Premium
Regression models using parametric pseudo‐observations
Author(s) -
Nygård Johansen Martin,
LundbyeChristensen Søren,
Thorlund Parner Erik
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8586
Subject(s) - nonparametric statistics , estimator , parametric statistics , statistics , parametric model , nonparametric regression , semiparametric regression , econometrics , proportional hazards model , computer science , regression analysis , spline (mechanical) , mathematics , structural engineering , engineering
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.