TITE-STEIN: Time-to-event simple toxicity and efficacy interval design to accelerate phase I/II trials

Oncology dose-finding trials are shifting from identifying the maximum-tolerated dose (MTD) to determining the optimal biological dose (OBD), driven by the need for efficient methods that consider both toxicity and efficacy. This is particularly important for novel therapies, such as immunotherapies and molecularly targeted therapies, which often exhibit non-monotonic dose-efficacy curves. The Simple Toxicity and Efficacy Interval (STEIN) design has demonstrated strong performance in accommodating diverse dose-efficacy patterns and incorporating both toxicity and efficacy outcomes to select the OBD. However, the rapid accrual of patients and the often-delayed onset of toxicity and/or efficacy pose challenges to timely adaptive-dose decisions. To address these challenges, we propose TITE-STEIN, a model-assisted design that incorporates time-to-event (TITE) outcomes for toxicity and/or efficacy, by extending STEIN. In this article, we demonstrate that TITE-STEIN significantly shortens the trial duration compared to STEIN. Furthermore, by integrating an OBD verification procedure during OBD selection, TITE-STEIN effectively mitigates the risk of exposing patients to inadmissible doses when the OBD does not exist. Extensive simulations demonstrate that TITE-STEIN outperforms existing TITE designs, including TITE-BONI12, TITE-BOIN-ET, LO-TC, and Joint TITE-CRM, by selecting the OBD more accurately, allocating more patients to it, and improving overdose control.

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Publication Details

Journal
Journal of Biopharmaceutical Statistics
Published
2025-12-04
DOI
https://doi.org/10.1080/10543406.2025.2592615
Primary Topic
Statistical Methods in Clinical Trials
Type
article
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article

TITE-STEIN: Time-to-event simple toxicity and efficacy interval design to accelerate phase I/II trials

Hao Sun, Revathi Ananthakrishnan, Jieqi Tu, Eunhee Kim
Journal of Biopharmaceutical Statistics
Statistical Methods in Clinical Trials
article

TITE-STEIN: Time-to-event simple toxicity and efficacy interval design to accelerate phase I/II trials

Hao Sun, Revathi Ananthakrishnan, Jieqi Tu, Eunhee Kim
article en

Abstract

Oncology dose-finding trials are shifting from identifying the maximum-tolerated dose (MTD) to determining the optimal biological dose (OBD), driven by the need for efficient methods that consider both toxicity and efficacy. This is particularly important for novel therapies, such as immunotherapies and molecularly targeted therapies, which often exhibit non-monotonic dose-efficacy curves. The Simple Toxicity and Efficacy Interval (STEIN) design has demonstrated strong performance in accommodating diverse dose-efficacy patterns and incorporating both toxicity and efficacy outcomes to select the OBD. However, the rapid accrual of patients and the often-delayed onset of toxicity and/or efficacy pose challenges to timely adaptive-dose decisions. To address these challenges, we propose TITE-STEIN, a model-assisted design that incorporates time-to-event (TITE) outcomes for toxicity and/or efficacy, by extending STEIN. In this article, we demonstrate that TITE-STEIN significantly shortens the trial duration compared to STEIN. Furthermore, by integrating an OBD verification procedure during OBD selection, TITE-STEIN effectively mitigates the risk of exposing patients to inadmissible doses when the OBD does not exist. Extensive simulations demonstrate that TITE-STEIN outperforms existing TITE designs, including TITE-BONI12, TITE-BOIN-ET, LO-TC, and Joint TITE-CRM, by selecting the OBD more accurately, allocating more patients to it, and improving overdose control.

Journal of Biopharmaceutical Statistics
Bristol-Myers Squibb (Germany) (DE), University of Illinois Chicago (US), Nurix (United States) (US)
Openalex Percentile: Top 65%
Statistical Methods in Clinical Trials
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TITE-STEIN: Time-to-event simple toxicity and efficacy interval design to accelerate phase I/II trials — Hao Sun, Revathi Ananthakrishnan, et al. · Journal of Biopharmaceutical Statistics (2025) | TGRS Research Map | TGRS