Zobrazeno 1 - 10
of 241
pro vyhledávání: '"Hu Liangyuan"'
In longitudinal observational studies with time-to-event outcomes, a common objective in causal analysis is to estimate the causal survival curve under hypothetical intervention scenarios. The g-formula is a useful tool for this analysis. To enhance
Externí odkaz:
http://arxiv.org/abs/2402.02306
The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence fun
Externí odkaz:
http://arxiv.org/abs/2210.04100
Autor:
Hu, Liangyuan
We recently developed a new method riAFT-BART to draw causal inferences about population treatment effect on patient survival from clustered and censored survival data while accounting for the multilevel data structure. The practical utility of this
Externí odkaz:
http://arxiv.org/abs/2206.08271
Autor:
Li, Jia, Wisnivesky, Juan, Gonzalez, Adam, Feder, Adriana, Pietrzak, Robert H., Chanumolu, Dhanya, Hu, Liangyuan, Kale, Minal
Publikováno v:
In Journal of Affective Disorders 1 January 2025 368:390-397
When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring and unmeasured confoundi
Externí odkaz:
http://arxiv.org/abs/2202.08318
Autor:
Hu, Liangyuan, Ji, Jiayi
CIMTx provides efficient and unified functions to implement modern methods for causal inferences with multiple treatments using observational data with a focus on binary outcomes. The methods include regression adjustment, inverse probability of trea
Externí odkaz:
http://arxiv.org/abs/2110.10276
Publikováno v:
In Contemporary Clinical Trials July 2024 142
To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a novel weighting strategy to account for time-varying confounding and
Externí odkaz:
http://arxiv.org/abs/2109.13368
Autor:
Hu, Liangyuan
Publikováno v:
Bayesian Analysis 2020: 15 (3), 1020-1023
Hahn et al. (2020) offers an extensive study to explicate and evaluate the performance of the BCF model in different settings and provides a detailed discussion about its utility in causal inference. It is a welcomed addition to the causal machine le
Externí odkaz:
http://arxiv.org/abs/2108.02836
Prior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good performances achievable on fully observed data when covariate and outcome data are missing at random (MAR). This app
Externí odkaz:
http://arxiv.org/abs/2107.09730