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TMS: Using the Theta-Burst Protocol to Explore Mechanism of Plasticity in Individuals with Fragile X Syndrome and Autism
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Comment on Oberman & Vink: Should we fix or simulate the complete data in simulation studies evaluating missing data

Tim P Morris1, Ian R White1, Suzie Cro2

  • 1MRC Clinical Trials Unit at UCL, University College London, London, UK.

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|October 12, 2023
PubMed
Summary
This summary is machine-generated.

Generating partially observed data by simulating missingness indicators is rarely appropriate for missing data handling simulation studies. This method, while seemingly attractive, often fails to accurately reflect real-world data complexities.

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Area of Science:

  • Statistics
  • Data Science
  • Computational Statistics

Background:

  • Simulation studies are crucial for evaluating statistical methods, particularly for handling missing data.
  • A common approach involves generating partially observed data from complete datasets.
  • Simulating missingness indicators after fixing complete data is a frequently considered, yet often flawed, technique.

Discussion:

  • The method of fixing complete data and simulating missingness indicators can lead to biased evaluations of missing data handling techniques.
  • This approach may not accurately represent the mechanisms that cause data to be missing in real-world scenarios.
  • Researchers must carefully consider the appropriateness of this simulation strategy to avoid misleading conclusions.

Key Insights:

  • Generating partially observed data by simulating missingness indicators is only rarely appropriate for missing data simulation studies.
  • This simulation technique can produce superficially attractive but ultimately misleading results.
  • The validity of simulation studies hinges on the accurate representation of missing data mechanisms.

Outlook:

  • Future research should focus on developing and validating more appropriate simulation strategies for missing data.
  • Emphasize the importance of understanding and correctly modeling missing data mechanisms in simulation studies.
  • Promote rigorous methodological standards for evaluating statistical techniques in the presence of missing data.