scholarly journals Variable selection in subdistribution hazard frailty models with competing risks data

2014 ◽  
Vol 33 (26) ◽  
pp. 4590-4604 ◽  
Author(s):  
Il Do Ha ◽  
Minjung Lee ◽  
Seungyoung Oh ◽  
Jong-Hyeon Jeong ◽  
Richard Sylvester ◽  
...  
Keyword(s):  
Variable Selection ◽  
Competing Risks ◽  
Frailty Models ◽  
Subdistribution Hazard ◽  
Competing Risks Data

scholarly journals Regression Analysis of Masked Competing Risks Data under Cumulative Incidence Function Framework

2020 ◽  
Vol 49 (3) ◽  
pp. 25-29
Author(s):  
Yosra Yousif ◽  
Faiz Ahmed Mohamed Elfaki ◽  
Meftah Hrairi
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Model Simulation ◽  
Cumulative Incidence Function ◽  
Subdistribution Hazard ◽  
Incidence Function ◽  
Proposed Model ◽  
Competing Risks Data ◽  
Cause Of Failure ◽  
Proportional Subdistribution Hazard

In the studies that involve competing risks, somehow, masking issues might arise. That is, the cause of failure for some subjects is only known as a subset of possible causes. In this study, a Bayesian analysis is developed to assess the effect of risks factor on the Cumulative Incidence Function (CIF) by adopting the proportional subdistribution hazard model. Simulation is conducted to evaluate the performance of the proposed model and it shows that the model is feasible for the possible applications.

G-estimation of structural nested mean models for competing risks data using pseudo-observations

Biostatistics ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 860-875 ◽  
Author(s):  
Shiro Tanaka ◽  
M Alan Brookhart ◽  
Jason P Fine
Keyword(s):  
Type 2 Diabetes ◽  
Competing Risks ◽  
Causal Effects ◽  
Time Varying ◽  
Finite Sample ◽  
Subdistribution Hazard ◽  
Hazard Ratios ◽  
Competing Risks Data ◽  
Risk Ratios

Summary This article provides methods of causal inference for competing risks data. The methods are formulated as structural nested mean models of causal effects directly related to the cumulative incidence function or subdistribution hazard, which reflect the survival experience of a subject in the presence of competing risks. The effect measures include causal risk differences, causal risk ratios, causal subdistribution hazard ratios, and causal effects of time-varying exposures. Inference is implemented by g-estimation using pseudo-observations, a technique to handle censoring. The finite-sample performance of the proposed estimators in simulated datasets and application to time-varying exposures in a cohort study of type 2 diabetes are also presented.

scholarly journals Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties

2016 ◽  
Vol 25 (6) ◽  
pp. 2488-2505 ◽  
Author(s):  
Il Do Ha ◽  
Nicholas J Christian ◽  
Jong-Hyeon Jeong ◽  
Junwoo Park ◽  
Youngjo Lee
Keyword(s):  
Clinical Trials ◽  
Competing Risks ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Likelihood Function ◽  
Profile Likelihood ◽  
Partial Likelihood ◽  
Likelihood Method ◽  
Subdistribution Hazard ◽  
Competing Risks Data

Competing risks data often exist within a center in multi-center randomized clinical trials where the treatment effects or baseline risks may vary among centers. In this paper, we propose a subdistribution hazard regression model with multivariate frailty to investigate heterogeneity in treatment effects among centers from multi-center clinical trials. For inference, we develop a hierarchical likelihood (or h-likelihood) method, which obviates the need for an intractable integration over the frailty terms. We show that the profile likelihood function derived from the h-likelihood is identical to the partial likelihood, and hence it can be extended to the weighted partial likelihood for the subdistribution hazard frailty models. The proposed method is illustrated with a dataset from a multi-center clinical trial on breast cancer as well as with a simulation study. We also demonstrate how to present heterogeneity in treatment effects among centers by using a confidence interval for the frailty for each individual center and how to perform a statistical test for such heterogeneity using a restricted h-likelihood.

scholarly journals Quantifying time-varying cause-specific hazard and subdistribution hazard ratios with competing risks data

2019 ◽  
Vol 16 (4) ◽  
pp. 363-374
Author(s):  
Guoqing Diao ◽  
Joseph G Ibrahim
Keyword(s):  
Competing Risks ◽  
Weight Functions ◽  
Time Varying ◽  
Proportional Hazard ◽  
Test Statistics ◽  
Test Statistic ◽  
Subdistribution Hazard ◽  
New Methods ◽  
Hazard Ratios ◽  
Competing Risks Data

Various non-proportional hazard models have been developed in the literature for competing risks data. The regression coefficients under these models, however, typically cannot be compared directly. We propose new methods to quantify the average of the time-varying cause-specific hazard ratios and subdistribution hazard ratios through two general classes of transformations and weight functions that are chosen to reflect the relative importance of the hazard ratios in different time periods. We further propose an [Formula: see text] -norm type of test statistic that incorporates the test statistics for all possible pairs of the transformation function and weight function under consideration. Extensive simulations are conducted under various settings of the hazards and demonstrate that the proposed test performs well under all settings. An application to a clinical trial in follicular lymphoma is examined in detail.

scholarly journals Penalized Variable Selection for Multi-center Competing Risks Data

2016 ◽  
Vol 9 (2) ◽  
pp. 379-405 ◽  
Author(s):  
Zhixuan Fu ◽  
Shuangge Ma ◽  
Haiqun Lin ◽  
Chirag R. Parikh ◽  
Bingqing Zhou
Keyword(s):  
Variable Selection ◽  
Competing Risks ◽  
Competing Risks Data ◽  
Selection For

scholarly journals Nonparametric competing risks analysis using Bayesian Additive Regression Trees

2019 ◽  
Vol 29 (1) ◽  
pp. 57-77 ◽  
Author(s):  
Rodney Sparapani ◽  
Brent R Logan ◽  
Robert E McCulloch ◽  
Purushottam W Laud
Keyword(s):  
Competing Risks ◽  
Regression Trees ◽  
Dimensional Parameter ◽  
Subdistribution Hazard ◽  
Cox Models ◽  
Hematopoietic Stem ◽  
Simulation Performance ◽  
Competing Risks Data ◽  
Additive Regression ◽  
Bayesian Additive Regression Trees

Many time-to-event studies are complicated by the presence of competing risks. Such data are often analyzed using Cox models for the cause-specific hazard function or Fine and Gray models for the subdistribution hazard. In practice, regression relationships in competing risks data are often complex and may include nonlinear functions of covariates, interactions, high-dimensional parameter spaces and nonproportional cause-specific, or subdistribution, hazards. Model misspecification can lead to poor predictive performance. To address these issues, we propose a novel approach: flexible prediction modeling of competing risks data using Bayesian Additive Regression Trees (BART). We study the simulation performance in two-sample scenarios as well as a complex regression setting, and benchmark its performance against standard regression techniques as well as random survival forests. We illustrate the use of the proposed method on a recently published study of patients undergoing hematopoietic stem cell transplantation.

Linear and nonlinear variable selection in competing risks data

2018 ◽  
Vol 37 (13) ◽  
pp. 2134-2147
Author(s):  
Xiaowei Ren ◽  
Shanshan Li ◽  
Changyu Shen ◽  
Zhangsheng Yu
Keyword(s):  
Variable Selection ◽  
Competing Risks ◽  
Competing Risks Data ◽  
Linear And Nonlinear
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