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Personalized Dynamic Dose-Finding for Longitudinal Observational Data

Abstract Number:

1209

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Poster

Participants:

Md Nasim Saba Nishat (1), Alexander McLain (1)

Institutions:

(1) University of South Carolina, N/A

Co-Author:

Alexander McLain
University of South Carolina

First Author:

Md Nasim Saba Nishat
University of South Carolina

Presenting Author:

Md Nasim Saba Nishat
University of South Carolina

Abstract Text:

The prescribed doses for many drugs are based on population norms or physician discretion. For example, very low birth weight (VLBW) infants (BW < 1500 grams) often experience slower postnatal growth and require glucose treatment to support weight gain and prevent hyperglycemia. A uniform dosage is unsuitable due to individual differences in glucose metabolism influenced by weight, gestational age, and other factors. Personalized dynamic dosing adjusts to an infant's observed responses, but quantifying uncertainty is essential in patient-critical environments. This study employs a longitudinal mixed model to estimate personalized optimal doses, using patient-specific random effects as biomarkers to capture individual sensitivities. We quantify the uncertainty of these doses through the implicit value delta method, aiding in safe clinical decision-making. Simulation studies validate our model's robustness, and analysis of NICU data on glucose treatments for VLBW infants over their first seven days demonstrates key differences between optimal and prescribed dosing strategies.

Keywords:

Sponsors:

ENAR

Tracks:

Miscellaneous

Can this be considered for alternate subtype?

Yes

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I have read and understand that JSM participants must abide by the Participant Guidelines.

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I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.

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