Publicaties
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Longitudinal mediation analysis of time-to-event endpoints in the presence of competing risks Universiteit Gent
A statistical test to reject the structural interpretation of a latent factor model Universiteit Gent
On estimation and cross–validation of dynamic treatment regimes with competing risks Universiteit Gent
The optimal moment to start renal replacement therapy in a patient with acute kidney injury (AKI) remains a challenging problem in intensive care nephrology. Multiple randomised controlled trials have tried to answer this question, but these contrast only a limited number of treatment initiation strategies. In view of this, we use routinely collected observational data from the Ghent University Hospital intensive care units (ICUs) to investigate ...
Timing of dialysis in acute kidney injury using routinely collected data and dynamic treatment regimes Universiteit Gent
Background and objectives : Defining the optimal moment to start renal replacement therapy (RRT) in acute kidney injury (AKI) remains challenging. Multiple randomized controlled trials (RCTs) addressed this question whilst using absolute criteria such as pH or serum potassium. However, there is a need for identification of the most optimal cut-offs of these criteria. We conducted a causal analysis on routinely collected data (RCD) to compare the ...
Robust inference for mediated effects in partially linear models Universiteit Gent
Principled selection of baseline covariates to account for censoring in randomized trials with a survival endpoint Universiteit Gent
Efficient, doubly robust estimation of the effect of dose switching for switchers in a randomized clinical trial Universiteit Gent
Confounder selection strategies targeting stable treatment effect estimators Universiteit Gent
Inferring the causal effect of a treatment on an outcome in an observational study requires adjusting for observed baseline confounders to avoid bias. However, adjusting for all observed baseline covariates, when only a subset are confounders of the effect of interest, is known to yield potentially inefficient and unstable estimators of the treatment effect. Furthermore, it raises the risk of finite-sample bias and bias due to model ...