scholarly journals Siamese Survival Analysis with Competing Risks

2018 ◽  
pp. 260-269
Author(s):  
Anton Nemchenko ◽  
Trent Kyono ◽  
Mihaela Van Der Schaar
Keyword(s):  
Survival Analysis ◽  
Competing Risks

scholarly journals Incidence of cardiovascular events and associated risk factors in kidney transplant patients: a competing risks survival analysis

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
María Teresa Seoane-Pillado ◽  
Salvador Pita-Fernández ◽  
Francisco Valdés-Cañedo ◽  
Rocio Seijo-Bestilleiro ◽  
Sonia Pértega-Díaz ◽  
...  
Keyword(s):  
Risk Factors ◽  
Survival Analysis ◽  
Kidney Transplant ◽  
Competing Risks ◽  
Cardiovascular Events ◽  
Transplant Patients ◽  
Kidney Transplant Patients ◽  
Associated Risk Factors

Survival analysis

2020 ◽  
pp. 181-218
Author(s):  
Bendix Carstensen
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Cox Model ◽  
Survival Function ◽  
Event Analysis ◽  
Time To Event ◽  
Poisson Models ◽  
Kaplan Meier ◽  
Analysis Survival ◽  
Event Rates

This chapter describes survival analysis. Survival analysis concerns data where the outcome is a length of time, namely the time from inclusion in the study (such as diagnosis of some disease) till death or some other event — hence the term 'time to event analysis', which is also used. There are two primary targets normally addressed in survival analysis: survival probabilities and event rates. The chapter then looks at the life table estimator of survival function and the Kaplan–Meier estimator of survival. It also considers the Cox model and its relationship with Poisson models, as well as the Fine–Gray approach to competing risks.

Bounds on the covariate-time transformation for competing-risks survival analysis

2006 ◽  
Vol 12 (3) ◽  
pp. 285-303 ◽  
Author(s):  
Simon J. Bond ◽  
J. Ewart H. Shaw
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Time Transformation

A global test for competing risks survival analysis

2020 ◽  
Vol 29 (12) ◽  
pp. 3666-3683
Author(s):  
Dominic Edelmann ◽  
Maral Saadati ◽  
Hein Putter ◽  
Jelle Goeman
Keyword(s):  
Survival Analysis ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Competing Risks ◽  
Cox Model ◽  
Wald Test ◽  
Ratio Test ◽  
Type I ◽  
Multistate Models ◽  
Global Test

Standard tests for the Cox model, such as the likelihood ratio test or the Wald test, do not perform well in situations, where the number of covariates is substantially higher than the number of observed events. This issue is perpetuated in competing risks settings, where the number of observed occurrences for each event type is usually rather small. Yet, appropriate testing methodology for competing risks survival analysis with few events per variable is missing. In this article, we show how to extend the global test for survival by Goeman et al. to competing risks and multistate models[Per journal style, abstracts should not have reference citations. Therefore, can you kindly delete this reference citation.]. Conducting detailed simulation studies, we show that both for type I error control and for power, the novel test outperforms the likelihood ratio test and the Wald test based on the cause-specific hazards model in settings where the number of events is small compared to the number of covariates. The benefit of the global tests for competing risks survival analysis and multistate models is further demonstrated in real data examples of cancer patients from the European Society for Blood and Marrow Transplantation.

scholarly journals Survival analysis in the presence of competing risks

2017 ◽  
Vol 5 (3) ◽  
pp. 47-47 ◽  
Author(s):  
Zhongheng Zhang
Keyword(s):  
Survival Analysis ◽  
Competing Risks

scholarly journals Competing Risks Models for an Enterprises Duration on the Market

2020 ◽  
Vol 20 (1) ◽  
pp. 456-473
Author(s):  
Dominika M. Urbańczyk
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Waiting Time ◽  
Research Topic ◽  
Business Activity ◽  
Full Understanding ◽  
Kaplan Meier ◽  
Duration Distribution ◽  
The Impact ◽  
Valuable Result

AbstractResearch background: Enterprises are an important element of the economy, which explains that the analysis of their duration on the market is an important and willingly undertaken research topic. In the case of complex problems like this, considering only one type of event, which ends the duration, is often insufficient for full understanding.Purpose: In this paper there is an analysis of the duration of enterprises on the market, taking into account various reasons for the termination of their business activity as well as their characteristics.Research methodology: A survival analysis can be used to study duration on the market. However, the possibility of considering the waiting time for only one type of event is its important limitation. One solution is to use competing risks. Various competing risks models (naive Kaplan-Meier estimator, subdistribution model, subhazard and cause-specific hazard) are presented and compared with an indication of their advantages and weakness.Results: The competing risks models are estimated to investigate the impact of the causes of an enterprises liquidation on duration distribution. The greatest risk concerns enterprises with a natural person as the owner (regardless of the reason of failure). For each of the competing risks, it is also indicated that there is a section of activity which adversely affects the ability of firms to survive on the market.Novelty: A valuable result is considering the reasons for activity termination in the duration analysis for enterprises from the Mazowieckie Voivodeship.

scholarly journals Nomogram for survival analysis in the presence of competing risks

2017 ◽  
Vol 5 (20) ◽  
pp. 403-403 ◽  
Author(s):  
Zhongheng Zhang ◽  
Ronald B. Geskus ◽  
Michael W. Kattan ◽  
Haoyang Zhang ◽  
Tongyu Liu
Keyword(s):  
Survival Analysis ◽  
Competing Risks
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