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Minimum area confidence set optimality for simultaneous confidence bands for percentiles with applications to drug shelf-life estimation

Minimum area confidence set optimality for simultaneous confidence bands for percentiles with applications to drug shelf-life estimation
Minimum area confidence set optimality for simultaneous confidence bands for percentiles with applications to drug shelf-life estimation
One important property of any drug product is its stability over time. A keyobjective in drug stability studies is to estimate the shelf-life of a drug, involving asuitable definition of the true shelf-life and the construction of an appropriate estimate of the true shelf-life. Simultaneous confidence bands (SCBs) for percentiles in linear regression are valuable tools for determining the shelf-life in drug stability studies. In this paper, we propose a novel criterion, the Minimum Area Confidence Set (MACS) criterion, for finding the optimal SCB for percentile regression lines. This criterion focuses on the area of the constrained regions for the newly proposed pivotal quantities, which are generated from the confidence set for the unknown parameters of a SCB. We employ the new pivotal quantities to construct exact SCBs over any finite covariate intervals and use the MACS criterion to compare several SCBs of different forms. The optimal SCB under the MACS criterion can be used to construct the interval estimate of the true shelf-life. Furthermore, a new computationally efficient method is proposed for calculating the critical constants of exact SCBs for percentile regression lines. A real data example on drug stability is provided for illustration.
0277-6715
Wang, Lingjiao
df56eafc-f639-498b-bb35-7e2bbb7d51fa
Han, Yang
f5a6d423-6a9c-487c-be8d-17dcdc35829f
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Bretz, Frank
aa8a675f-f53f-4c50-8931-8e9b7febd9f0
Wang, Lingjiao
df56eafc-f639-498b-bb35-7e2bbb7d51fa
Han, Yang
f5a6d423-6a9c-487c-be8d-17dcdc35829f
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Bretz, Frank
aa8a675f-f53f-4c50-8931-8e9b7febd9f0

Wang, Lingjiao, Han, Yang, Liu, Wei and Bretz, Frank (2025) Minimum area confidence set optimality for simultaneous confidence bands for percentiles with applications to drug shelf-life estimation. Statistics in Medicine. (In Press)

Record type: Article

Abstract

One important property of any drug product is its stability over time. A keyobjective in drug stability studies is to estimate the shelf-life of a drug, involving asuitable definition of the true shelf-life and the construction of an appropriate estimate of the true shelf-life. Simultaneous confidence bands (SCBs) for percentiles in linear regression are valuable tools for determining the shelf-life in drug stability studies. In this paper, we propose a novel criterion, the Minimum Area Confidence Set (MACS) criterion, for finding the optimal SCB for percentile regression lines. This criterion focuses on the area of the constrained regions for the newly proposed pivotal quantities, which are generated from the confidence set for the unknown parameters of a SCB. We employ the new pivotal quantities to construct exact SCBs over any finite covariate intervals and use the MACS criterion to compare several SCBs of different forms. The optimal SCB under the MACS criterion can be used to construct the interval estimate of the true shelf-life. Furthermore, a new computationally efficient method is proposed for calculating the critical constants of exact SCBs for percentile regression lines. A real data example on drug stability is provided for illustration.

Text
MACS_20250526 - Accepted Manuscript
Restricted to Repository staff only until 9 June 2026.
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More information

Accepted/In Press date: 9 June 2025

Identifiers

Local EPrints ID: 503940
URI: http://eprints.soton.ac.uk/id/eprint/503940
ISSN: 0277-6715
PURE UUID: 291422a7-71ba-4191-897f-a32bb069bfed
ORCID for Wei Liu: ORCID iD orcid.org/0000-0002-4719-0345

Catalogue record

Date deposited: 18 Aug 2025 17:01
Last modified: 11 Sep 2025 01:38

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Contributors

Author: Lingjiao Wang
Author: Yang Han
Author: Wei Liu ORCID iD
Author: Frank Bretz

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