Volume 14, Issue 4 (Vol.14, No.4, 2019)
irje 2019, 14(4): 359-365 |
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Dehghani S, Abadi A, Namdari M, Ghorbani Z. Comparing Multi-level and Ordinary Logistic Regression Models in Evaluating Factors Related to Periodontal Clinical Attachment Loss. irje 2019; 14 (4) :359-365
URL: http://irje.tums.ac.ir/article-1-6199-en.html
URL: http://irje.tums.ac.ir/article-1-6199-en.html
1- MSc Student of Biostatistics, Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2- PhD, Professor of Biostatistics, Department of Community Medicine, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Social Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ,alirezaabadi@gmail.com
3- PhD, Assistant Professor of Biostatistics, Community Oral Health Department, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4- DDS, PhD, Assistant Professor, Community Oral Health Department, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2- PhD, Professor of Biostatistics, Department of Community Medicine, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Social Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ,
3- PhD, Assistant Professor of Biostatistics, Community Oral Health Department, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4- DDS, PhD, Assistant Professor, Community Oral Health Department, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract: (3809 Views)
Background and Objectives: Periodontal disease is one of the most common oral health problems. Clinical attachment loss occurs in sever periodontal cases (CAL>3). In this study, we applied a classic regression model and the models that consider the hierarchical structure of the data to estimate and compare the effect of different factors on CAL.
Methods: This cross-sectional study was performed in 375 pregnant women and 192 mothers of three-year-old children. The data were gathered from 16 health networks of Shahid Beheshti University of Medical Sciences, Tehran, Iran. CAL was determined for 6 teeth per person by a dentist according to WHO standard oral health examination form. Three-level and ordinary logistic regression analyses were applied for data analysis using the STATA software 14.
Results: Of 3,402 examined teeth, 6.3% had CAL> 3mm. Based on the obtained results, the odds of CAL>3mm were 2.4 in the third semester compared to non-pregnant women. The odds of CAL>3mm were 2.86 in women without daily floss use compared to women with routine daily floss use. Posterior teeth were more likely to have CAL>3m than anterior teeth (OR = 1.65) (P-value < 0.05).
Conclusion: According to the AIC index, multi-level logistic regression model has a better fit than ordinary logistic regression model and can estimate the coefficients of factors related to CAL>3mm more precisely. The use of the ordinary logistic regression model in hierarchical data can result in underestimated standard errors of the estimated parameters.
Methods: This cross-sectional study was performed in 375 pregnant women and 192 mothers of three-year-old children. The data were gathered from 16 health networks of Shahid Beheshti University of Medical Sciences, Tehran, Iran. CAL was determined for 6 teeth per person by a dentist according to WHO standard oral health examination form. Three-level and ordinary logistic regression analyses were applied for data analysis using the STATA software 14.
Results: Of 3,402 examined teeth, 6.3% had CAL> 3mm. Based on the obtained results, the odds of CAL>3mm were 2.4 in the third semester compared to non-pregnant women. The odds of CAL>3mm were 2.86 in women without daily floss use compared to women with routine daily floss use. Posterior teeth were more likely to have CAL>3m than anterior teeth (OR = 1.65) (P-value < 0.05).
Conclusion: According to the AIC index, multi-level logistic regression model has a better fit than ordinary logistic regression model and can estimate the coefficients of factors related to CAL>3mm more precisely. The use of the ordinary logistic regression model in hierarchical data can result in underestimated standard errors of the estimated parameters.
Type of Study: Research |
Subject:
General
Received: 2019/04/16 | Accepted: 2019/04/16 | Published: 2019/04/16
Received: 2019/04/16 | Accepted: 2019/04/16 | Published: 2019/04/16
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