10 citations · 13 across the 4 of their papers we have counts for
6 papers
Large Language Models as Co-Pilots for Causal Inference in Medical Studies
Ahmed Alaa, Rachael V. Phillips, Emre Kıcıman +3
The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about med…
When exposure affects subgroup membership: Framing relevant causal questions in perinatal epidemiology and beyond
Shalika Gupta, Laura B. Balzer, Moses R. Kamya +2
Perinatal epidemiology often aims to evaluate exposures on infant outcomes. When the exposure affects the composition of people who give birth to live infants (e.g., by affecting f…
Applying the causal roadmap to longitudinal national Danish registry data: a case study of second-line diabetes medication and dementia
Nerissa Nance, Andrew Mertens, Thomas Gerds +7
The causal roadmap is a formal framework for causal and statistical inference that supports clear specification of the causal question, interpretable and transparent statement of r…
A Causal Roadmap for Hybrid Randomized and Real-World Data Designs: Case Study of Semaglutide and Cardiovascular Outcomes
Lauren E Dang, Edwin Fong, Jens Magelund Tarp +6
Introduction: Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three…
A Causal Roadmap for Generating High-Quality Real-World Evidence
Lauren E Dang, Susan Gruber, Hana Lee +23
Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks…
Targeted Maximum Likelihood Based Estimation for Longitudinal Mediation Analysis
Zeyi Wang, Lars van der Laan, Maya Petersen +3
Causal mediation analysis with random interventions has become an area of significant interest for understanding time-varying effects with longitudinal and survival outcomes. To ta…