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G-computation and doubly robust standardisation for continuous-time data: A comparison with inverse probability
Arthur Chatton1,2, Florent Le Borgne1,2, Clémence Leyrat3,4
1INSERM UMR 1246 - SPHERE, 27045Nantes University, Tours University, France.
Statistical Methods in Medical Research
|December 4, 2021
Summary
This study extends g-computation and doubly robust methods for continuous-time survival data. These continuous-time approaches offer improved efficiency over traditional methods for estimating hazard ratios and survival differences.
Area of Science:
- Biostatistics
- Survival Analysis
- Epidemiology
Background:
- Time-to-event analyses often rely on discrete-time models.
- Biological processes frequently exhibit continuous-time dynamics.
- Existing methods like inverse-probability-weighting have limitations in continuous settings.
Purpose of the Study:
- To extend g-computation and doubly robust standardization to continuous-time settings.
- To compare the performance of these extended methods against inverse-probability-weighting.
- To provide practical tools for analyzing continuous-time survival data.
Main Methods:
- Development of continuous-time g-computation and doubly robust estimators.
- Simulation studies to evaluate estimator performance.
- Analysis of real-world datasets using updated R package RISCA.
Main Results:
- All tested methods (g-computation, doubly robust, inverse-probability-weighting) are unbiased under correct model specification.
- Continuous-time g-computation and doubly robust methods demonstrate superior efficiency compared to inverse-probability-weighting.
- Successful application to two real-world datasets.
Conclusions:
- Extended g-computation and doubly robust methods are valuable for continuous-time survival data.
- These methods offer enhanced efficiency for estimating hazard ratios and restricted mean survival times differences.
- The updated RISCA package facilitates the adoption and dissemination of these advanced statistical techniques.
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