scholarly journals Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks

Biometrics ◽  
2014 ◽  
Vol 71 (1) ◽  
pp. 102-113 ◽  
Author(s):  
Paul Blanche ◽  
Cécile Proust-Lima ◽  
Lucie Loubère ◽  
Claudine Berr ◽  
Jean-François Dartigues ◽  
...  
Keyword(s):  
Competing Risks ◽  
Predictive Accuracy ◽  
Time To Event ◽  
Joint Models

scholarly journals Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

2011 ◽  
Vol 53 (1) ◽  
pp. 88-112 ◽  
Author(s):  
Rotraut Schoop ◽  
Jan Beyersmann ◽  
Martin Schumacher ◽  
Harald Binder
Keyword(s):  
Competing Risks ◽  
Predictive Accuracy ◽  
Time To Event ◽  
Event Models

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 Modeling potential time to event data with competing risks

2013 ◽  
Vol 20 (2) ◽  
pp. 316-334 ◽  
Author(s):  
Liang Li ◽  
Bo Hu ◽  
Michael W. Kattan
Keyword(s):  
Competing Risks ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

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.

scholarly journals The Effect of MSM and CD4+ Count on the Development of Cancer AIDS (AIDS-defining Cancer) and Non-cancer AIDS in the HAART Era

2019 ◽  
Vol 16 (4) ◽  
pp. 288-296
Author(s):  
Prosanta Mondal ◽  
Hyun J. Lim ◽  
OHTN Cohort Study Team
Keyword(s):  
Competing Risks ◽  
Joint Modeling ◽  
Study Data ◽  
Hiv Treatment ◽  
Cd4 Count ◽  
Survival Outcomes ◽  
Risk Category ◽  
Joint Models ◽  
Kaplan Meier ◽  
Sex With Men

Background: The HIV epidemic is increasing among Men who have Sex with Men (MSM) and the risk for AIDS defining cancer (ADC) is higher among them. Objective: To examine the effect of MSM and CD4+ count on time to cancer AIDS (ADC) and noncancer AIDS in competing risks setting in the HAART era. Method: Using Ontario HIV Treatment Network Cohort Study data, HIV-positive adults diagnosed between January 1997 and October 2012 having baseline CD4+ counts ≤ 500 cells/mm3 were evaluated. Two survival outcomes, cancer AIDS and non-cancer AIDS, were treated as competing risks. Kaplan-Meier analysis, Cox cause-specific hazards (CSH) model and joint modeling of longitudinal and survival outcomes were used. Results: Among the 822 participants, 657 (79.9%) were males; 686 (83.5%) received anti-retroviral (ARV) ever. Regarding risk category, the majority (58.5%) were men who have Sex with men (MSM). Mean age was 37.4 years (SD = 10.3). In the multivariate Cox CSH models, MSM were not associated with cancer AIDS but with non-cancer AIDS [HR = 2.92; P = 0.055, HR = 0.54; P = 0.0009, respectively]. However, in joint models of longitudinal and survival outcomes, MSM were associated with cancer AIDS but not with non-cancer AIDS [HR = 3.86; P = 0.013, HR = 0.73; P = 0.10]. CD4+ count, age, ARV ever were associated with both events in the joint models. Conclusion: This study demonstrates the importance of considering competing risks, and timedependent biomarker in the survival model. MSM have higher hazard for cancer AIDS. CD4+ count is associated with both survival outcomes.

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