Quantile regression for competing risks analysis under case-cohort design

Fan, Caiyun; Ma, Huijuan; Zhou, Yong

doi:10.1080/00949655.2017.1419352

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…In the second scenario of simulation studies, we explore two efficiency improving approaches. One method is to use the weighted function (13), the other method is to use the stratified case-cohort design. Here the event time T is generated from…”

Section: Simulation Studiesmentioning

confidence: 99%

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Proportional mean residual life model with censored survival data under case-cohort design

Shi

Zhou

2019

Statistics and Its Interface

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Proportional mean residual life model is studied for analysing survival data from the case-cohort design. To simultaneously estimate the regression parameters and the baseline mean residual life function, weighted estimating equations based on an inverse selection probability are proposed. The resulting regression coefficients estimates are shown to be consistent and asymptotically normal with easily estimated variance-covariance. Simulation studies show that the proposed estimators perform very well. An application to a real dataset from the South Welsh nickel refiners study is also given to illustrate the methodology.

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Section: Simulation Studiesmentioning

confidence: 99%

“…[1], [16] and [3] among others, extended the classical case-cohort design to more complex sampling schemes. Besides, [30] and [13] conducted quantile regression analysis of case-cohort data. All these models may be adopted to indirectly make statistical inference for the mean residual lifetime.…”

Section: Introductionmentioning

confidence: 99%

Proportional mean residual life model with censored survival data under case-cohort design

Shi

Zhou

2019

Statistics and Its Interface

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“…This paper has concentrated on the outcome data that were right censored, as this was the main feature of our motivating dataset. Subsequent work has greatly expanded the applicability of these methods to competing risks 17 – 19 , recurrent events 20 , 21 , various censoring types 17 , 22 and other settings. For example, Yang et al proposed a new method for different forms of censoring including doubly censored and interval censored data 9 .…”

Section: Introductionmentioning

confidence: 99%

The comparison of censored quantile regression methods in prognosis factors of breast cancer survival

Yazdani

Yaseri

Haghighat

et al. 2021

Sci Rep

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The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.

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“…Quantile regression 1 retains the direct interpretation as the accelerated failure time (AFT) model 2 and provides a natural way to model the data with heterogeneity; thus, it has become an appealing alternative to the Cox proportional hazards model 3 and the accelerated failure time model in survival analysis. 4–6 In the literature, a variety of approaches have been developed to handle the quantile regression model with right-censored survival data. Under the fixed censoring assumption, Powell 7,8 first proposed a regression quantile estimator for responses using the least absolute deviation (LAD) idea.…”

Section: Introductionmentioning

confidence: 99%

Quantile regression models for survival data with missing censoring indicators

Zhiping

Chen

et al. 2021

Stat Methods Med Res

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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.

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Quantile regression for competing risks analysis under case-cohort design

Cited by 5 publications

References 27 publications

Proportional mean residual life model with censored survival data under case-cohort design

Proportional mean residual life model with censored survival data under case-cohort design

The comparison of censored quantile regression methods in prognosis factors of breast cancer survival

Quantile regression models for survival data with missing censoring indicators

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