The CAUSALab uses data to investigate what works in medicine, public health, and policy. We generate, analyze, and interpret data so that decision makers—patients, clinicians, regulators, policy makers…—can make better decisions. By combining sound methodology with high-quality data, we produce actionable causal inference with real-world impact. We also train the next generation of investigators.
Our methodological research focuses on developing a methodological framework for causal inference research based on observational health databases (e.g., administrative claims, electronic health records, biobanks) and pragmatic randomized trials. Our areas of work include causal inference and AI, transportability, instrumental variable estimation based on genetic variants (aka, Mendelian randomization), g-methods for sustained treatment strategies, and benchmarking of observational studies with randomized trials.
Our applied research focuses on implementing the frameworks to determine comparative effectiveness and safety of health and policy interventions. Our areas of work include infectious diseases, cardiovascular diseases, cancer, mental Health, and pregnancy.
VA-CAUSAL Methods Core: Infectious diseases (COVID-19), Cancer, Cardiovascular disease
VA-CAUSAL is a new causal inference research initiative within the Veterans Health Administration. The goal of VA-CAUSAL is to help transform the VA into a learning health system that expedites the translation of research into practice and supports decision-making by patients, clinicians, and other stakeholders to improve health.
The Methods Core of VA-CAUSAL develops and applies causal inference methods using large-scale data resources at the VA, including electronic health records and the Million Veterans Program multi-omics biobank. Our projects include explicit emulation of target trials of sustained treatment strategies using real-world data, advanced instrumental variable estimation for Mendelian randomization, and estimation of per-protocol effects in randomized trials. The methods, computer code and materials generated during these projects are optimized for use by investigators in the VA research ecosystem and, in collaboration with the Implementation Core of VA-CAUSAL, made available to them.
Funding
The VA-CAUSAL Methods Core is a collaboration between the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the Boston VA and the CAUSALab at the Harvard T.H. School of Public Health. It is funded by the Cooperative Studies Program of the U.S. Department of Veterans Affairs as part of the study CSP #2032.
HIV-CAUSAL Collaboration: Infectious diseases (HIV)
The HIV-CAUSAL Collaboration is a multinational consortia of HIV cohorts—prospective observational studies of persons living with HIV—from Europe and the Americas. The research team includes investigators from each of theparticipating studiesand from the coordinating center.
The investigators of the HIV-CAUSAL Collaboration love randomized trials, but also understand that trials cannot answer all important questions for the clinical management of HIV, and cannot answer some urgent questions in a timely manner. Because observational HIV cohorts will necessarily be used to inform clinical decisions, it is important that observational data sources are used in the best possible way. The HIV-CAUSAL Collaboration was created to tackle the difficult methodological problems that arise when conducting comparative effectiveness and safety research in HIV cohorts.
For over a decade, the HIV-CAUSAL Collaboration has been at the forefront of development and implementation of statistical methods to compare the effectiveness of clinical strategies using HIV cohorts. Some of the methods pioneered by the Collaboration, including inverse probability weighting of marginal structural models and the parametric g-formula, have transformed the analysis of HIV cohorts in the 21st century. Investigators now have at their disposal a powerful arsenal of tools to evaluate the effectiveness and safety of clinical strategies using observational data. Our methodological work supports the clinical research that studies novel problems in HIV research.
Besides being an incubator for new methodologies, the HIV-CAUSAL Collaboration conducts clinical HIV researchdesigned to inform evidence-based guidelines and the planning of randomized trials. Findings from the HIV-CAUSAL Collaboration played a prominent role in the now largely settled discussions about when to start antiretroviral therapy. Our recent research has contributed to a better understanding of the effects of antiretroviral therapy on opportunistic infections, neurological conditions, and viral resistance, as well to the study of dynamic strategies for the treatment and monitoring of individuals living with HIV.
In addition, the Collaboration facilitates understanding and training in causal modeling across leading HIV observational research groups in Europe and the Americas.
Funding
Our research is supported by MERIT award R37 AI102634 (PI: Hernán) from the National Institute of Allergy and Infectious Diseases, U.S. National Institutes of Health.
Hep-CAUSAL Collaboration: Infectious diseases (Hepatitis C)
Hep-CAUSAL is a consortium of cohorts of people who are co-infected with HIV and the hepatitis C virus (HCV) in Europe and the Americas. The research team, led by Sara Lodi, includes investigators from each of the participating studies.
Co-infection places patients at higher risks of morbidity and mortality from hepatic (cirrhosis, hepatocellular carcinoma) and extra-hepatic (cardiovascular disease, kidney disease, diabetes) diseases. Direct-acting antiviral agents (DAA), which clear the virus in 95 percent of patients, transformed treatment for HCV infection. However, even after cure is achieved, the risk of hepatic and extra-hepatic disease remains and some individuals become reinfected with HCV. By combining observational data and cutting-edge causal inference methods, Hep-CAUSAL will estimate the long-term risk of HCV reinfection, hepatic disease, and extra-hepatic disease under the current guidelines that recommend DAA for all HCV and HIV co-infected patients. As millions of people are expected to receive DAA in the US and globally, the findings of Hep-CAUSAL will inform post-cure management guidelines and how to maximize the benefits of DAA treatment in the long-term.
Funding
Our research is supported by MERIT award R37 AI102634 (PI: Hernán) from the National Institute of Allergy and Infectious Diseases, U.S. National Institutes of Health.
FEP-CAUSAL Collaboration: Mental health (first episode psychosis)
The CAUSALab is the Methods Core of the Laboratory for Early Psychosis (LEAP) Center, one of the U.S. National Institute of Mental Health’s Advanced Laboratories for Accelerating the Reach and Impact of Treatments for Youth and Adults With Mental Illness (ALACRITY) Research Centers.
The FEP-CAUSAL Collaboration is an international consortium of prospective cohorts of individuals with first episode psychosis (FEP) that is coordinated by LEAP’s Methods Core. Come back soon for more information about FEP-CAUSAL.
Funding
Our research is supported by grant P50 MH115846 (PIs: Öngür, Hsu, Hernán) from the National Institute of Mental Health, U.S. National Institutes of Health.
Pregnancy
Come back soon for more information about research on pregnancy.
Funding
Our research is supported by grant R01 HD097778 (PI: Hernández-Díaz) from the National Institute of Child Health and Human Development, U.S. National Institutes of Health.
Benchmarking and transportability
Come back soon for more information about research on benchmarking and transportability.
Randomized Trials
Pragmatic randomized trials are designed to support clinical decision-making rather than regulatory approval. The features of pragmatic trials (e.g., typical patients in typical care settings, active treatment strategies as comparators, unconcealed assignment, long-term outcomes) make their results more relevant for patients, clinicians, and other stakeholders. However, these features also introduce an increased risk of non-adherence and loss to follow-up, which reduce the value of the usual intention-to-treat analysis.
This document proposes a preliminary set of guidelines for more informative causal inference in pragmatic trials. We hope that a revised version of these draft guidelines will help guide the design and analysis of future pragmatic trials. We are therefore soliciting feedback to ensure that the content and citations of the revised guidelines reflect the best practices for estimation of causal effects in pragmatic trials.
If you have comments, please send them to Eleanor Murray (ejmurray@bu.edu). You can also download the MS Word file and send track changes directly in the manuscript.
For background information, click here.
The development of these guidelines was supported through a Patient Centered Outcomes Research Institute (PCORI) award (ME-1503-8119). All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee.
