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Related Experiment Video

Updated: Jul 17, 2026

Surgical Protocol for a Large, Resealable Cranial Window Enabling Longitudinal, Multi-Modal Electrophysiology Recordings of Mouse Default Mode Network

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One- and two-stage designs for phase II window studies.

Myron N Chang¹, Meenakshi Devidas, James Anderson

¹Division of Biostatistics, Department of Epidemiology and Health Policy Research, College of Medicine, University of Florida, Gainesville, FL, USA. mchang@cog.ufl.edu

Statistics in Medicine

|January 11, 2007

Summary

This study introduces new statistical designs for phase II cancer window studies. These methods efficiently evaluate new therapies in newly diagnosed patients, improving cancer drug development.

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Area of Science:

Oncology
Biostatistics
Clinical Trial Design

Background:

Phase II window studies evaluate novel cancer therapies in newly diagnosed patients, offering a more representative population than those with prior treatment failures.
These studies assess treatment response, stable disease, or early progression, with potential for early stopping based on efficacy or toxicity.
Current designs may not optimally balance the monitoring of response and progression rates.

Purpose of the Study:

To develop and present novel one-stage and two-stage statistical designs for phase II cancer window studies.
To enable efficient monitoring of both treatment response and early disease progression rates.
To provide a framework for calculating significance levels and statistical power easily.

Main Methods:

Development of one-stage and two-stage adaptive designs for phase II window studies.
Statistical modeling to monitor two key outcome rates: response and early disease progression.
Demonstration of the monotonicity of the power function with respect to these rates.

Main Results:

The proposed designs allow for straightforward computation of significance level and power.
The power function's monotonicity simplifies interpretation and application.
Computational procedures and illustrative examples are provided for practical implementation.

Conclusions:

The developed statistical designs offer an effective approach for phase II cancer window studies.
These methods facilitate robust evaluation of new single agents or combination therapies.
The approach is adaptable to other clinical studies with categorical outcomes.