G-computation and doubly robust standardisation for continuous-time data: a comparison with inverse probability weighting

TL;DR

This paper extends g-computation and doubly robust standardisation methods to continuous-time data in survival analysis, comparing their efficiency and bias to inverse probability weighting through simulations and real data applications.

Contribution

It introduces continuous-time versions of g-computation and doubly robust standardisation, and compares their performance to IPW in survival analysis.

Findings

G-computation and doubly robust methods are more efficient than IPW under correct models.

All methods are unbiased when models are correctly specified.

The updated RISCA R package facilitates these methods' application.

Abstract

In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting (IPW) estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse probability weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination.