A nonparametric method for assessment of interactions in a median regression model for analyzing right censored data

2018 ◽  
Vol 28 (4) ◽  
pp. 1170-1187
Author(s):  
MinJae Lee ◽  
Mohammad H Rahbar ◽  
Hooshang Talebi
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Survival Data ◽  
Real Life ◽  
Simulation Studies ◽  
Median Regression ◽  
Right Censored Data ◽  
Censored Quantile Regression ◽  
Quantile Regression Model ◽  
The Impact

We propose a nonparametric test for interactions when we are concerned with investigation of the simultaneous effects of two or more factors in a median regression model with right censored survival data. Our approach is developed to detect interaction in special situations, when the covariates have a finite number of levels with a limited number of observations in each level, and it allows varying levels of variance and censorship at different levels of the covariates. Through simulation studies, we compare the power of detecting an interaction between the study group variable and a covariate using our proposed procedure with that of the Cox Proportional Hazard (PH) model and censored quantile regression model. We also assess the impact of censoring rate and type on the standard error of the estimators of parameters. Finally, we illustrate application of our proposed method to real life data from Prospective Observational Multicenter Major Trauma Transfusion (PROMMTT) study to test an interaction effect between type of injury and study sites using median time for a trauma patient to receive three units of red blood cells. The results from simulation studies indicate that our procedure performs better than both Cox PH model and censored quantile regression model based on statistical power for detecting the interaction, especially when the number of observations is small. It is also relatively less sensitive to censoring rates or even the presence of conditionally independent censoring that is conditional on the levels of covariates.

A censored quantile regression approach for the analysis of time to event data

2016 ◽  
Vol 27 (3) ◽  
pp. 955-965 ◽  
Author(s):  
Xiaonan Xue ◽  
Xianhong Xie ◽  
Howard D Strickler
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Time To Event ◽  
Censored Quantile Regression ◽  
Quantile Regression Model ◽  
Hazards Model

The commonly used statistical model for studying time to event data, the Cox proportional hazards model, is limited by the assumption of a constant hazard ratio over time (i.e., proportionality), and the fact that it models the hazard rate rather than the survival time directly. The censored quantile regression model, defined on the quantiles of time to event, provides an alternative that is more flexible and interpretable. However, the censored quantile regression model has not been widely adopted in clinical research, due to the complexity involved in interpreting its results properly and consequently the difficulty to appreciate its advantages over the Cox proportional hazards model, as well as the absence of adequate validation procedure. In this paper, we addressed these limitations by (1) using both simulated examples and data from National Wilms’ Tumor clinical trials to illustrate proper interpretation of the censored quantile regression model and the differences and the advantages of the model compared to the Cox proportional hazards model; and (2) developing a validation procedure for the predictive censored quantile regression model. The performance of this procedure was examined using simulation studies. Overall, we recommend the use of censored quantile regression model, which permits a more sensitive analysis of time to event data together with the Cox proportional hazards model.

scholarly journals ON THE IMPACT OF REAL ESTATE PRICES ON THE DEVELOPMENT OF REGIONAL ECONOMY IN CHINA–AN ESTIMATION BASED ON PANEL QUANTILE REGRESSION MODEL

2021 ◽  
Vol 5 (2) ◽  
pp. 51-54
Author(s):  
Baili Zhang ◽  
Yadong Ma ◽  
Mengyue Yin ◽  
Zhengxun Li
Keyword(s):  
Economic Development ◽  
Real Estate ◽  
Quantile Regression ◽  
Regression Model ◽  
Marginal Effect ◽  
Quantile Regression Model ◽  
Panel Quantile Regression ◽  
Real Estate Prices ◽  
Impact Of Urbanization ◽  
The Impact

The paper analyzes the mechanism of real estate prices on economic development with panel quantile regression model. It is found that real estate prices can significantly promote economic development. Generally speaking, the contribution of real estate prices to economic development in regions with higher level of economic development is higher than that in regions with lower level. With the continuous improvement of the quantile, the impact of real estate prices has generally increased gradually, and the impact of urbanization level basically shows the law of diminishing marginal effect.

scholarly journals What Affects The Ability To Accumulate The Best Applicants By Russian Universities? The Application Of Quantile Regression Model

2017 ◽  
Vol 19 (5) ◽  
pp. 81-98
Author(s):  
Edyta Łaszkiewicz ◽  
Stepan Zemstov ◽  
Vera Barinova
Keyword(s):  
Higher Education ◽  
Quantile Regression ◽  
Regression Model ◽  
Higher Education Institutions ◽  
Research Quality ◽  
Related Factors ◽  
Public And Private ◽  
Quantile Regression Model ◽  
The Impact ◽  
Russian Universities

The aim of this paper is to evaluate which university’s characteristics have the greatest impact on the competitiveness of universities in their ability to attract better students in Russia. We examined the impact of three groups of factors,related to teaching, research and entrepreneurial activities of universities. The quantile regression model was applied for the subsample of public and private higher education institutions localized in Russia. The results prove that not only traditional, teaching-related factors affect the attractiveness of the universities. We found that the research quality and entrepreneurial experience both increase the ability to accumulate the best applicants by Russian universities. However, the synergy between training, research and business activities is not always achieved. The importance of science and business-oriented activities varies between public and private institutions. According to the results from the quantile regression the importance of the certain factors differs between the quantiles of the dependent variable distribution. Our findings might be useful for the governmental authorities during the universities’ assessment as well as for the higher education institutions themselves – in order to define their strategic development and attract better students.

scholarly journals THE IMPACT OF CORONAVIRUS PANDEMIC ON AIRLINE STOCKS: AN APPLICATION OF QUANTILE-ON-QUANTILE REGRESSION MODEL

2020 ◽  
Vol 19 (COVID-19 Special Issue) ◽  
pp. 429-446
Author(s):  
Buğra BAĞCI ◽  
Ferhat ÇITAK ◽  
Muhammet Yunus ŞİŞMAN
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Quantile Regression Model ◽  
The Impact

scholarly journals On the Arcsecant Hyperbolic Normal Distribution. Properties, Quantile Regression Modeling and Applications

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 117 ◽  
Author(s):  
Mustafa Ç. Korkmaz ◽  
Christophe Chesneau ◽  
Zehra Sedef Korkmaz
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Normal Distribution ◽  
Order Statistics ◽  
Random Variable ◽  
Unit Interval ◽  
Parametric Estimation ◽  
Simulation Studies ◽  
Quantile Regression Model ◽  
New Distribution

This work proposes a new distribution defined on the unit interval. It is obtained by a novel transformation of a normal random variable involving the hyperbolic secant function and its inverse. The use of such a function in distribution theory has not received much attention in the literature, and may be of interest for theoretical and practical purposes. Basic statistical properties of the newly defined distribution are derived, including moments, skewness, kurtosis and order statistics. For the related model, the parametric estimation is examined through different methods. We assess the performance of the obtained estimates by two complementary simulation studies. Also, the quantile regression model based on the proposed distribution is introduced. Applications to three real datasets show that the proposed models are quite competitive in comparison to well-established models.

Quantile regression models for survival data with missing censoring indicators

2021 ◽  
pp. 096228022199598
Author(s):  
Zhiping Qiu ◽  
Huijuan Ma ◽  
Jianwei Chen ◽  
Gregg E Dinse
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Survival Data ◽  
Asymptotic Properties ◽  
Estimating Equations ◽  
Real Data ◽  
Finite Sample ◽  
Dynamic Relationship ◽  
Quantile Regression Model ◽  
Missing Censoring Indicators

The quantile regression model has increasingly become a useful approach for analyzing survival data due to its easy interpretation and flexibility in exploring the dynamic relationship between a time-to-event outcome and the covariates. In this paper, we consider the quantile regression model for survival data with missing censoring indicators. Based on the augmented inverse probability weighting technique, two weighted estimating equations are developed and corresponding easily implemented algorithms are suggested to solve the estimating equations. Asymptotic properties of the resultant estimators and the resampling-based inference procedures are established. Finally, the finite sample performances of the proposed approaches are investigated in simulation studies and a real data application.

Sign in / Sign up

Export Citation Format

Share Document