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pro vyhledávání: '"Bartlett, P. W."'
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
Autor:
Bartlett, Jonathan W., Parra, Camila Olarte, Granger, Emily, Keogh, Ruth H., van Zwet, Erik W., Daniel, Rhian M.
G-formula is a popular approach for estimating treatment or exposure effects from longitudinal data that are subject to time-varying confounding. G-formula estimation is typically performed by Monte-Carlo simulation, with non-parametric bootstrapping
Externí odkaz:
http://arxiv.org/abs/2301.12026
Autor:
Kumar, Bharati, Bartlett, Jonathan W.
Non-proportional hazards (NPH) have been observed in confirmatory clinical trials with time to event outcomes. Under NPH, the hazard ratio does not stay constant over time and the log-rank test is no longer the most powerful test. The weighted log-ra
Externí odkaz:
http://arxiv.org/abs/2209.11702
Autor:
Wolbers, Marcel, Noci, Alessandro, Delmar, Paul, Gower-Page, Craig, Yiu, Sean, Bartlett, Jonathan W.
Clinical trials with longitudinal outcomes typically include missing data due to missed assessments or structural missingness of outcomes after intercurrent events handled with a hypothetical strategy. Approaches based on Bayesian random multiple imp
Externí odkaz:
http://arxiv.org/abs/2109.11162
The ICH E9 addendum introduces the term intercurrent event to refer to events that happen after randomisation and that can either preclude observation of the outcome of interest or affect its interpretation. It proposes five strategies for handling i
Externí odkaz:
http://arxiv.org/abs/2107.04392
Autor:
Bartlett, Jonathan W.
Reference based multiple imputation methods have become popular for handling missing data in randomised clinical trials. Rubin's variance estimator is well known to be biased compared to the reference based imputation estimator's true repeated sampli
Externí odkaz:
http://arxiv.org/abs/2104.14016
Multiple imputation has become one of the most popular approaches for handling missing data in statistical analyses. Part of this success is due to Rubin's simple combination rules. These give frequentist valid inferences when the imputation and anal
Externí odkaz:
http://arxiv.org/abs/1911.09980