riskRegression: Predicting the risk of an event using cox regression models

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

riskRegression : Predicting the risk of an event using cox regression models. / Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas; Torp-Pedersen, Christian; Gerds, Thomas Alexander.

In: The R Journal, Vol. 9, No. 2, 01.12.2017, p. 440-460.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Ozenne, B, Sørensen, AL, Scheike, T, Torp-Pedersen, C & Gerds, TA 2017, 'riskRegression: Predicting the risk of an event using cox regression models', The R Journal, vol. 9, no. 2, pp. 440-460.

APA

Ozenne, B., Sørensen, A. L., Scheike, T., Torp-Pedersen, C., & Gerds, T. A. (2017). riskRegression: Predicting the risk of an event using cox regression models. The R Journal, 9(2), 440-460.

Vancouver

Ozenne B, Sørensen AL, Scheike T, Torp-Pedersen C, Gerds TA. riskRegression: Predicting the risk of an event using cox regression models. The R Journal. 2017 Dec 1;9(2):440-460.

Author

Ozenne, Brice ; Sørensen, Anne Lyngholm ; Scheike, Thomas ; Torp-Pedersen, Christian ; Gerds, Thomas Alexander. / riskRegression : Predicting the risk of an event using cox regression models. In: The R Journal. 2017 ; Vol. 9, No. 2. pp. 440-460.

Bibtex

@article{9ffebd16bd764ce680f824210f40619d,
title = "riskRegression: Predicting the risk of an event using cox regression models",
abstract = "In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical functionals. The software presented here is implemented in the riskRegression package.",
author = "Brice Ozenne and S{\o}rensen, {Anne Lyngholm} and Thomas Scheike and Christian Torp-Pedersen and Gerds, {Thomas Alexander}",
year = "2017",
month = "12",
day = "1",
language = "English",
volume = "9",
pages = "440--460",
journal = "The R Journal",
issn = "2073-4859",
publisher = "R Foundation for Statistical Computing",
number = "2",

}

RIS

TY - JOUR

T1 - riskRegression

T2 - Predicting the risk of an event using cox regression models

AU - Ozenne, Brice

AU - Sørensen, Anne Lyngholm

AU - Scheike, Thomas

AU - Torp-Pedersen, Christian

AU - Gerds, Thomas Alexander

PY - 2017/12/1

Y1 - 2017/12/1

N2 - In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical functionals. The software presented here is implemented in the riskRegression package.

AB - In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical functionals. The software presented here is implemented in the riskRegression package.

UR - http://www.scopus.com/inward/record.url?scp=85041234025&partnerID=8YFLogxK

UR - http://search.ebscohost.com.ep.fjernadgang.kb.dk/login.aspx?direct=true&db=a9h&AN=127755692&site=ehost-live

M3 - Journal article

VL - 9

SP - 440

EP - 460

JO - The R Journal

JF - The R Journal

SN - 2073-4859

IS - 2

ER -

ID: 189623972