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Survival Analysis
In logistic regression, we are interested in how risk factors are associated with the presence or absence of an event. Sometimes, however, we are...
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Survival Analysis
The term “survival analysis” comprises a collection of longitudinal analysis methods for studying time-to-event data. Here, the term “time”...
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Survival Analysis
Survival analysis is commonly used in the medical literature. Sections 4.1–4.4 cover basic concepts in survival analysis commonly used in the medical...
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Phylogenetic Survival Analysis
Going back in time through a phylogenetic tree makes it possible to evaluate ancestral genomes and assess their potential to acquire key...
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A Survival Analysis Guide in Oncology
Survival analysis has a crucial role in oncology. These statistical methods are ubiquitous in oncology, hel** physicians determine the death risk,...
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Survival Analysis and Applications Using SAS and SPSS
Survival analysis is used to analyze data in which the time until the event is of interest. The event is sometimes, but not always, death. Typically,...
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Time-Based Survival Analysis for Breast Cancer
The aim of this study is to predict the survival rates of breast cancer patients using the Cox regression and Kaplan–Meier model implementation and...
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Nonparametric Survival Analysis
Survival or time-to-event data are ubiquitous in clinical trials research. The presence of censoring requires specialized methods for the analysis of...
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Survival Analysis
Clinical studies play a key role in the continuous development of the treatment of diseases to improve the survival of patients. Thus, a solid...
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Survival Analysis II
Survival analysis modeling is integral to clinical trial analysis, as even in well-designed randomized trials where the primary inference is to be...
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Clinical Factors to Investigate Survival Analysis in Cardiovascular Patients
Cardiovascular diseases are one of the leading causes of mortality worldwide; however, the management of patients with this pathology within hospital...
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DeFi Survival Analysis: Insights into Risks and User Behaviors
We propose a decentralized finance (DeFi) survival analysis approach for discovering and characterizing user behavior and risks in lending protocols....
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Conditional survival analysis and dynamic survival prediction for intracranial solitary-fibrous tumor/hemangiopericytoma
BackgroundAs the form of World Health Organization Central Nervous System (WHO CNS) tumor classifications is updated, there is a lack of research on...
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Dynamical Survival Analysis for Epidemic Modeling
This chapter describes the dynamical survival analysis (DSA) method for modeling infectious diseases. This method provides a powerful framework for...
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Cirrhosis Patient Survival Prediction Analysis Using ML Algorithms
This research aims to produce accurate and clinically applicable pre dictions of life expectancy for cirrhosis patients, leveraging sophisticated...
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Analysis of Survival Times
This chapter is dedicated to “survival analysis”, which also encompasses the statistical characterization of material failures and machine...
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Deep Survival Analysis in Multiple Sclerosis
Multiple Sclerosis (MS) is the most frequent non-traumatic debilitating neurological disease. It is usually diagnosed based on clinical observations...
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Survival Prediction After Transarterial Chemoembolization for Hepatocellular Carcinoma: a Deep Multitask Survival Analysis Approach
The accurate prediction of postoperative survival time of patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC)...
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Modern Survival Analysis in Clinical Research Cox Regressions Versus Accelerated Failure Time Models
An important novel menu for Survival Analysis entitled Accelerated Failure Time (AFT) models has been published by IBM (international Businesss...
