5 papers · 1 filter
Bayesian analysis of the causal reference-based model for missing data in clinical trials, accommodating partially observed post-intercurrent event data
Brendah Nansereko, Marcel Wolbers, James R. Carpenter +1
When treatment policy estimands are of interest, clinical trials often attempt to collect patient data after intercurrent events (ICEs), although such data are often limited. Retri…
The role of post intercurrent event data in the estimation of hypothetical estimands in clinical trials
Jonathan W. Bartlett, Rhian M. Daniel
Estimation of hypothetical estimands in clinical trials typically does not make use of data that may be collected after the intercurrent event (ICE). Some recent papers have shown…
Dealing with multiple intercurrent events using hypothetical and treatment policy strategies simultaneously
Camila Olarte Parra, Rhian M. Daniel, Jonathan W. Bartlett
To precisely define the treatment effect of interest in a clinical trial, the ICH E9 estimand addendum describes that relevant so-called intercurrent events should be identified an…
The Estimand Framework and Causal Inference: Complementary not Competing Paradigms
Thomas Drury, Jonathan W. Bartlett, David Wright +1
The creation of the ICH E9 (R1) estimands framework has led to more precise specification of the treatment effects of interest in the design and statistical analysis of clinical tr…
Multiple imputation of missing covariates when using the Fine-Gray model
Edouard F. Bonneville, Jan Beyersmann, Ruth H. Keogh +5
The Fine-Gray model for the subdistribution hazard is commonly used for estimating associations between covariates and competing risks outcomes. When there are missing values in th…