5 papers · 1 filter
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…
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…
Adaptive Proximal Causal Inference with Some Invalid Proxies
Prabrisha Rakshit, Xu Shi, Eric Tchetgen Tchetgen
Proximal causal inference (PCI) is a recently proposed framework to identify and estimate the causal effect of an exposure on an outcome in the presence of hidden confounders, usin…
Fortified Proximal Causal Inference with Many Invalid Proxies
Myeonghun Yu, Xu Shi, Eric J. Tchetgen Tchetgen
Causal inference from observational data often relies on the assumption of no unmeasured confounding, an assumption frequently violated in practice due to unobserved or poorly meas…
Regression-based proximal causal inference for right-censored time-to-event data
Kendrick Li, George C. Linderman, Xu Shi +1
Unmeasured confounding is one of the major concerns in causal inference from observational data. Proximal causal inference (PCI) is an emerging methodological framework to detect a…