9 citations · 21 across the 9 of their papers we have counts for
9 papers
tteICE: An R Package for Estimating Treatment Effects on Time-to-Event Outcomes with Intercurrent Events in Two-Arm Trials
Yuhao Deng, Yi Zhou, Yi Wang +1
Evaluating treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events (ICEs), e.g., treatment discontinuation or competing events. While…
Estimating Pathway Treatment Effects in the Presence of Intermediate Events with Multi-State Data
Yuhao Deng, Haoyu Wei, Donglin Zeng +2
During clinical trials evaluating a drug's effect on a survival endpoint, intermediate events often occur in addition to the primary event. The treatment can exert its effect on th…
CSTEapp: An interactive R-Shiny application of the covariate-specific treatment effect curve for visualizing individualized treatment rule
Yi Zhou, Yuhao Deng, Yu-Shi Tian +5
In precision medicine, deriving the individualized treatment rule (ITR) is crucial for recommending the optimal treatment based on patients' baseline covariates. The covariate-spec…
Inference for Cumulative Incidences and Treatment Effects in Randomized Controlled Trials with Time-to-Event Outcomes under ICH E9 (R1)
Yuhao Deng, Shasha Han, Xiao-Hua Zhou
In randomized controlled trials (RCTs) that focus on time-to-event outcomes, intercurrent events can arise in two ways: as semi-competing events, which modify the hazard of the pri…
Direct and Indirect Treatment Effects in the Presence of Semi-Competing Risks
Yuhao Deng, Yi Wang, Xiao-Hua Zhou
Semi-competing risks refer to the phenomenon that the terminal event (such as death) can censor the non-terminal event (such as disease progression) but not vice versa. The treatme…
Separable pathway effects of semi-competing risks using multi-state models
Yuhao Deng, Yi Wang, Xiang Zhan +1
Semi-competing risks refer to the phenomenon where a primary event (such as mortality) can ``censor'' an intermediate event (such as relapse of a disease), but not vice versa. Unde…
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
Yan Lyu, Sunhao Dai, Peng Wu +7
Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item…
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Peng Wu, Haoxuan Li, Yuhao Deng +6
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction a…
Causal Inference with Truncation-by-Death and Unmeasured Confounding
Yuhao Deng, Yingjun Chang, Xiao-Hua Zhou
Clinical studies sometimes encounter truncation by death, rendering outcomes undefined. Statistical analysis based solely on observed survivors may give biased results because the…