Identification of longitudinal biomarkers for survival by a score test derived from a joint model of longitudinal and competing risks data

2014 ◽  
Vol 41 (10) ◽  
pp. 2270-2281 ◽  
Author(s):  
Feng-Shou Ko
Keyword(s):  
Competing Risks ◽  
Score Test ◽  
Joint Model ◽  
Competing Risks 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.

On correlation rank screening for ultra-high dimensional competing risks data

2021 ◽  
pp. 1-17
Author(s):  
Xiaolin Chen ◽  
Chenguang Li ◽  
Tao Zhang ◽  
Zhenlong Gao
Keyword(s):  
Competing Risks ◽  
High Dimensional ◽  
Competing Risks Data

scholarly journals Modeling semi‐competing risks data as a longitudinal bivariate process

Biometrics ◽  
2021 ◽  
Author(s):  
Daniel Nevo ◽  
Deborah Blacker ◽  
Eric B. Larson ◽  
Sebastien Haneuse
Keyword(s):  
Competing Risks ◽  
Competing Risks Data

scholarly journals Competing risks joint models using R-INLA

2021 ◽  
Vol 21 (1-2) ◽  
pp. 56-71
Author(s):  
Janet van Niekerk ◽  
Haakon Bakka ◽  
Håvard Rue
Keyword(s):  
Competing Risks ◽  
Count Data ◽  
Joint Model ◽  
Point Of View ◽  
Spatial Structures ◽  
Joint Models ◽  
Hazard Functions ◽  
Longitudinal Count Data ◽  
Non Gaussian ◽  
Made In

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.

scholarly journals Nonparametric estimation with recurrent competing risks data

2013 ◽  
Vol 20 (4) ◽  
pp. 514-537 ◽  
Author(s):  
Laura L. Taylor ◽  
Edsel A. Peña
Keyword(s):  
Competing Risks ◽  
Nonparametric Estimation ◽  
Competing Risks Data

A Practical Guide to Family Studies with Lifetime Data

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas H. Scheike ◽  
Klaus Kähler Holst
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Age Of Onset ◽  
Familial Aggregation ◽  
Threshold Model ◽  
Right Censoring ◽  
Annual Review ◽  
Publication Date ◽  
Lifetime Data ◽  
Competing Risks Data

Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death—that is, competing risks—and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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