13 citations · 21 across the 10 of their papers we have counts for
11 papers
Improving Variance Estimation for Covariate Adjustment with Binary Outcomes
Kaitlyn Lee, Alex Ocampo, Courtney Schiffman +3
Covariate adjustment is a general method for improving precision when estimating treatment effects in randomized trials and is recommended by the FDA in its 2023 guidance when base…
Using Targeted Maximum Likelihood Estimation to Estimate Treatment Effect with Longitudinal Continuous or Binary Data: A Systematic Evaluation of 28 Diabetes Clinical Trials
Lingjing Jiang, Michael Rosenblum, Yu Du
The primary analysis of clinical trials in diabetes therapeutic area often involves a mixed-model repeated measure (MMRM) approach to estimate the average treatment effect for long…
Combining Covariate Adjustment with Group Sequential, Information Adaptive Designs to Improve Randomized Trial Efficiency
Kelly Van Lancker, Joshua Betz, Michael Rosenblum
In clinical trials, there is potential to improve precision and reduce the required sample size by appropriately adjusting for baseline variables in the statistical analysis. This…
Optimizing Precision and Power by Machine Learning in Randomized Trials, with an Application to COVID-19
Nicholas Williams, Michael Rosenblum, Iván Díaz
The rapid finding of effective therapeutics requires the efficient use of available resources in clinical trials. The use of covariate adjustment can yield statistical estimates wi…
The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods
Roy Adams, Suchi Saria, Michael Rosenblum
The use of observational time series data to assess the impact of multi-time point interventions is becoming increasingly common as more health and activity data are collected and…
Improving Precision through Adjustment for Prognostic Variables in Group Sequential Trial Designs: Impact of Baseline Variables, Short-Term Outcomes, and Treatment Effect Heterogeneity
Tianchen Qian, Michael Rosenblum, Huitong Qiu
In randomized trials, appropriately adjusting for baseline variables and short-term outcomes can lead to increased precision and reduced sample size. We examine the impact of such…
Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment
Bingkai Wang, Ryoko Susukida, Ramin Mojtabai +2
Two commonly used methods for improving precision and power in clinical trials are stratified randomization and covariate adjustment. However, many trials do not fully capitalize o…
Improved Precision in the Analysis of Randomized Trials with Survival Outcomes, without Assuming Proportional Hazards
Iván Díaz, Elizabeth Colantuoni, Daniel F. Hanley +1
We present a new estimator of the restricted mean survival time in randomized trials where there is right censoring that may depend on treatment and baseline variables. The propose…
Targeted Maximum Likelihood Estimation using Exponential Families
Iván Díaz, Michael Rosenblum
Targeted maximum likelihood estimation (TMLE) is a general method for estimating parameters in semiparametric and nonparametric models. Each iteration of TMLE involves fitting a pa…
interAdapt -- An Interactive Tool for Designing and Evaluating Randomized Trials with Adaptive Enrollment Criteria
Aaron Fisher, Harris Jaffee, Michael Rosenblum
The interAdapt R package is designed to be used by statisticians and clinical investigators to plan randomized trials. It can be used to determine if certain adaptive designs offer…
Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming
Michael Rosenblum, Han Liu, and En-Hsu Yen
We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such sub-populations could…