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Reference-based sensitivity analysis for bounded longitudinal outcomes — arXiv:2611.02642

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02642
License (as archived): CC0-1.0
Archived: 2026-11-03T18:43:40.962Z
SHA-256 of the archived text: ddc93a07c96b63765a8fb12960bdef60f0c465558fa9a06a0ac60484a1d36109
Stable URL of this page: https://metraipolis.com/documento/dc_da7da39de4e9

How to cite

Reference-based sensitivity analysis for bounded longitudinal outcomes — arXiv:2611.02642 — arXiv (en), https://arxiv.org/abs/2611.02642 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_da7da39de4e9 (Metraipolis archive dc_da7da39de4e9, sha256 ddc93a07c96b63765a8fb12960bdef60f0c465558fa9a06a0ac60484a1d36109)

BibTeX

@misc{dcda7da39de4e9,
  title = {Reference-based sensitivity analysis for bounded longitudinal outcomes — arXiv:2611.02642},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02642},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_da7da39de4e9; sha256 ddc93a07c96b63765a8fb12960bdef60f0c465558fa9a06a0ac60484a1d36109},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02642. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Reference-based sensitivity analysis is widely used to assess departures from the assumption that data are missing at random in longitudinal randomized trials, but existing methods are typically formulated for approximately Gaussian continuous outcomes and do not directly accommodate bounded outcomes, particularly when exact lower or upper bounds may occur with appreciable probability. We propose a Bayesian reference-based sensitivity analysis framework for bounded longitudinal outcomes, based on a thresholded latent Gaussian working model. Interior observations are mapped one-to-one to the latent scale, whereas boundary observations correspond to latent values below or above prespecified thresholds, yielding a distribution on the observed scale with a continuous interior density and positive mass at the bounds. Reference-based rules are imposed on the latent scale, and posterior predictive imputation proceeds on that scale before mapping back to the original outcome scale. Simulations assessed model calibration, recovery of complete data contrasts, and population treatment effect inference after imputation. For the proposed model, population treatment effect inference showed small bias when the analysis rule matched the truth rule after the trigger, but conservative Rubin intervals, particularly under jump to reference. The method is illustrated using the BtheB study, a longitudinal trial with a bounded depression outcome and incomplete follow-up.

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/2611.02642 — 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 2026-11-03T18:13:53.124Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).