scholarly journals Joint modeling of longitudinal, recurrent events and failure time data for survivor's population

Biometrics ◽  
2017 ◽  
Vol 73 (4) ◽  
pp. 1150-1160 ◽  
Author(s):  
Qing Cai ◽  
Mei‐Cheng Wang ◽  
Kwun Chuen Gary Chan
Keyword(s):  
Recurrent Events ◽  
Failure Time ◽  
Joint Modeling ◽  
Failure Time Data ◽  
Time Data

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.

A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

2021 ◽  
pp. 096228022110092
Author(s):  
Mingyue Du ◽  
Hui Zhao ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Significant Feature ◽  
Failure Time Data ◽  
Time Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Cox's Proportional Hazards Model

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

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