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Related Concept Videos

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Related Experiment Video

Updated: Oct 13, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Advancing data science in drug development through an innovative computational framework for data sharing and

Ann-Marie Mallon1, Dieter A Häring2, Frank Dahlke2

  • 1MRC Harwell Institute, Harwell Campus, Oxfordshire, OX11 0RD, UK. a.mallon@har.mrc.ac.uk.

BMC Medical Research Methodology
|November 14, 2021
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Summary
This summary is machine-generated.

Novartis and Oxford BDI created an IT platform using machine learning to find hidden patterns in anonymized patient data for better disease prediction and drug development.

Keywords:
Clinical trialData anonymisationData managementMachine learning

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Area of Science:

  • Data Science
  • Computational Biology
  • Pharmacology

Background:

  • Novartis and Oxford's Big Data Institute (BDI) formed a research alliance to enhance healthcare and drug development.
  • The alliance aims to leverage advanced statistical machine learning and an innovative IT platform for efficient and targeted health solutions.

Purpose of the Study:

  • To identify novel, clinically relevant patterns undetectable by humans alone.
  • To discover phenotypes and early predictors of patient disease activity and progression using big data analytics.

Main Methods:

  • Development of a computational framework integrating diverse data modalities for complex autoimmune diseases.
  • Anonymization and integration of clinical and imaging trial data from over 35,000 Multiple Sclerosis patients (Phase II-IV).
  • Anonymization and integration of clinical and imaging data from over 15,000 patients across four autoimmune disorders in 30+ Cosentyx trials (Phase II-III).

Main Results:

  • Construction of a research informatics framework enabling collaborative data management and analysis.
  • The framework captures, anonymizes, quality-controls, and integrates diverse clinical trial data into a research-ready relational database.
  • Facilitation of collaborative data management, making complex pharmaceutical clinical trial data accessible to academic researchers.

Conclusions:

  • An informatics framework was successfully developed for clinical trial data processing, including anonymization, quality control, and database integration.
  • This framework is crucial for developing advanced analytical tools and facilitating collaborative research between industry and academia.