34 citations · 89 across the 8 of their papers we have counts for
8 papers
A unified Bayesian framework for interval hypothesis testing in clinical trials
Abhisek Chakraborty, Megan H. Murray, Ilya Lipkovich +1
The American Statistical Association (ASA) statement on statistical significance and P-values \cite{wasserstein2016asa} cautioned statisticians against making scientific decisions…
Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data
Ilya Lipkovich, David Svensson, Bohdana Ratitch +1
In this paper we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation…
Improving randomized controlled trial analysis via data-adaptive borrowing
Chenyin Gao, Shu Yang, Mingyang Shan +3
In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balan…
Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study
Xiaoqing Tan, Shu Yang, Wenyu Ye +3
Observational cohort studies are increasingly being used for comparative effectiveness research to assess the safety of therapeutics. Recently, various doubly robust methods have b…
Selection bias in the treatment effect for a principal stratum
Yongming Qu, Stephen J. Ruberg, Junxiang Luo +1
Estimation of treatment effect for principal strata has been studied for more than two decades. Existing research exclusively focuses on the estimation, but there is little researc…
Using principal stratification in analysis of clinical trials
Ilya Lipkovich, Bohdana Ratitch, Yongming Qu +3
The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limita…
Assessing the commonly used assumptions in estimating the principal causal effect in clinical trials
Yongming Qu, Ilya Lipkovich, Stephen J. Ruberg
In clinical trials, it is often of interest to understand the principal causal effect (PCE), the average treatment effect for a principal stratum (a subset of patients defined by t…
Implementation of ICH E9 (R1): a few points learned during the COVID-19 pandemic
Yongming Qu, Ilya Lipkovich
The current COVID-19 pandemic poses numerous challenges for ongoing clinical trials and provides a stress-testing environment for the existing principles and practice of estimands…