Regression Models for Event History Outcomes
Compute inverse probability of censoring weights pseudo observations
Error check censoring model
Confidence Intervals for pseudoglm Model Parameters
Generalized linear models for cumulative incidence
Regression Models for Event History Outcomes
Utility to get jackknife pseudo observations of cumulative incidence
Utility to get jackknife pseudo observations of restricted mean
Compute jackknife pseudo-observations of the cause-specific cumulative...
Compute jackknife pseudo-observations of the survival function
Compute jackknife pseudo-observations of the cause-specific cumulative...
Compute jackknife pseudo-observations of the cause-specific cumulative...
Compute leave one out jackknife contributions of the survival function
Compute leave one out jackknife contributions of the survival function
Match cause specification against model response
Print method for pseudoglm
Compute censoring weighted pseudo observations
Compute censoring weighted pseudo observations
Compute pseudo observations under independent censoring
Compute infinitesimal jackknife pseudo observations
Compute pseudo-observations for the restricted mean survival
Compute pseudo observations using stratified jackknife
Objects exported from other packages
Pseudo-observation scaled residuals
Generalized linear models for the restricted mean survival
Summary method
Compute covariance matrix of regression coefficient estimates
A user friendly, easy to understand way of doing event history regression for marginal estimands of interest, including the cumulative incidence and the restricted mean survival, using the pseudo observation framework for estimation. For a review of the methodology, see Andersen and Pohar Perme (2010) <doi:10.1177/0962280209105020> or Sachs and Gabriel (2022) <doi:10.18637/jss.v102.i09>. The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation.