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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 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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Genetic architecture of rainbow trout survival from egg to adult [PDF]
, 2010 Survival from birth to a reproductive adult is a challenge that only robust individuals resistant to a variety of mortality factors will overcome. To assess whether survival traits share genetic architecture throughout the life cycle, we estimated ...
Kause, A. +5 more
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Advanced survival models for risk-factor analysis in scrapie [PDF]
, 2007 Because of the confounding effects of long incubation duration and flock management, accurate epidemiological studies of scrapie outbreaks are difficult to carry out.
Andréoletti, Olivier +6 more
core +4 more sources