10 citations · 31 across the 16 of their papers we have counts for
16 papers
Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches
Laura B. Balzer, Lei Nie, Issa J. Dahabreh +15
While randomization justifies the use of unadjusted effect estimators in randomized trials, there is growing interest in covariate adjustment to improve precision. Adjusting for ba…
History-Aware Conformal Prediction Sets for Censored Time-to-Event Outcomes
Yuyao Wang, Alexander W. Levis, Shu Yang +1
Existing conformal prediction methods for time-to-event outcomes leverage only baseline covariates, producing prediction intervals that are insufficiently informative to facilitate…
Considerations for the Integration of Randomized Controlled Trials and Real-World Data
Sky Qiu, Charles Barr, Lauren Dang +18
As clinical decision-making increasingly moves toward individualized and context-specific treatment recommendations, reliance on any single evidence source, randomized or observati…
On the Role of Surrogates in Conformal Inference of Individual Causal Effects
Chenyin Gao, Peter B. Gilbert, Larry Han
Learning the Individual Treatment Effect (ITE) is essential for personalized decision-making, yet causal inference has traditionally focused on aggregated treatment effects. While…
Targeted Data Fusion for Region-Specific Survival Effects in the AMP HIV Prevention Trials
Yi Liu, Alexander W. Levis, Ke Zhu +3
The Antibody Mediated Prevention (AMP) trials opened a new scientific frontier by showing that passively administered monoclonal broadly neutralizing antibodies (bnAbs) could preve…
COADVISE: Covariate Adjustment with Variable Selection in Randomized Controlled Trials
Yi Liu, Ke Zhu, Larry Han +1
Adjusting for covariates in randomized controlled trials can enhance the credibility and efficiency of treatment effect estimation. However, handling numerous covariates and their…
Bounding causal effects with an unknown mixture of informative and non-informative missingness
Max Rubinstein, Denis Agniel, Larry Han +2
In experimental and observational data settings, researchers often have limited knowledge of the reasons for missing outcomes. To address this uncertainty, we propose bounds on cau…
A Surrogate Endpoint Based Provisional Approval Causal Roadmap
Peter B. Gilbert, James Peng, Larry Han +12
For many rare diseases with no approved preventive interventions, promising interventions exist, yet it has been difficult to conduct a pivotal phase 3 trial that could provide dir…
Multi-Source Conformal Inference Under Distribution Shift
Yi Liu, Alexander W. Levis, Sharon-Lise Normand +1
Recent years have experienced increasing utilization of complex machine learning models across multiple sources of data to inform more generalizable decision-making. However, distr…
A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery
Larry Han, Yi Zhang, Meena Nathan +5
Congenital heart defects (CHD) are the most prevalent birth defects in the United States and surgical outcomes vary considerably across the country. The outcomes of treatment for C…
Multiply Robust Federated Estimation of Targeted Average Treatment Effects
Larry Han, Zhu Shen, Jose Zubizarreta
Federated or multi-site studies have distinct advantages over single-site studies, including increased generalizability, the ability to study underrepresented populations, and the…
Robust Inference for Federated Meta-Learning
Zijian Guo, Xiudi Li, Larry Han +1
Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogene…
Sensitivity Analyses of Clinical Trial Designs: Selecting Scenarios and Summarizing Operating Characteristics
Larry Han, Andrea Arfe, Lorenzo Trippa
The use of simulation-based sensitivity analyses is fundamental to evaluate and compare candidate designs for future clinical trials. In this context, sensitivity analyses are espe…
Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement
Larry Han, Yige Li, Bijan A. Niknam +1
Integrating information from multiple data sources can enable more precise, timely, and generalizable decisions. However, it is challenging to make valid causal inferences using ob…
Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects
Larry Han, Jue Hou, Kelly Cho +2
Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecific…
On the Evaluation of Surrogate Markers in Real World Data Settings
Larry Han, Xuan Wang, Tianxi Cai
Shortcomings of randomized clinical trials are pronounced in urgent health crises, when rapid identification of effective treatments is critical. Leveraging short-term surrogates i…