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Bias reduction in g-computation for covariate adjustment in randomized clinical trials
Xin Zhang, Lin Liu, Haitao Chu
G-computation is a powerful method for estimating unconditional treatment effects with covariate adjustment in randomized clinical trials. It typically relies on fitting canonical…
Assumption-lean covariate adjustment under covariate adaptive randomization when
Yujia Gu, Lin Liu, Wei Ma
Adjusting for (baseline) covariates with working regression models becomes standard practice in the analysis of randomized clinical trials (RCT). When the dimension of the cova…
Covariate Adjustment in Randomized Experiments Motivated by Higher-Order Influence Functions
Sihui Zhao, Xinbo Wang, Lin Liu +1
Higher-Order Influence Functions (HOIF), developed in a series of papers over the past twenty years, are a fundamental theoretical device for constructing rate-optimal causal-effec…
Average Treatment Effect Estimation with Non-binary Instrumental Variables
Mei Dong, Lin Liu, Dingke Tang +3
Non-binary instrumental variables, especially continuous ones, are common in practice. A binary recoding induces a Wald ratio but may discard useful variation and reduce efficiency…
A robust score test in g-computation for covariate adjustment in randomized clinical trials leveraging different variance estimators via influence functions
Xin Zhang, Haitao Chu, Lin Liu +1
G-computation has become a widely used robust method for estimating unconditional (marginal) treatment effects with covariate adjustment in the analysis of randomized clinical tria…