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Covariate Adjustment for Time-Restricted Marginal Win Statistics in Randomized Trials: Assumption-Lean Inference and Guaranteed Efficiency Gain — arXiv:2611.04302

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04302
License (as archived): CC0-1.0
Archived: 2026-11-04T21:54:45.560Z
SHA-256 of the archived text: f718ef121bba88f09a8e428c85e90f2d6ca0101de579229a71eebdad7e91d591
Stable URL of this page: https://metraipolis.com/documento/dc_ded152a5f160

How to cite

Covariate Adjustment for Time-Restricted Marginal Win Statistics in Randomized Trials: Assumption-Lean Inference and Guaranteed Efficiency Gain — arXiv:2611.04302 — arXiv (en), https://arxiv.org/abs/2611.04302 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ded152a5f160 (Metraipolis archive dc_ded152a5f160, sha256 f718ef121bba88f09a8e428c85e90f2d6ca0101de579229a71eebdad7e91d591)

BibTeX

@misc{dcded152a5f160,
  title = {Covariate Adjustment for Time-Restricted Marginal Win Statistics in Randomized Trials: Assumption-Lean Inference and Guaranteed Efficiency Gain — arXiv:2611.04302},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04302},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ded152a5f160; sha256 f718ef121bba88f09a8e428c85e90f2d6ca0101de579229a71eebdad7e91d591},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04302. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Hierarchical composite endpoints, analyzed through win statistics such as the net benefit (NB), win ratio (WR), and win odds (WO), are increasingly used in randomized trials to prioritize endpoints according to clinical importance. Although covariate-adjustment methods for win statistics have been developed, a unified model-assisted approach for mixed-type, time-restricted HCEs with simultaneous asymptotic no-loss efficiency guarantees remains of interest. We propose a unified, model-assisted covariate-adjustment framework for time-restricted marginal win statistics evaluated at a prespecified post-randomization restriction horizon, a clinically interpretable estimand particularly suited to acute and high-acuity trials with well-defined short-term follow-up windows. The Hoeffding--H'ajek projection of the two-sample win--loss U-statistics yields subject-level influence functions for the marginal win and loss probabilities. We apply treatment-specific linear covariate adjustment to these influence functions and propagate the adjustment to NB, log WR, and log WO. The resulting estimators are assumption-lean, remaining consistent and asymptotically valid without correct specification of the linear working model, and are asymptotically guaranteed efficiency gain over their unadjusted counterparts. Simulations with mixed-type HCEs demonstrate substantial efficiency and power gains with prognostic covariate adjustment. However, including additional non-prognostic covariates can impair finite-sample variance estimation and inflate Type I error, particularly in smaller trials. Bootstrap variance estimation improved inferential calibration in these settings. We illustrate the proposed method using a randomized acute kidney injury alert trial.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2611.04302 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T21:39:49.509Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).