output
20182025
most citedEnvironment Scan of Generative AI Infrastructure for Clinical and Translational Science

10 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
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6 papers · 1 filter

stat.ME2025

Improving Variance and Confidence Interval Estimation in Small-Sample Propensity Score Analyses: Bootstrap vs. Asymptotic Methods

Baoshan Zhang, Sean M. O'Brien, Yuan Wu +1

Propensity score (PS) methods are widely used to estimate treatment effects in non-randomized studies. Variance is typically estimated using sandwich or bootstrap methods, which ca…

stat.ME2021★ 1 cited

Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach

Siyun Yang, Fan Li, Laine E. Thomas

Background: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although ra…

stat.ME2020★ 9 cited

Propensity Score Weighting for Causal Subgroup Analysis

Siyun Yang, Elizabeth Lorenzi, Georgia Papadogeorgou +3

A common goal in comparative effectiveness research is to estimate treatment effects on pre-specified subpopulations of patients. Though widely used in medical research, causal inf…

stat.ME2020

Regression with a right-censored predictor, using inverse probability weighting methods

Roland A. Matsouaka, Folefac D. Atem

In a longitudinal study, measures of key variables might be incomplete or partially recorded due to drop-out, loss to follow-up, or early termination of the study occurring before…

stat.ME2020★ 7 cited

An improved sample size calculation method for score tests in generalized linear models

Yongqiang Tang, Liang Zhu, Jiezhun Gu

Self and Mauritsen (1988) developed a sample size determination procedure for score tests in generalized linear models under contiguous alternatives. Its performance may deteriorat…

stat.ME2018★ 3 cited

Semiparametric estimation of structural failure time model in continuous-time processes

Shu Yang, Karen Pieper, Frank Cools

Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been propose…