Semiparametric transformation models with length-biased and right-censored data under the case-cohort design

Ma, Huijuan; Zhiping, Qiu; Zhou, Yong

doi:10.4310/sii.2016.v9.n2.a8

Cited by 8 publications

(5 citation statements)

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“…Other significant variables shows that female nominees tend to live longer than male nominees, USA performers are likely to live shorter than others, the number of total films and the number of four-star films in career have a positive effect on performers' life expectancy. Part of these results are consistent with those in Ma et al (2016). The refitted model is logT = 4.2004−0.1106 * Gender−0.1232 * U SA+0.0019 * N OT F +0.0058 * N OF F.…”

Section: Real Datasupporting

confidence: 86%

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High-Dimensional Variable Selection with Right Censored Length-biased Data

Zhou

Zou

2020

STAT SINICA

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Length-biased data are inevitably encountered in various fields ranging from epidemiological cohort studies to studies of labor economics, attracting much attention in the survival literature. A crucial goal of survival analysis is to identify a subset of risk factors and their risk contributions among massive clinical covariates. However, there has been no work on variable selection for length-biased data due to the complex nature of such data and the lack of a convenient loss function. In this article, we propose an estimation method based on the penalized estimating equations to obtain both sparse and consistent estimator for length-biased data under the accelerated failure time (AFT) model. The proposed estimator possesses selection and estimation consistency property. In particular, we implement the method with SCAD penalty and local linear approximation algorithm. We suggest selecting the tuning parameter by extended BIC criterion in high-dimensional setting. Furthermore, a novel multi-stage SCAD

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Section: Real Datasupporting

confidence: 86%

“…Several authors (Redelmeier and Singh, 2001;Han et al, 2011;Chen et al, 2015;Ma et al, 2016) are interested in finding out whether winning an Oscar Award causes the actor or actress' expected lifetime to increase.…”

Section: Real Datamentioning

confidence: 99%

High-Dimensional Variable Selection with Right Censored Length-biased Data

Zhou

Zou

2020

STAT SINICA

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“…, estimating equations of [5] which has been lately used by several authors for example [9] [10] [11] and [12] is modified for the effect of time-varying coeffcients and time-varying covariates.…”

Section: The Modified Estimating Equationsmentioning

confidence: 99%

“…Someone may use computationally easiest alternative versions of (12) which were first mentioned by [5] and lately by [11].…”

Section: The Modified Estimating Equationsmentioning

confidence: 99%

A Simulation Study on Comparing General Class of Semiparametric Transformation Models for Survival Outcome with Time-Varying Coefficients and Covariates

Fissuh¹,

Woldu²,

Ahmed³

et al. 2019

OJS

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The consideration of the time-varying covariate and time-varying coefficient effect in survival models are plausible and robust techniques. Such kind of analysis can be carried out with a general class of semiparametric transformation models. The aim of this article is to develop modified estimating equations under semiparametric transformation models of survival time with time-varying coefficient effect and time-varying continuous covariates. For this, it is important to organize the data in a counting process style and transform the time with standard transformation classes which shall be applied in this article. In the situation when the effect of coefficient and covariates change over time, the widely used maximum likelihood estimation method becomes more complex and burdensome in estimating consistent estimates. To overcome this problem, alternatively, the modified estimating equations were applied to estimate the unknown parameters and unspecified monotone transformation functions. The estimating equations were modified to incorporate the time-varying effect in both coefficient and covariates. The performance of the proposed methods is tested through a simulation study. To sum up the study, the effect of possibly time-varying covariates and time-varying coefficients was evaluated in some special cases of semiparametric transformation models. Finally, the results have shown that the role of the time-varying covariate in the semiparametric transformation models was plausible and credible.

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“…For example, a pseudo-likelihood procedure proposed by [27] was later elaborated by [28], [18] and [22]. Several authors studied other regression models such as the additive hazards model [15,21,17], the proportional odds model [5] and the semiparametric transformation regression model [4,14,19,7,20]. [1], [16] and [3] among others, extended the classical case-cohort design to more complex sampling schemes.…”

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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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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Semiparametric transformation models with length-biased and right-censored data under the case-cohort design

Cited by 8 publications

References 0 publications

High-Dimensional Variable Selection with Right Censored Length-biased Data

High-Dimensional Variable Selection with Right Censored Length-biased Data

A Simulation Study on Comparing General Class of Semiparametric Transformation Models for Survival Outcome with Time-Varying Coefficients and Covariates

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

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