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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Generating and using real-world data: A worthwhile uphill battle.

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This summary is machine-generated.

Precision oncology needs new methods. Real-world data (RWD) can address challenges in clinical trials by providing accessible datasets for biomarker validation and evaluating drug value.

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Area of Science:

  • Oncology
  • Biostatistics
  • Health Informatics

Background:

  • Precision oncology faces challenges with traditional clinical trials regarding feasibility and data generalizability.
  • There is a need for practical research methods to test diverse patient subgroups and assess real-world drug effectiveness.
  • Real-world data (RWD) offers a potential solution for generating comprehensive datasets and validating biomarkers.

Purpose of the Study:

  • To explore the potential of leveraging real-world data (RWD) to advance precision oncology.
  • To highlight the benefits and challenges associated with using RWD in oncology research.
  • To suggest a path forward for integrating RWD into oncology trial design and drug evaluation.

Main Methods:

  • This perspective reviews existing literature and discusses the established and potential applications of RWD in oncology.
  • It examines the concerns and limitations hindering the broader adoption of RWD, including data quality, privacy, and biases.
  • The discussion focuses on how purpose-built RWD repositories can support biomarker discovery and validation.

Main Results:

  • RWD can supplement traditional clinical trials, enabling conditional reimbursement and accelerated drug access.
  • RWD facilitates innovative trial designs and supports the extension or refinement of drug indications.
  • Purpose-built RWD repositories are crucial for discovering and validating novel biomarkers in oncology.

Conclusions:

  • Real-world data holds significant potential to overcome limitations in current oncology research methodologies.
  • Addressing concerns regarding RWD quality, privacy, and bias is essential for its effective implementation.
  • Strategic utilization of RWD can significantly advance precision oncology, biomarker discovery, and drug evaluation.