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Efficient Causal Survival Inference under Limited Surrogate Monitoring

  • Zhenyue Du
  • Weishang Kong
  • Wei Li
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Abstract

Post-treatment surrogate endpoints can improve the efficiency of causal survival effect estimation, but their monitoring may end before follow-up for the primary outcome. We investigate the impact of limited recurrence monitoring on semiparametric efficiency when mortality follow-up continues. We derive the efficient influence function under limited monitoring and establish an exact expression for the efficiency gain from extending monitoring between any two durations. Our framework encompasses the absence of surrogate information and the usual shared-censoring scheme as boundary cases, and allows comparison with fully observed surrogates. We develop a doubly robust estimator under limited surrogate monitoring and establish asymptotic normality and semiparametric efficiency for its cross-fitted version under suitable conditions. Simulations and an application to breast cancer data illustrate the proposed method.

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Cite

BibTeXRIS
BibTeX
@misc{du2026efficient,
  title = {{Efficient Causal Survival Inference under Limited Surrogate Monitoring}},
  author = {Zhenyue Du and Weishang Kong and Wei Li},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.00553},
  eprint = {2611.00553},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.00553},
}
RIS
TY  - UNPB
TI  - Efficient Causal Survival Inference under Limited Surrogate Monitoring
AU  - Zhenyue Du
AU  - Weishang Kong
AU  - Wei Li
PY  - 2026
DA  - 2026/11/01
PB  - arXiv
DO  - 10.48550/arxiv.2611.00553
UR  - https://arxiv.org/abs/2611.00553
AB  - Post-treatment surrogate endpoints can improve the efficiency of causal survival effect estimation, but their monitoring may end before follow-up for the primary outcome. We investigate the impact of limited recurrence monitoring on semiparametric efficiency when mortality follow-up continues. We derive the efficient influence function under limited monitoring and establish an exact expression for the efficiency gain from extending monitoring between any two durations. Our framework encompasses the absence of surrogate information and the usual shared-censoring scheme as boundary cases, and allows comparison with fully observed surrogates. We develop a doubly robust estimator under limited surrogate monitoring and establish asymptotic normality and semiparametric efficiency for its cross-fitted version under suitable conditions. Simulations and an application to breast cancer data illustrate the proposed method.
ER  -