scholarly journals An approach to joint analysis of longitudinal measurements and competing risks failure time data

2007 ◽  
Vol 26 (14) ◽  
pp. 2813-2835 ◽  
Author(s):  
Robert M. Elashoff ◽  
Gang Li ◽  
Ning Li
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Joint Analysis ◽  
Failure Time Data ◽  
Time Data ◽  
Longitudinal Measurements

Likelihood‐based inference for a frailty‐copula model based on competing risks failure time data

2020 ◽  
Vol 36 (5) ◽  
pp. 1622-1638 ◽  
Author(s):  
Yin‐Chen Wang ◽  
Takeshi Emura ◽  
Tsai‐Hung Fan ◽  
Simon M.S. Lo ◽  
Ralf Andreas Wilke
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Failure Time Data ◽  
Copula Model ◽  
Time Data ◽  
Model Based

An issue of identifying longitudinal biomarkers for competing risks data with masked causes of failure considering frailties model

2019 ◽  
Vol 29 (2) ◽  
pp. 603-616
Author(s):  
Feng-shou Ko
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Score Test ◽  
Joint Modeling ◽  
Repeated Measurements ◽  
Failure Time Data ◽  
Time Data ◽  
Hazard Functions ◽  
Competing Risks Data ◽  
Causes Of Failure

In this paper, we consider joint modeling of repeated measurements and competing risks failure time data to allow for more than one distinct failure type in the survival endpoint. Hence, we can fit a cause-specific hazards submodel to allow for competing risks, with a separate latent association between longitudinal measurements and each cause of failure. We also consider the possible masked causes of failure in joint modeling of repeated measurements and competing risks failure time data. We also derive a score test to identify longitudinal biomarkers or surrogates for a time-to-event outcome in competing risks data which contain masked causes of failure. With a carefully chosen definition of complete data, the maximum likelihood estimation of the cause-specific hazard functions and of the masking probabilities is performed via an expectation maximization algorithm. The simulations are used to explore how the number of individuals, the number of time points per individual, and the functional form of the random effects from the longitudinal biomarkers considering heterogeneous baseline hazards in individuals influence the power to detect the association of a longitudinal biomarker and the survival time.

scholarly journals Simultaneous inference of a misclassified outcome and competing risks failure time data

2015 ◽  
Vol 42 (5) ◽  
pp. 1080-1090 ◽  
Author(s):  
Sheng Luo ◽  
Xiao Su ◽  
Min Yi ◽  
Kelly K. Hunt
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Failure Time Data ◽  
Simultaneous Inference ◽  
Time Data
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