Zobrazeno 1 - 10
of 652
pro vyhledávání: '"BAIO, GIANLUCA"'
Autor:
Phillippo, David M., Remiro-Azócar, Antonio, Heath, Anna, Baio, Gianluca, Dias, Sofia, Ades, A. E., Welton, Nicky J.
Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, treatment recommendations may vary by population and by subgroups withi
Externí odkaz:
http://arxiv.org/abs/2410.11438
Polyhazard models are a class of flexible parametric models for modelling survival over extended time horizons. Their additive hazard structure allows for flexible, non-proportional hazards whose characteristics can change over time while retaining a
Externí odkaz:
http://arxiv.org/abs/2406.14182
Time to an event of interest over a lifetime is a central measure of the clinical benefit of an intervention used in a health technology assessment (HTA). Within the same trial multiple end-points may also be considered. For example, overall and prog
Externí odkaz:
http://arxiv.org/abs/2401.13820
Autor:
Gascoigne, Connor, Blangiardo, Marta, Shao, Zejing, Jeffery, Annie, Geneletti, Sara, Kirkbride, James, Baio, Gianluca
Factors contributing to social inequalities are also associated with negative mental health outcomes leading to disparities in mental well-being. We propose a Bayesian hierarchical model which can evaluate the impact of policies on population well-be
Externí odkaz:
http://arxiv.org/abs/2306.15525
We examine four important considerations in the development of covariate adjustment methodologies for indirect treatment comparisons. Firstly, we consider potential advantages of weighting versus outcome modeling, placing focus on bias-robustness. Se
Externí odkaz:
http://arxiv.org/abs/2305.08651
When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates. The treatment coefficient of the outcome model targets a condition
Externí odkaz:
http://arxiv.org/abs/2305.08284
In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimensional settings featuring multiple actions and multiple correlated outc
Externí odkaz:
http://arxiv.org/abs/2211.11119
Patients who are mechanically ventilated in the intensive care unit (ICU) participate in exercise as a component of their rehabilitation to ameliorate the long-term impact of critical illness on their physical function. The effective implementation o
Externí odkaz:
http://arxiv.org/abs/2206.14047
In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models. In particular, we focus on the problem of estimating individual causal/treatment effects under observed confoun
Externí odkaz:
http://arxiv.org/abs/2206.10261
Background Survival extrapolation is essential in the cost-effectiveness analysis to quantify the lifetime survival benefit associated with a new intervention, due to the restricted duration of randomized controlled trials (RCTs). Current approaches
Externí odkaz:
http://arxiv.org/abs/2206.00154