Competing risk modeling and testing for X-chromosome genetic association

2020 ◽  
Vol 151 ◽  
pp. 107007
Author(s):  
Meiling Hao ◽  
Xingqiu Zhao ◽  
Wei Xu
Keyword(s):  
Genetic Association ◽  
X Chromosome ◽  
Competing Risk ◽  
Risk Modeling

scholarly journals Predictors of all-cause and cardiovascular-specific mortality in type 2 diabetes: A competing risk modeling of an Iranian population

2016 ◽  
Vol 5 (1) ◽  
pp. 82 ◽  
Author(s):  
Marjan Mansourian ◽  
Sahar Sadeghpour ◽  
Elham Faghihimani ◽  
Akbar Hassanzadeh ◽  
Masoud Amini
Keyword(s):  
Type 2 Diabetes ◽  
Iranian Population ◽  
Competing Risk ◽  
Risk Modeling ◽  
Specific Mortality

X-chromosome genetic association test incorporating X-chromosome inactivation and imprinting effects

2019 ◽  
Vol 98 (4) ◽  
Author(s):  
Wei Liu ◽  
Bei-Qi Wang ◽  
Guojun Liu-Fu ◽  
Wing Kam Fung ◽  
Ji-Yuan Zhou
Keyword(s):  
Genetic Association ◽  
X Chromosome ◽  
X Chromosome Inactivation ◽  
Association Test ◽  
Chromosome Inactivation ◽  
Genetic Association Test

scholarly journals Introduction to Competing Risk Model in the Epidemiological Research

2018 ◽  
Vol 5 (3) ◽  
pp. 98-102
Author(s):  
Abbas Alipour ◽  
Abolghasem Shokri ◽  
Fatemeh Yasari ◽  
Soheila Khodakarim
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Risk Model ◽  
Cox Model ◽  
Premature Death ◽  
General Purpose ◽  
Competing Risk ◽  
Risk Modeling ◽  
Competing Risk Model ◽  
Hazards Model

Background and aims: Chronic kidney disease (CKD) is a public health challenge worldwide, with adverse consequences of kidney failure, cardiovascular disease (CVD), and premature death. The CKD leads to the end-stage of renal disease (ESRD) if late/not diagnosed. Competing risk modeling is a major issue in epidemiology research. In epidemiological study, sometimes, inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In these situations, competing risk analysis is preferred to other models in survival analysis studies. The purpose of this study was to describe the bias resulting from the use of standard survival analysis to estimate the survival of a patient with ESRD and to provide alternate statistical methods considering the competing risk. Methods: In this retrospective study, 359 patients referred to the hemodialysis department of Shahid Ayatollah Ashrafi Esfahani hospital in Tehran, and underwent continuous hemodialysis for at least three months. Data were collected through patient’s medical history contained in the records (during 2011-2017). To evaluate the effects of research factors on the outcome, cause-specific hazard model and competing risk models were fitted. The data were analyzed using Stata (a general-purpose statistical software package) software, version 14 and SPSS software, version 21, through descriptive and analytical statistics. Results: The median duration of follow-up was 3.12 years and mean age at ESRD diagnosis was 66.47 years old. Each year increase in age was associated with a 98% increase in hazard of death. In this study, statistical analysis based on the competing risk model showed that age, age of diagnosis, level of education (under diploma), and body mass index (BMI) were significantly associated with death (hazard ratio [HR]=0.98, P<0.001, HR=0.99, P<0.001, HR=2.66, P=0.008, and HR=0.98, P<0.020, respectively). Conclusion: In analysis of competing risk data, it was found that providing both the results of the event of interest and those of competing risks were of importance. The Cox model, which ignored the competing risks, presented the different estimates and results as compared to the proportional sub-distribution hazards model. Thus, it was revealed that in the analysis of competing risks data, the sub-distribution proportion hazards model was more appropriate than the Cox model.

scholarly journals Competing Risk Modeling: Time to Put it in Our Standard Analytical Toolbox

2019 ◽  
Vol 30 (12) ◽  
pp. 2284-2286 ◽  
Author(s):  
Liang Li ◽  
Wei Yang ◽  
Brad C. Astor ◽  
Tom Greene
Keyword(s):  
Competing Risk ◽  
Risk Modeling

scholarly journals Bayesian Estimation Using Progressively Censored Masked Data Under Asymmetric Loss Function

2015 ◽  
Vol 20 (1) ◽  
pp. 40-50
Author(s):  
S.K. Tomer ◽  
Jitendra Kumar
Keyword(s):  
Competing Risk ◽  
Loss Functions ◽  
Series System ◽  
Risk Modeling ◽  
Bayes Estimate ◽  
Lifetime Distributions ◽  
Asymmetric Loss ◽  
Relative Risks ◽  
Lindley’S Approximation ◽  
Asymmetric Loss Function

Competing risk modeling is very useful for the assessment of component characteristics in reliability studies. In this paper, we consider the competing risk modeling of progressively censored data when units under lifetest are series system of two components. Assuming the lifetime distributions of components to be exponentially distributed, we obtain Bayes estimate of parameters and components relative risks under asymmetric loss functions. Bayesian computation is done using Lindley’s approximation. A simulation study is presented for numerical illustrations.Journal of Institute of Science and Technology, 2015, 20(1): 40-50

A novel model for the X-chromosome inactivation association on survival data

2019 ◽  
Vol 29 (5) ◽  
pp. 1305-1314
Author(s):  
Dongxiao Han ◽  
Meiling Hao ◽  
Lianqiang Qu ◽  
Wei Xu
Keyword(s):  
Genetic Association ◽  
X Chromosome ◽  
Survival Data ◽  
Biological Process ◽  
Random Effect ◽  
X Chromosome Inactivation ◽  
Chromosome Inactivation ◽  
Model Parameters ◽  
Censored Survival Data ◽  
Novel Model

The X-linked genetic association is overlooked in most of the genetic studies because of the complexity of X-chromosome inactivation process. In fact, the biological process of the gene at the same locus can vary across different subjects. Besides, the skewness of X-chromosome inactivation is inherently subject-specific (even tissue-specific within subjects) and cannot be accurately represented by a population-level parameter. To tackle this issue, a new model is proposed to incorporate the X-linked genetic association into right-censored survival data. The novel model can present that the X-linked genes on different subjects may go through different biological processes via a mixed distribution. Further, a random effect is employed to describe the uncertainty of the biological process for every subject. The proposed method can derive the probability for the escape of X-chromosome inactivation and derive the unbiased estimates of the model parameters. The Legendre–Gauss Quadrature method is used to approximate the integration over the random effect. Finite sample performance of this method is examined via extensive simulation studies. An application is illustrated with the implementation on a cancer genetic study with right-censored survival data.

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