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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

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Published on: September 10, 2018

Regulatory decision-making meets the real world.

Ryan Hohman¹, Michael Shea¹, Marina Kozak¹

¹Friends of Cancer Research, 1800 M Street, NW, Washington, DC 20036, USA.

Science Translational Medicine

|November 13, 2015

Summary

Regulators must organize and ensure data quality as patient input and new data sources grow in drug development to support informed decision-making.

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Area of Science:

Regulatory Science
Data Management
Pharmacovigilance

Background:

Growing patient involvement in drug development necessitates robust data handling.
Emerging data sources present challenges for traditional regulatory oversight.

Purpose of the Study:

To highlight the need for organized and quality-assured data in regulatory decision-making.
To address the challenges posed by increased patient input and diverse data streams.

Main Methods:

Literature review of current data management practices.
Analysis of regulatory guidelines and challenges.
Case study on data quality assessment in drug development.

Main Results:

Current data management strategies are insufficient for the evolving landscape.
Standardized data quality frameworks are crucial for regulatory bodies.
Effective organization of patient-generated data is key.

Conclusions:

A proactive approach to data organization and quality assurance is essential for regulators.
Implementing standardized data quality metrics will enhance regulatory decision-making.
Adapting to new data sources ensures patient safety and effective drug development.