9 papers · 1 filter
Automated, efficient and model-free inference for randomized clinical trials via data-driven covariate adjustment
Kelly Van Lancker, Iván DÃaz, 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…
Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
Matthew Pryce, Karla Diaz-Ordaz, Ruth H. Keogh +1
In recent years, there has been growing interest in causal machine learning estimators for quantifying subject-specific effects of a binary treatment on time-to-event outcomes. Est…
Robust evaluation of treatment effects in longitudinal studies with truncation by death or other intercurrent events
Georgi Baklicharov, Kelly Van Lancker, Stijn Vansteelandt
Intercurrent events, such as treatment switching, rescue medication, dropout, or truncation by death, frequently complicate intention-to-treat analyses in randomized clinical trial…
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…
On estimands in target trial emulation
Edoardo Efrem Gervasoni, Liesbet De Bus, Stijn Vansteelandt +1
The target trial framework enables causal inference from longitudinal observational data by emulating randomized trials initiated at multiple time points. Precision is often improv…
Causal machine learning methods and use of cross-fitting in settings with high-dimensional confounding
Susan Ellul, Stijn Vansteelandt, John B. Carlin +1
Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is chall…