3 citations · 3 across the 4 of their papers we have counts for
5 papers
A Cosine-Projection Framework for Choosing Among Cox HR, D-RMST, and Average-Hazard Tests in Survival Trials
Xiang Meng, Lu Tian, Hajime Uno
The log-rank/Cox hazard-ratio test remains the standard approach for the primary analysis of randomized trials with time-to-event endpoints and is fully efficient under proportiona…
Average Cause-Specific Hazard: A Censoring-Invariant Measure of Event Burden Under Competing Risks
Khondoker Nazmoon Nabi, Xiang Meng, Lu Tian +3
Competing events are common in clinical and epidemiologic studies, including semi-competing risks in which a terminal event such as death may follow a nonfatal event but also compe…
Efficient and Debiased Learning of Average Hazard Under Non-Proportional Hazards
Xiang Meng, Lu Tian, Kenneth Kehl +1
The hazard ratio from the Cox proportional hazards model is a ubiquitous summary of treatment effect. However, when hazards are non-proportional, the hazard ratio can lose a stable…
REFINE2: A tool to evaluate real-world performance of machine-learning based effect estimators for molecular and clinical studies
Xiang Meng, Jonathan Huang
Data-adaptive (machine learning-based) effect estimators are increasingly popular to reduce bias in high-dimensional bioinformatic and clinical studies (e.g. real-world data, targe…
Coherent modeling of longitudinal causal effects on binary outcomes
Linbo Wang, Xiang Meng, Thomas S. Richardson +1
Analyses of biomedical studies often necessitate modeling longitudinal causal effects. The current focus on personalized medicine and effect heterogeneity makes this task even more…