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
AN - SCOPUS:85041234025
SN - 2073-4859
VL - 9
SP - 440
EP - 460
JO - The R Journal
JF - The R Journal
IS - 2
ER -