahabreh, Miguel Hernan, Jamie Robins and Sarah Robertson for this distinguished recognition. Congratulations also to co-authors, Sebastien Haneuse, Ashley Buchanan, and Elizabeth Stuart. The American Journal of Epidemiology describes the 2021 Articles of the Year as, “In our assessment, the articles of the year, which are chosen by the editors of the Journal, represent scholarship that is truly distinguished.”

A list of all 2021 Articles of the Year will be released online and will appear in the American Journal of Epidemiology. Published dates forthcoming.

Thank you to the American Journal of Epidemiology and the Society for Epidemiologic Research for this recognition.

Colonoscopy screening reduces the risk of colorectal cancer, according to the results of the recently published Nordic-European Initiative on Colorectal Cancer (NordICC) trial in the New England Journal of Medicine. This pragmatic, multicenter randomized trial was led by researchers at the University of Oslo with contributions from CAUSALab researchers Miguel A. Hernán and Joy Shi.

The trial included 84,585 participants who were randomized to either receive an invitation to undergo a single colonoscopy screening or receive no invitation. Risk of colorectal cancer at 10 years was 18% lower among those who received an invitation (10-year risk of 0.98%) compared to those in the usual-care group (10-year risk of 1.20%). These findings are important for informing the relative benefits and harms of population-level colonoscopy screening programs and provide evidence for decision makers when prioritizing resources for comprehensive cancer control.

Our cohort study of over 20,000 male veterans with HIV from the Veterans Aging Cohort Study (VACS) shows that the risks of SARS-CoV-2 infection, COVID-19-related hospitalization and ICU admissions were lowest for those taking tenofovir disoproxil (TDF)/emtricitabine (FTC), compared with other antiretroviral regimens. Our findings, along with other current evidence, are important to inform clinical trials investigators to further examine the potential role of TDF/FTC as prophylaxis and early treatment of COVID-19 in the general population. This study was funded by National Institutes of Health and US Department of Veterans Affairs Office of Research and Development. You can read more about the report here.

Congratulations to CAUSALab’s Drs. Jamie Robins, Head of Methods Research, and Miguel Hernan, Director, along with the other laureates, Andrea Rotnitzky, Eric Tchetgen Tchetgen and Thomas Richardson, who have won the 2022 Rousseeuw Prize for Statistics for their pioneering work on Causal Inference with applications in Medicine and Public Health!

The King Baudouin Foundation has honored these laureates with the 2022 Rousseeuw Prize for Statistics for their work which has “completely transformed the way in which statisticians, epidemiologists, and others infer the effects of interventions, treatments, and exposures to potentially harmful substances. It has greatly improved the overall reliability of causal analysis in medicine and public health, with great benefit to society.”

As reported in KBF’s press release,

Half the prize amount will go to James Robins of Harvard University and half will be shared by Miguel Hernán (Harvard University), Thomas Richardson (University of Washington), Andrea Rotnitzky (Universidad Torcuato di Tella, Argentina) and Eric Tchetgen Tchetgen (University of Pennsylvania). The latter four laureates were either trained or deeply influenced by Robins. They remain his principal collaborators to this day.

The prize will be awarded at a ceremony taking place at University of Leuven, Belgium on Wednesday, October 12, 2022.

CAUSALab researcher and Postdoc Fellow, Alejandro Szmulewicz, has been awarded the 2022 Young Investigator Grant by Brain and Behavior Research Foundation. The BBRF Young Investigator Grant provides support for the most promising young scientists. Alejandro was recognized for his participation in the creation of an international consortium of cohorts of individuals with early psychosis: the FEP-CAUSAL Collaboration that is used as a platform for target trial emulations.

CAUSALab researcher and Postdoc Fellow, Gonzalo Martinez-Ales, has been awarded the 2022 Young Investigator Grant by Brain and Behavior Research Foundation. The BBRF Young Investigator Grant provides support for the most promising young scientists. Gonzalo’s project on Suicide Prevention will use target trial emulation to estimate the comparative effectiveness of antidepressants for post-discharge suicide prevention.

Emma McGee, CAUSALab researcher and PhD student, has been awarded the Eric and Wendy Schmidt Center (EWSC) PhD Fellowship from the Broad Institute. This competitive fellowship recognizes PhD students doing exceptional work in causal inference, machine learning, and human biology. The fellowship funds 100% of Emma’s graduate school tuition, fees, and stipend.