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Outsourcing-Pharma: Remarque Lands Patent for Risk-Based Quality Management Platform

The technology is designed to enable connection to any source data system, enabling connectivity in identifying, monitoring, and managing trial risks.

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Rewriting the Rules: How to Prepare for ICH E6 (R3)

The International Council for Harmonisation (ICH) establishes industry-wide standards for clinical trials – and those standards are undergoing a complete rewrite, affecting the ways trial data is monitored, analyzed, and de-risked. These changes are being developed both at the behest of and with extensive input from a wide range of industry insiders; and they are

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Data Literacy: The Foundation for Modern Trial Execution

Data literacy is a simple concept. Looking at data related to your area of specialty, you can deduce insights, ask appropriate questions, and make clear data-based decisions. Within the clinical trial ecosphere, each role requires a different degree of skill both vis-à-vis data in general and the trial data specifically.

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Clinical Trial Evolution: The Drive to Update ICH E6

The ICH E6 guidelines has set the clinical trial industry standard since it was first published in 1996. As clinical trials and technologies, however, continue to rapidly evolve, there is a need for these guidelines to shift as well. The industry has called for a revision of the ICH E6 (R2) guidelines to spotlight risk-based

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How to Manage Machine Learning Algorithms in Clinical Trials

As today’s clinical trials grow increasingly complex, risk-based monitoring has become the watchword of the day. It is integral to any quality management program; it is mandated by regulatory agencies; and, critically, it is a potent tool for forestalling costly failures…

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Six Considerations When Implementing Machine Learning Algorithms in Clinical Research

Machine learning provides a crucial opportunity to shape and direct clinical trial research, helping harmonize and analyze the massive amounts of data generated in the course of a study, providing the means needed by researchers to make faster, more informed decisions. Yet, the machines are only as effective as their algorithms and their interaction with

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Age of the Machine: Using Algorithms to Advance Outcomes

Starting with trial design and site selection, machine learning algorithms can be used at every stage of drug development in order to increase efficiency and data quality. It offers endless opportunities for medical imaging, trial recruitment, and other aspects of the life-sciences industry.

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Swimming in Data

By: Kristin Mauri and Rhonda Roberts at Remarque Systems The amount of data collected during clinical trials has increased dramatically. In order to keep up with trends, machine learning offers the opportunity to keep studies on the right trajectory using real-time data to identify anomalies and missing data. This is important not only during the