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pro vyhledávání: '"Chang, Chung Chou H."'
In recent years, precision treatment strategy have gained significant attention in medical research, particularly for patient care. We propose a novel framework for estimating conditional average treatment effects (CATE) in time-to-event data with co
Externí odkaz:
http://arxiv.org/abs/2407.18389
Identifying causal treatment (or exposure) effects in observational studies requires the data to satisfy the unconfoundedness assumption which is not testable using the observed data. With sensitivity analysis, one can determine how the conclusions m
Externí odkaz:
http://arxiv.org/abs/2301.12396
Bayesian response adaptive randomization design with a composite endpoint of mortality and morbidity
Autor:
Xu, Zhongying, Bandos, Andriy I., Ma, Tianzhou, Tang, Lu, Talisa, Victor B., Chang, Chung-Chou H.
Allocating patients to treatment arms during a trial based on the observed responses accumulated prior to the decision point, and sequential adaptation of this allocation,, could minimize the expected number of failures or maximize total benefit to p
Externí odkaz:
http://arxiv.org/abs/2208.08472
Autor:
Andersen, Sarah K.1,2, Chang, Chung-Chou H.3,4, Arnold, Robert M.5,6, Pidro, Caroline3, Darby, Joseph M.7, Angus, Derek C.2, White, Douglas B.1 douglas.white@pitt.edu
Publikováno v:
Annals of Intensive Care. 7/2/2024, Vol. 14 Issue 1, p1-9. 9p.
Accurately estimating personalized treatment effects within a study site (e.g., a hospital) has been challenging due to limited sample size. Furthermore, privacy considerations and lack of resources prevent a site from leveraging subject-level data f
Externí odkaz:
http://arxiv.org/abs/2103.06261
Akademický článek
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Finite mixture model is an important branch of clustering methods and can be applied on data sets with mixed types of variables. However, challenges exist in its applications. First, it typically relies on the EM algorithm which could be sensitive to
Externí odkaz:
http://arxiv.org/abs/1905.03680
Publikováno v:
Journal of Data Science 19(2021)15-36
Clustering is an essential technique for discovering patterns in data. The steady increase in amount and complexity of data over the years led to improvements and development of new clustering algorithms. However, algorithms that can cluster data wit
Externí odkaz:
http://arxiv.org/abs/1905.02257
An individualized risk prediction model that dynamically updates the probability of a clinical event from a specific cause is valuable for physicians to be able to optimize personalized treatment strategies in real-time by incorporating all available
Externí odkaz:
http://arxiv.org/abs/1904.09002
Autor:
Gellad, Walid F., Yang, Qingnan, Adamson, Kayleigh M., Kuza, Courtney C., Buchanich, Jeanine M., Bolton, Ashley L., Murzynski, Stanley M., Goetz, Carrie Thomas, Washington, Terri, Lann, Michael F., Chang, Chung-Chou H., Suda, Katie J., Tang, Lu
Publikováno v:
In Drug and Alcohol Dependence 1 May 2023 246