The ever-increasing pace of development of novel therapies mandates efficient methodologies for assessment of their tolerability and activity. Evidence increasingly support the merits of model-based dose-finding designs in identifying the recommended phase II dose compared with conventional rule-based designs such as the 3 + 3 but despite this, their use remains limited. Here, we propose a useful tool, dose transition pathways (DTP), which helps overcome several commonly faced practical and methodologic challenges in the implementation of model-based designs. DTP projects in advance the doses recommended by a model-based design for subsequent patients (stay, escalate, de-escalate, or stop early), using all the accumulated information. After specifying a model with favorable statistical properties, we utilize the DTP to fine-tune the model to tailor it to the trial's specific requirements that reflect important clinical judgments. In particular, it can help to determine how stringent the stopping rules should be if the investigated therapy is too toxic. Its use to design and implement a modified continual reassessment method is illustrated in an acute myeloid leukemia trial. DTP removes the fears of model-based designs as unknown, complex systems and can serve as a handbook, guiding decision-making for each dose update. In the illustrated trial, the seamless, clear transition for each dose recommendation aided the investigators' understanding of the design and facilitated decision-making to enable finer calibration of a tailored model. We advocate the use of the DTP as an integral procedure in the co-development and successful implementation of practical model-based designs by statisticians and investigators. .

Download full-text PDF

Source
http://dx.doi.org/10.1158/1078-0432.CCR-17-0582DOI Listing

Publication Analysis

Top Keywords

model-based designs
12
dose transition
8
transition pathways
8
dose-finding designs
8
designs
6
dose
5
model-based
5
dtp
5
pathways missing
4
missing link
4

Similar Publications

A vision model for automated frozen tuna processing.

Sci Rep

January 2025

School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan, 316022, People's Republic of China.

Accurate and rapid segmentation of key parts of frozen tuna, along with precise pose estimation, is crucial for automated processing. However, challenges such as size differences and indistinct features of tuna parts, as well as the complexity of determining fish poses in multi-fish scenarios, hinder this process. To address these issues, this paper introduces TunaVision, a vision model based on YOLOv8 designed for automated tuna processing.

View Article and Find Full Text PDF

This study introduces a novel ensemble learning technique namely Multi-Armed Bandit Ensemble (MAB-Ensemble), designed for lane detection in road images intended for autonomous vehicles. The foundation of the proposed MAB-Ensemble technique is inspired in terms of Multi-Armed bandit optimization to facilitate efficient model selection for lane segmentation. The benchmarking dataset namely TuSimple is used for training, validating and testing the proposed and existing lane detection techniques.

View Article and Find Full Text PDF

Quantitative Evaluation of Multiple Treatment Regimens for Treatment-Resistant Depression.

Int J Neuropsychopharmacol

January 2025

Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai 201203, China.

Objective: This study aims to quantitatively evaluate the efficacy and safety of various treatment regimens for treatment-resistant depression (TRD) across oral, intravenous, and intranasal routes to inform clinical guidelines.

Methods: A systematic review identified randomized controlled trials on TRD, with efficacy measured by changes in the Montgomery-Åsberg Depression Rating Scale (MADRS). We developed pharmacodynamic and covariate models for different administration routes, using Monte Carlo simulations to estimate efficacy distribution.

View Article and Find Full Text PDF

Research on Fire Detection of Cotton Picker Based on Improved Algorithm.

Sensors (Basel)

January 2025

College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model.

View Article and Find Full Text PDF

Real-time and accurate traffic forecasting aids in traffic planning and design and helps to alleviate congestion. Addressing the negative impacts of partial data loss in traffic forecasting, and the challenge of simultaneously capturing short-term fluctuations and long-term trends, this paper presents a traffic forecasting model, D-MGDCN-CLSTM, based on Multi-Graph Gated Dilated Convolution and Conv-LSTM. The model uses the DTWN algorithm to fill in missing data.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!