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Statistical analysis in orthodontic journals: are we ignoring confounding?

Anastasia Spanou1, Despina Koletsi2, Padhraig S Fleming3

  • 1*Laboratory of Research of the Musculoskeletal System, School of Medicine, University of Athens, Athens, Greece.

European Journal of Orthodontics
|March 6, 2015
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Summary

Few orthodontic studies adequately adjust for confounding factors in their statistical analysis or study design. This oversight, prevalent in leading journals, may lead to biased research findings and requires improved awareness and expertise in handling non-randomized studies.

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Area of Science:

  • Orthodontics
  • Biostatistics
  • Research Methodology

Background:

  • Confounding is a significant challenge in non-randomized studies, potentially biasing results.
  • Effective adjustment for confounding is crucial for the validity of research findings in orthodontics.

Purpose of the Study:

  • To evaluate the frequency of confounding adjustment in statistical analysis and design within top orthodontic journals.
  • To investigate factors associated with the reporting of confounding adjustment in orthodontic research.

Main Methods:

  • A systematic review of 426 studies from four high-impact orthodontic journals was conducted.
  • Data on confounding adjustment (statistical analysis and design matching) and publication characteristics were extracted.

Main Results:

  • Only 17% of studies statistically adjusted for confounding; 26% reported matching at the design stage.
  • Journal, country of authorship, and statistician involvement predicted statistical adjustment for confounding.
  • Overall, 41% of studies appropriately addressed confounding at either the design or analysis stage.

Conclusions:

  • A significant proportion of orthodontic research inadequately addresses confounding, indicating potential gaps in expertise.
  • This limitation can lead to biased estimates and flawed conclusions in orthodontic studies.
  • Enhancing awareness and training in handling confounding in non-randomized studies is essential for improving research quality.