Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes

Research output: Contribution to journal › Journal article › Research › peer-review

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Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes. / Gabriel, Erin E; Sachs, Michael C; Jensen, Andreas Kryger.

In: Biometrika, 2024.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Gabriel, EE, Sachs, MC & Jensen, AK 2024, 'Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes', Biometrika. https://doi.org/10.1093/biomet/asae020

APA

Gabriel, E. E., Sachs, M. C., & Jensen, A. K. (2024). Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes. Biometrika. https://doi.org/10.1093/biomet/asae020

Vancouver

Gabriel EE, Sachs MC, Jensen AK. Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes. Biometrika. 2024. https://doi.org/10.1093/biomet/asae020

Author

Gabriel, Erin E ; Sachs, Michael C ; Jensen, Andreas Kryger. / Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes. In: Biometrika. 2024.

Bibtex

@article{474e23ef2b1f466abd181c9fe5fea5f6,
title = "Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes",
abstract = "The probability of benefit can be a valuable and meaningful measure of treatment effect. Particularly for an ordinal outcome, it can have an intuitive interpretation. Unfortunately, this measure, and variations of it, are not identifiable even in randomized trials with perfect compliance. There is, for this reason, a long literature on nonparametric bounds for unidentifiable measures of benefit. These have primarily focused on perfect randomized trial settings and one or two specific estimands. We expand these bounds to observational settings with unmeasured confounders and imperfect randomized trials for all three estimands considered in the literature: the probability of benefit, the probability of no harm and the relative treatment effect.",
author = "Gabriel, {Erin E} and Sachs, {Michael C} and Jensen, {Andreas Kryger}",
year = "2024",
doi = "10.1093/biomet/asae020",
language = "English",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",

}

RIS

TY - JOUR

T1 - Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes

AU - Gabriel, Erin E

AU - Sachs, Michael C

AU - Jensen, Andreas Kryger

PY - 2024

Y1 - 2024

N2 - The probability of benefit can be a valuable and meaningful measure of treatment effect. Particularly for an ordinal outcome, it can have an intuitive interpretation. Unfortunately, this measure, and variations of it, are not identifiable even in randomized trials with perfect compliance. There is, for this reason, a long literature on nonparametric bounds for unidentifiable measures of benefit. These have primarily focused on perfect randomized trial settings and one or two specific estimands. We expand these bounds to observational settings with unmeasured confounders and imperfect randomized trials for all three estimands considered in the literature: the probability of benefit, the probability of no harm and the relative treatment effect.

AB - The probability of benefit can be a valuable and meaningful measure of treatment effect. Particularly for an ordinal outcome, it can have an intuitive interpretation. Unfortunately, this measure, and variations of it, are not identifiable even in randomized trials with perfect compliance. There is, for this reason, a long literature on nonparametric bounds for unidentifiable measures of benefit. These have primarily focused on perfect randomized trial settings and one or two specific estimands. We expand these bounds to observational settings with unmeasured confounders and imperfect randomized trials for all three estimands considered in the literature: the probability of benefit, the probability of no harm and the relative treatment effect.

U2 - 10.1093/biomet/asae020

DO - 10.1093/biomet/asae020

M3 - Journal article

JO - Biometrika

JF - Biometrika

SN - 0006-3444

ER -

ID: 398540767