Quantile regression based on counting process approach under semi-competing risks data

Hsieh, Jin-Jian; Wang, Hongrui

doi:10.1007/s10463-016-0593-6

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…Peng and Huang [60] developed an estimator which is very close to Nelson-Aalen estimator. Most recently, great work is still expanding this area to recurrent events [61][62][63], various censoring types [64][65][66], competing risks [65,[67][68][69].…”

Section: Qr Models For Time-to-event Datamentioning

confidence: 99%

Quantile Regression Models and Their Applications: A Review

Huang¹,

Zhang²,

Chen³

et al. 2017

J Biom Biostat

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Quantile regression (QR) has received increasing attention in recent years and applied to wide areas such as investment, finance, economics, medicine and engineering. Compared with conventional mean regression, QR can characterize the entire conditional distribution of the outcome variable, may be more robust to outliers and misspecification of error distribution, and provides more comprehensive statistical modeling than traditional mean regression. QR models could not only be used to detect heterogeneous effects of covariates at different quantiles of the outcome, but also offer more robust and complete estimates compared to the mean regression, when the normality assumption violated or outliers and long tails exist. These advantages make QR attractive and are extended to apply for different types of data, including independent data, time-to-event data and longitudinal data. Consequently, we present a brief review of QR and its related models and methods for different types of data in various application areas.Citation: Huang Q, Zhang H, Chen J, He M (2017) Quantile Regression Models and Their Applications: A Review. J Biom Biostat 8: 354.

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Section: Qr Models For Time-to-event Datamentioning

confidence: 99%

Quantile Regression Models and Their Applications: A Review

Huang¹,

Zhang²,

Chen³

et al. 2017

J Biom Biostat

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show abstract

“…Competing risks arise when different types of events are possible, and they are all terminal, while semicompeting risks have two event: one terminal and one nonterminal. QR approaches have been developed for such scenarios …”

Section: Introductionmentioning

confidence: 99%

“…This strategy is linked to marginal modeling and leads to a consistent estimator under quite general assumptions . Copula models have also been used in QR modeling with time‐to‐event data …”

Section: Introductionmentioning

confidence: 99%

“…QR approaches have been developed for such scenarios. [20][21][22][23][24][25][26] In this work, we are interested in QR for discrete state-space continuous-time stochastic processes, a class of multistate processes. 1,27 In our setting, there are a number of states (eg, healthy, transplanted, graft failed, and dead) and individuals jump from a given state to another.…”

Section: Introductionmentioning

confidence: 99%

“…38,39 Copula models have also been used in QR modeling with time-to-event data. 23,25 In summary, we develop an approach to QR to model time-to-transition following a first-jump representation of the data, via censored QR. Our methods are general, as they can be applied to different multistate processes including, but not limited to, recurrent events, competing risks, and semicompeting risks, and they can be implemented using readily available software.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multistate quantile regression models

Farcomeni

Geraci

2019

Statistics in Medicine

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We develop regression methods for inference on conditional quantiles of time‐to‐transition in multistate processes. Special cases include survival, recurrent event, semicompeting, and competing risk data. We use an ad hoc representation of the underlying stochastic process, in conjunction with methods for censored quantile regression. In a simulation study, we demonstrate that the proposed approach has a superior finite sample performance over simple methods for censored quantile regression, which naively assume independence between states, and over methods for competing risks, even when the latter are applied to competing risk data settings. We apply our approach to data on hospital‐acquired infections in cirrhotic patients, showing a quantile‐dependent effect of catheterization on time to infection.

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Quantile Process with Applications : Chirp Signal Model

Dhar¹

2021

Preprint

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<p>The parameters in the well-known chirp signal model controls the frequency fluctuations of the signals, and consequently, the estimation of the parameters has received considerable attention in the literature of statistical signal processing. In the same spirit with a broader view, this article investigates the quantile estimator of parameters involved in the chirp signal model, which enables us to provide basic features of the entire distribution of the signals. In the course of this study, we establish the limiting behaviour of the associated stochastic process, which we call quantile process. As the applications of this result, we obtain the limiting distributions of various quantile based measures of descriptive statistics, which give us summarized features of the fluctuations of the signals in various senses. Finally, along with extensive simulation study, the practicability of the proposed methodology is shown on a few benchmark real datasets closely related with various chirp signal models.<br></p>

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Quantile regression based on counting process approach under semi-competing risks data

Cited by 5 publications

References 26 publications

Quantile Regression Models and Their Applications: A Review

Quantile Regression Models and Their Applications: A Review

Multistate quantile regression models

Quantile Process with Applications : Chirp Signal Model

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