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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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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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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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Data Science Methods for Real-World Evidence Generation in Real-World Data.

Fang Liu1

  • 1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA;

Annual Review of Biomedical Data Science
|May 15, 2024
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Summary

Data science methods are crucial for analyzing complex real-world data (RWD) to generate trustworthy real-world evidence (RWE). This review covers the RWE pipeline, offering guidance for selecting appropriate data science techniques.

Keywords:
RWDRWEcausal inferencedeep neural networksmachine learningreal-world datareal-world evidencestatistical inference

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Area of Science:

  • Health Informatics
  • Data Science in Healthcare
  • Real-World Evidence Generation

Background:

  • Data science (DS) methods are essential for utilizing real-world data (RWD) from diverse sources like electronic health records and digital health technologies.
  • Real-world evidence (RWE) derived from RWD enhances understanding of patient outcomes for healthcare stakeholders.
  • Challenges with RWD (e.g., complexity, heterogeneity) necessitate advanced DS methods for reliable RWE.

Purpose of the Study:

  • To systematically review common data science methods used for extracting real-world evidence from real-world data.
  • To provide a comprehensive overview of the real-world evidence generation pipeline, from study design to data ethics.
  • To assist readers, including non-experts, in selecting appropriate DS methods and improving RWE generation.

Main Methods:

  • Review of current data science methodologies applicable to real-world data analysis.
  • Coverage of the entire real-world evidence generation workflow: study design, data preprocessing, exploratory analysis, and analytical methods.
  • Inclusion of trustworthiness, reliability, data ethics, and open-source tools relevant to real-world evidence studies.

Main Results:

  • Identified common data science methods for analyzing complex and diverse real-world data.
  • Outlined key considerations for ensuring the trustworthiness and reliability of generated real-world evidence.
  • Highlighted the importance of data ethics and the utility of open-source tools in the real-world evidence pipeline.

Conclusions:

  • Effective application of data science methods is critical for overcoming real-world data challenges and generating robust real-world evidence.
  • This review provides a foundational understanding of data science techniques for real-world evidence generation, aiding researchers and clinicians.
  • Guidance on method selection and ethical considerations supports the advancement of real-world evidence utilization in healthcare.