Results 51 to 60 of about 32,632 (220)
Confidence Intervals For The Survival Function [PDF]
, 2016 This manuscript, taken from Olive(2010, ch.16), suggests confidence intervals for the survival function as estimated by the Kaplan Meier estimator and the empirical survival ...
Yang, Luke H
core +1 more source
Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data [PDF]
, 2012 Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis.
Aickelin, Uwe +3 more
core +3 more sources
Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source
Epidemiology, Biostatistics and Public Health, 2022 Survival analysis is a powerful statistical tool to study failure-time data. In introductory courses students learn how to describe right-censored survival time data using the product-limit estimator of the survival function on a given end-point relying
Davide Paolo Bernasconi +1 more
doaj
Survival Analysis Using Auxiliary Variables Via Nonparametric Multiple Imputation [PDF]
, 2004 We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variable to recover information for censored observations.
Commenges, Daniel +3 more
core +1 more source
Targeted modulation of IGFL2‐AS1 reveals its translational potential in cervical adenocarcinoma
Molecular Oncology, EarlyView.Cervical adenocarcinoma patients face worse outcomes than squamous cell carcinoma counterparts despite similar treatment. The identification of IGFL2‐AS1's differential expression provides a molecular basis for distinguishing these histotypes, paving the way for personalized therapies and improved survival in vulnerable populations globally.
Ricardo Cesar Cintra +6 more
wiley +1 more source
Assessing Dominance in Survival Functions: A Test for Right-Censored Data
MathematicsThis paper proposes a new statistical test to assess the dominance of survival functions in the presence of right-censored data. Traditional methods, such as the Log-Rank test, are inadequate for determining whether one survival function consistently ...
Félix Belzunce +2 more
doaj +1 more source
Epidemiology, Biostatistics and Public Health, 2013 Survival analysis is a powerful statistical tool to study failure-time data. In introductory courses students learn how to describe right-censored survival time data using the product-limit estimator of the survival function on a given end-point relying ...
Davide Paolo Bernasconi, Laura Antolini
doaj +1 more source
Mean Survival Time from Right Censored Data [PDF]
, 2009 A nonparametric estimate of the mean survival time can be obtained as the area under the Kaplan-Meier estimate of the survival curve. A common modification is to change the largest observation to a death time if it is censored.
Hess, Kenneth R, Zhong, Ming
core +1 more source
Product-limit estimators of the gap time distribution of a renewal process under different sampling patterns [PDF]
, 2010 Nonparametric estimation of the gap time distribution in a simple renewal process may be considered a problem in survival analysis under particular sampling frames corresponding to how the renewal process is observed.
Gill, Richard D., Keiding, Niels
core +2 more sources