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Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Recommendations for capture, validation and summarisation of data from studies using RECIST.

Melanie Paules¹, Michelle Casey, Grant Williams

¹GlaxoSmithKline, Collegeville, PA 19426-0989, USA. Melanie.a.paules@gsk.com

European Journal of Cancer (Oxford, England : 1990)

|February 9, 2011

Summary

Managing data for solid tumor lesion and response assessment in oncology clinical trials using Response Evaluation Criteria in Solid Tumors (RECIST) can be complex. This article offers practical recommendations for data capture, validation, and summarization to improve consistency.

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Introductory Analysis and Validation of CUT&RUN Sequencing Data

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Oncology
Clinical Trials
Data Management

Background:

Response Evaluation Criteria in Solid Tumors (RECIST) is a standard for assessing cancer treatment efficacy in clinical trials.
Current data capture, validation, and summarization processes for RECIST data are complex and can be challenging to manage.
Harmonizing these processes is crucial for consistent and reliable trial outcomes.

Purpose of the Study:

To provide a comprehensive guide for managing solid tumor lesion and response assessment data in oncology clinical trials.
To outline best practices for data capture, validation, and summarization based on real-world company experience.
To promote harmonization of RECIST data management practices across the industry.

Main Methods:

Review of data capture forms, including specific data items and instructions used in solid tumor studies.
Analysis of data validation practices and common challenges encountered.
Examination of algorithms and methodologies for summarizing RECIST data from clinical trials.

Main Results:

Development of standardized conventions for capturing essential lesion data.
Identification of key considerations and potential pitfalls in data validation.
Establishment of clear algorithms for efficient and accurate data summarization.

Conclusions:

Implementing standardized data capture, validation, and summarization improves RECIST data management.
Sharing these methods facilitates harmonization and enhances the reliability of oncology clinical trial results.
These recommendations aim to streamline RECIST data handling for more efficient treatment evaluation.