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Deep Learning-Based Survival Analysis for High-Dimensional Survival Data
Mathematics, 2021 With the development of high-throughput technologies, more and more high-dimensional or ultra-high-dimensional genomic data are being generated. Therefore, effectively analyzing such data has become a significant challenge.
Lin Hao +3 more
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Multiscale Bayesian survival analysis [PDF]
The Annals of Statistics, 2021 We consider Bayesian nonparametric inference in the right-censoring survival model, where modeling is made at the level of the hazard rate. We derive posterior limiting distributions for linear functionals of the hazard, and then for `many' functionals simultaneously in appropriate multiscale spaces.
Castillo, Ismaël +1 more
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Journal of Infection in Developing Countries, 2022 Introduction: Coronavirus disease 2019 (COVID-19) has caused an outbreak around the world. Early detection of severe illness is crucial for patients’ survival.
Yun Gou +12 more
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Survival analysis of spinal muscular atrophy type I [PDF]
Korean Journal of Pediatrics, 2010 PurposeThe life expectancy of patients with spinal muscular atrophy (SMA) type I is generally considered to be less than 2 years. Recently, with the introduction of proactive treatments, a longer survival and an improved survival rate have been reported.
Hyun Bin Park +6 more
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Conformalized survival analysis
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2023 AbstractIn this paper, we develop an inferential method based on conformal prediction, which can wrap around any survival prediction algorithm to produce calibrated, covariate-dependent lower predictive bounds on survival times. In the Type I right-censoring setting, when the censoring times are completely exogenous, the lower predictive bounds have ...
Emmanuel Candès, Lihua Lei, Zhimei Ren
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Survival cluster analysis [PDF]
Proceedings of the ACM Conference on Health, Inference, and Learning, 2020 Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population-level phenotypic heterogeneity, resulting from (unknown) subpopulations with diverse risk profiles or survival distributions. As a result, there is an unmet need in survival
Chapfuwa, Paidamoyo +4 more
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Informed Bayesian survival analysis
BMC Medical Research Methodology, 2022 AbstractBackgroundWe provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of ...
Bartoš, F., Aust, F., Haaf, J.M.
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Survival mediation analysis with the death-truncated mediator: The completeness of the survival mediation parameter [PDF]
, 2020 In medical research, the development of mediation analysis with a survival outcome has facilitated investigation into causal mechanisms. However, studies have not discussed the death-truncation problem for mediators, the problem being that conventional ...
Avin C +4 more
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Survival Analysis of Hemodialysis Patients [PDF]
, 2016 Survival analysis as a collection of statistical procedures for analyzing the data that its outcome variable was the time to occurrence of an event. Kaplan-Meier method is a type of survival analysis technique, this method is often called the Product ...
Ardianto, E. T. (Efri) +2 more
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The role of adjuvant chemotherapy for patients with resected pancreatic cancer: Systematic review of randomized controlled trials and meta-analysis [PDF]
, 2007 Background: In patients undergoing surgery for resectable pancreatic cancer prognosis still remains poor. The role of adjuvant treatment strategies (including chemotherapy and chemoradiotherapy) following resection of pancreatic cancer remains ...
Bakkevold KE +17 more
core +1 more source