期刊
STATISTICAL METHODS IN MEDICAL RESEARCH
卷 31, 期 4, 页码 706-718出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/09622802211047345
关键词
Causal inference; parametric g-formula; propensity score; restricted mean survival time; simulation study
类别
资金
- IDBC
This paper extends the G-computation and doubly robust estimators based on discrete-time data to continuous-time settings, showing their higher efficiency compared to the well-known inverse-probability-weighting estimator through a simulation study. The practical implementation of these methods is illustrated using real-world datasets, with the R package RISCA being updated to facilitate their use and dissemination.
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 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.
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