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Updated: Jun 24, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

A method to determine stroke trial success using multidimensional pooled control functions.

Pitchaiah Mandava1, Thomas A Kent

  • 1Department of Neurology, MSEE, MEDVAMC/Baylor College of Medicine, Houston, TX 770303, USA. pmandava@bcm.tmc.edu

Stroke
|March 17, 2009
PubMed
Summary
This summary is machine-generated.

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A new statistical model using pooled stroke trial data accurately predicts clinical trial outcomes. This method helps identify promising stroke therapies and avoid costly failures in later-phase trials.

Area of Science:

  • Neurology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Early-phase stroke trials often yield misleading results due to randomization imbalances in baseline factors.
  • Achieving balanced randomization in stroke trials is challenging, potentially compromising early findings.

Purpose of the Study:

  • To develop and validate a statistical model using pooled control arm data to predict clinical trial outcomes in stroke.
  • To mitigate randomization problems and provide reliable predictions for advancing stroke therapies to later-phase trials.

Main Methods:

  • Pooled control arm data from acute stroke trials (1994-2008) were analyzed, including National Institute of Health Stroke Scale, age, and 3-month outcomes.
  • A Matlab program (PPREDICTS) generated outcome functions and multidimensional 95% prediction surfaces for comparison with published treatment trials.

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Computerized Adaptive Testing System of Functional Assessment of Stroke
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Computerized Adaptive Testing System of Functional Assessment of Stroke

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Last Updated: Jun 24, 2026

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Published on: January 7, 2019

Main Results:

  • Models for mortality (R(2)=0.69) and functional outcome (modified Rankin Scale 0-2: R(2)=0.81) were successfully generated (P<0.0001).
  • The method correctly identified positive and negative trial outcomes, including trials that initially appeared promising but failed in Phase III.

Conclusions:

  • The developed model accurately confirmed past clinical stroke trial results, identifying both successful and unsuccessful studies.
  • Utilizing a pooled standard treatment group function with statistical bounds can enhance the selection of early-stage studies for further investigation.
  • This predictive approach is potentially applicable to various conditions where baseline factors influence outcomes and across different developmental stages.