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Related Experiment Video

Updated: Nov 2, 2025

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
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Quasi-least squares regression method with dentistry data.

Erdogan Asar1, Erdem Karabulut2

  • 1Department of Statistical Consultancy Turkish Statistical Institute, TurkStat Presidency, Ankara, Turkey.

Nigerian Journal of Clinical Practice
|June 14, 2021
PubMed
Summary
This summary is machine-generated.

This study found no significant difference between single-jaw and double-jaw surgery for correcting skeletal malocclusions. Quasi-least squares regression (QLS) analysis showed jaw surgery type did not impact outcome variables examined.

Keywords:
Generalized estimating equationMarkov correlation structurequasi-least squares regression

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Area of Science:

  • Orthodontics and Dental Surgery
  • Biostatistics
  • Skeletal Malocclusion Correction

Background:

  • Severe skeletal malocclusions are treated with single-jaw or double-jaw surgery.
  • Quasi-least squares regression (QLS) is a novel method for analyzing correlated data, extending generalized estimating equations (GEE).

Purpose of the Study:

  • To evaluate the effectiveness of single-jaw versus double-jaw surgery on specific cephalometric variables.
  • To assess the influence of time on these outcome variables using QLS analysis.

Main Methods:

  • Analysis of 114 lateral cephalometric radiographs from 34 adult patients (2000-2018).
  • Application of Quasi-least squares regression (QLS) and generalized estimating equations (GEE) to investigate group and time effects.
  • Evaluation of four dependent variables: C-menton distance, cervical plane angle, C-pogonion perpendicular distance, and cervical-facial plane angle.

Main Results:

  • Neither single-jaw nor double-jaw surgery demonstrated a statistically significant effect on the outcome variables.
  • The "Markov" working correlation structure yielded the highest correlation value within the QLS analysis.

Conclusions:

  • Jaw surgery type (single vs. double) was not statistically significant for the evaluated outcome variables.
  • QLS demonstrates advantages over GEE for analyzing data with unevenly spaced repeated measurements and missing observations.