Comparing linear regression and quantile regression to analyze the associated factors of length of hospitalization in patients with gastrointestinal tract cancers

Authors

  • Mohamad Amin Pourhoseingholi Research Center of Gastroenterology and Liver diseases, Shahid Beheshti University, (M.C), Tehran, Iran
  • Mohsen Vahedi Research Center of Gastroenterology and Liver diseases, Shahid Beheshti University, (M.C), Tehran, Iran
  • Asma Pourhoseingholi Research Center of Gastroenterology and Liver diseases, Shahid Beheshti University, (M.C), Tehran, Iran
  • Bijan Moghimi-Dehkordi Research Center of Gastroenterology and Liver diseases, Shahid Beheshti University, (M.C), Tehran, Iran
  • Azadeh Safaee Research Center of Gastroenterology and Liver diseases, Shahid Beheshti University, (M.C), Tehran, Iran
  • Elham Maserat Research Center of Gastroenterology and Liver diseases, Shahid Beheshti University, (M.C), Tehran, Iran
  • Fatemeh Ghafarnejad Research Center of Gastroenterology and Liver diseases, Shahid Beheshti University, (M.C), Tehran, Iran
  • Mohammad Reza Zali Research Center of Gastroenterology and Liver diseases, Shahid Beheshti University, (M.C), Tehran, Iran

DOI:

https://doi.org/10.2427/5787

Keywords:

Quantile regression, linear regression, GI cancers, length of hospitalization

Abstract

Objectives: The aim of this study is to compare linear regression and quantile regression to analyze the predictors of duration of staying in hospital for patients with GI cancers.

Methods: This study was designed as a retrospective cross-sectional survey included all consecutive GI cancer patients admitted over one year period in a random selected hospital group located in Tehran metropolitan in 2006. Residence, age, sex and type of cancer were analyzed respectively using linear and quantile regression.

Results: A total of 2,674 GI tract cancer patients were included in the study. There were 1,616 men (60.43%) and 1,058 women (39.57%). Results of the linear regression analyses showed that only type of cancer was significant. The diagnostic criteria showed that only 7% of variation has been predicted by linear regression. In spite of linear regression sex and type of hospital were significance in quantile regression analysis.

Conclusions: The results have demonstrated that if the duration data showed major skewness, using quantile
regression leads to better interpretation and richer inference.

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Published

2024-04-18

Issue

Vol. 6 No. 2 (2009)

Section

Theme Papers