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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
A review for clinical outcomes research: hypothesis generation, data strategy, and hypothesis-driven statistical
David C Chang1, Mark A Talamini
1Department of Surgery, University of California San Diego, 200 Arbor Drive, #8400, San Diego, CA 92103, USA. dchang1@ucsd.edu
Surgical Endoscopy
|March 2, 2011
Summary
Large clinical research databases present challenges. A structured outcomes research protocol can systematically guide investigators through complex data, enhancing manuscript evaluation.
Area of Science:
- Clinical Research
- Health Informatics
- Data Science
Background:
- Increasing availability of large, population-level databases for clinical research.
- Methodological complexities inherent in analyzing large-scale health data.
- Need for structured approaches to manage and interpret complex datasets.
Purpose of the Study:
- To propose a standardized "protocol" to facilitate research using large clinical databases.
- To enhance the systematic evaluation of outcomes research manuscripts.
- To provide a framework for investigators navigating complex data.
Main Methods:
- Conceptual proposal of a formal outcomes research protocol.
- Analogy drawn to the structured History and Physical (H&P) in patient case evaluation.
- Emphasis on systematic methodology for data analysis and manuscript preparation.
Main Results:
- The proposed protocol offers a structured approach to address methodological challenges.
- Systematic evaluation of outcomes research manuscripts is facilitated by the protocol.
- Improved clarity and reproducibility in research using large databases.
Conclusions:
- A formal outcomes research protocol is essential for efficient and rigorous studies.
- Protocols enhance the systematic analysis and interpretation of large clinical datasets.
- Standardized protocols improve the quality and impact of published outcomes research.
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