Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R

Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R

survival analysis, competing risks, pseudo-observations, regression, R.

Due to tradition and ease of estimation, the vast majority of clinical and epidemiological papers with time-to-event data report hazard ratios from Cox proportional hazards
regression models. Although hazard ratios are well known, they can be difficult to interpret, particularly as causal contrasts, in many settings. Nonparametric or fully parametric
estimators allow for the direct estimation of more easily causally interpretable estimands
such as the cumulative incidence and restricted mean survival. However, modeling these
quantities as functions of covariates is limited to a few categorical covariates with nonparametric estimators, and often requires simulation or numeric integration with parametric estimators. Combining pseudo-observations based on non-parametric estimands
with parametric regression on the pseudo-observations allows for the best of these two
approaches and has many nice properties. In this paper, we develop a user friendly, easy
to understand way of doing event history regression for the cumulative incidence and the
restricted mean survival, using the pseudo-observation framework for estimation. 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 correct variance
estimation

Michael C. Sachs

https://www.jstatsoft.org/article/view/v102i09

Karolinska Institutet

April 2022

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