most citedSample size and power calculations for causal inference of observational studies

1 citations · 1 across the 1 of their papers we have counts for

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5 papers

stat.ME20261 cited

Sample size and power calculations for causal inference of observational studies

Bo Liu, Chengxin Yang, Fan Li

This paper investigates the theoretical foundation and develops analytical formulas for sample size and power calculations for causal inference with observational data. By analyzin…

stat.ME2026

Joint Modeling of Longitudinal EHR Data with Shared Random Effects for Informative Visiting and Observation Processes

Cheng-Han Yang, Xu Shi, Bhramar Mukherjee

Longitudinal electronic health record (EHR) data offer opportunities to study biomarker trajectories; however, association estimates-the primary inferential target-from standard mo…

stat.ME2026

Meta-analysis of median survival times with inverse-variance weighting

Sean McGrath, Cheng-Han Yang, Jonathan Kimmelman +3

We consider the problem of meta-analyzing outcome measures based on median survival times. Primary studies with time-to-event outcomes often report estimates of median survival tim…

stat.ME2026

Demystify Doubly-Robust Estimation: The Role of Overlap

Chengxin Yang, Laine E. Thomas, Fan Li

The doubly-robust (DR) estimator is popular for evaluating causal effects in observational studies and is often perceived as more desirable than inverse probability weighting (IPW)…

stat.ME2026

Propensity score weighted Cox regression for survival outcomes in observational studies with multiple or factorial treatments

Zixian Zhao, Chengxin Yang, Fan Li

In observational studies with survival or time-to-event outcomes, a propensity score weighted marginal Cox proportional hazard model with the treatment variable as the only predict…