TY - JOUR
T1 - Regression models using parametric pseudo-observations
AU - Nygård Johansen, Martin
AU - Lundbye-Christensen, Søren
AU - Thorlund Parner, Erik
PY - 2020/9/30
Y1 - 2020/9/30
N2 - Pseudo-observations based on the nonparametric Kaplan-Meier estimator of the survival function have been proposed as an alternative to the widely used Cox model for analyzing censored time-to-event data. Using a spline-based estimator of the survival has some potential benefits over the nonparametric approach in terms of less variability. We propose to define pseudo-observations based on a flexible parametric estimator and use these for analysis in regression models to estimate parameters related to the cumulative risk. We report the results of a simulation study that compares the empirical standard errors of estimates based on parametric and nonparametric pseudo-observations in various settings. Our simulations show that in some situations there is a substantial gain in terms of reduced variability using the proposed parametric pseudo-observations compared with the nonparametric pseudo-observations. The gain can be measured as a reduction of the empirical standard error by up to about one third; corresponding to an additional 125% larger sample size. We illustrate the use of the proposed method in a brief data example.
AB - Pseudo-observations based on the nonparametric Kaplan-Meier estimator of the survival function have been proposed as an alternative to the widely used Cox model for analyzing censored time-to-event data. Using a spline-based estimator of the survival has some potential benefits over the nonparametric approach in terms of less variability. We propose to define pseudo-observations based on a flexible parametric estimator and use these for analysis in regression models to estimate parameters related to the cumulative risk. We report the results of a simulation study that compares the empirical standard errors of estimates based on parametric and nonparametric pseudo-observations in various settings. Our simulations show that in some situations there is a substantial gain in terms of reduced variability using the proposed parametric pseudo-observations compared with the nonparametric pseudo-observations. The gain can be measured as a reduction of the empirical standard error by up to about one third; corresponding to an additional 125% larger sample size. We illustrate the use of the proposed method in a brief data example.
KW - flexible parametric models
KW - pseudo-observations
KW - time-to-event
UR - http://www.scopus.com/inward/record.url?scp=85086166095&partnerID=8YFLogxK
U2 - 10.1002/sim.8586
DO - 10.1002/sim.8586
M3 - Journal article
C2 - 32519771
SN - 0277-6715
VL - 39
SP - 2949
EP - 2961
JO - Statistics in Medicine
JF - Statistics in Medicine
IS - 22
ER -