3 citations · 6 across the 6 of their papers we have counts for
9 papers
Estimands and estimation in trials with time trends
Marta Bofill Roig, Ekkehard Glimm, Kelly Van Lancker +1
In platform trials, model-based approaches typically estimate treatment effects conditional on calendar time. However, scientific and regulatory interest often lies in treatment ef…
On the use of G-computation in small randomized controlled trials with many covariates
Muluneh Alene, Kelly Van Lancker, Stijn Vansteelandt
In contemporary randomized controlled trials (RCTs), the number of patients is often small relative to the number of baseline covariates collected. In such settings, maximum likeli…
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…
Robust Covariate Adjustment in Multi-Center Randomized Trials
Muluneh Alene, Stijn Vansteelandt, Kelly Van Lancker
Augmented inverse probability weighting and G-computation with canonical generalized linear models have become increasingly popular for estimating the average treatment effect (ATE…
Automated, efficient and model-free inference for randomized clinical trials via data-driven covariate adjustment
Kelly Van Lancker, Iván Díaz, Stijn Vansteelandt
In 2023, the U.S. Food and Drug Administration issued guidance for adjustment of covariates in randomized clinical trials, emphasizing its role in enhancing precision and power thr…
The Use of Covariate Adjustment in Randomized Controlled Trials: An Overview
Kelly Van Lancker, Frank Bretz, Oliver Dukes
There has been a growing interest in covariate adjustment in the analysis of randomized controlled trials in past years. For instance, the U.S. Food and Drug Administration recentl…
Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions
Silvia Calderazzo, Sergey Tarima, Carissa Reid +9
Clinical trials disruption has always represented a non negligible part of the ending of interventional studies. While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive a…
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…
Ensuring valid inference for hazard ratios after variable selection
Kelly Van Lancker, Oliver Dukes, Stijn Vansteelandt
The problem of how to best select variables for confounding adjustment forms one of the key challenges in the evaluation of exposure effects in observational studies, and has been…