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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 trials. However, it is unclear how the new framework relates to causal i
Externí odkaz:
http://arxiv.org/abs/2412.12380
Autor:
Zhang, Jiaxin, Dashti, S. Ghazaleh, Carlin, John B., Lee, Katherine J., Bartlett, Jonathan W., Moreno-Betancur, Margarita
When using multiple imputation (MI) for missing data, maintaining compatibility between the imputation model and substantive analysis is important for avoiding bias. For example, some causal inference methods incorporate an outcome model with exposur
Externí odkaz:
http://arxiv.org/abs/2411.13829
Autor:
Bonneville, Edouard F., Beyersmann, Jan, Keogh, Ruth H., Bartlett, Jonathan W., Morris, Tim P., Polverelli, Nicola, de Wreede, Liesbeth C., Putter, Hein
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 the covariates included in a given model, researchers may wish to multip
Externí odkaz:
http://arxiv.org/abs/2405.16602
The recently published ICH E9 addendum on estimands in clinical trials provides a framework for precisely defining the treatment effect that is to be estimated, but says little about estimation methods. Here we report analyses of a clinical trial in
Externí odkaz:
http://arxiv.org/abs/2308.13085