2 citations · 3 across the 6 of their papers we have counts for
7 papers
Targeted maximum likelihood estimation for longitudinal two-stage designs with outcome subsampling
Kirsten E. Landsiedel, Maya L. Petersen, Mark J. van der Laan
We consider efficient estimation of causal parameters in longitudinal two-stage designs with outcome subsampling, motivated by resampling designs in HIV-related mortality studies.…
Hazard-Based Targeted Maximum Likelihood Estimation for Survival in Resampling Designs
Kirsten E. Landsiedel, Rachael V. Phillips, Maya L. Petersen +1
Survival is a key metric for evaluating standards of care for people living with HIV. In resource-limited settings, high rates of loss to follow-up (LTFU) often result in underesti…
Deep Causal Behavioral Policy Learning: Applications to Healthcare
Jonas Knecht, Anna Zink, Jonathan Kolstad +1
We present a deep learning-based approach to studying dynamic clinical behavioral regimes in diverse non-randomized healthcare settings. Our proposed methodology - deep causal beha…
An Online Meta-Level Adaptive Design Framework with Targeted Learning Inference: Applications to Evaluating and Utilizing Surrogate Outcomes in Adaptive Designs
Wenxin Zhang, Aaron Hudson, Maya Petersen +1
Adaptive designs are increasingly used in clinical trials and online experiments to improve participant outcomes by dynamically updating treatment allocation as data accumulate. In…
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…
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…
The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-Data Applications
Nerissa Nance, Maya L. Petersen, Mark van der Laan +1
The Causal Roadmap outlines a systematic approach to asking and answering questions of cause-and-effect: define the quantity of interest, evaluate needed assumptions, conduct stati…