4 citations · 11 across the 12 of their papers we have counts for
14 papers
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…
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…
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…
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…
Causal machine learning for high-dimensional mediation analysis using interventional effects mapped to a target trial
Tong Chen, Stijn Vansteelandt, David Burgner +2
Causal mediation analysis examines causal pathways linking exposures to disease. The estimation of interventional effects, which are mediation estimands that overcome certain ident…
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…
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data
Matthew Pryce, Karla Diaz-Ordaz, Ruth H. Keogh +1
When estimating heterogeneous treatment effects, missing outcome data can complicate treatment effect estimation, causing certain subgroups of the population to be poorly represent…
Chasing Shadows: How Implausible Assumptions Skew Our Understanding of Causal Estimands
Stijn Vansteelandt, Kelly Van Lancker
The ICH E9 (R1) addendum on estimands, coupled with recent advancements in causal inference, has prompted a shift towards using model-free treatment effect estimands that are more…
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…
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…
Propensity weighting plus adjustment in proportional hazards model is not doubly robust
Erin E Gabriel, Michael C Sachs, Ingeborg Waernbaum +5
Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, wi…
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…
Parameterising the effect of a continuous treatment using average derivative effects
Oliver J. Hines, Karla Diaz-Ordaz, Stijn Vansteelandt
The average treatment effect (ATE) is commonly used to quantify the main effect of a binary treatment on an outcome. Extensions to continuous treatments are usually based on the do…
Instrumental variables estimation of exposure effects on a time-to-event response using structural cumulative survival models
T. Martinussen, S. Vansteelandt, E. J. Tchetgen Tchetgen +1
The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity…