Volume 9, Issue 2 (Spring 2021)
Iran J Health Sci 2021, 9(2): 9-17 |
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Sheykholeslami A S, Behnampour N, Mohammadpour R A, Abdollahi F. Application of Survival Tree Model in Determining Affecting Factors in Breastfeeding Duration. Iran J Health Sci 2021; 9 (2) :9-17
URL: http://jhs.mazums.ac.ir/article-1-731-en.html
URL: http://jhs.mazums.ac.ir/article-1-731-en.html
Department of Biostatistics, Faculty of Health , Mohammadpour2002@yahoo.com
Abstract: (1907 Views)
Background and Purpose: Survival tree model is a nonparametric method which can be used to identify the affecting factors from a specific time to the onset of an event. In this method, the categories are selected according to the most important factors. The purpose of this study was to determine the factors affecting the duration of breastfeeding in mothers and introduce the homogeneous subgroups using a survival tree model.
Methods: It was a historical cohort study analyzing the survival data of mothers with healthy single childbirths referring to the rural and urban health centers of Agh-Ghala County since 2011 until 2014. Data analyses and groupings of breastfeeding survival were performed using survival tree model with conditional inference algorithm in R Software. A separation criterion (SEP) confirmed the relevance of the model.
Methods: It was a historical cohort study analyzing the survival data of mothers with healthy single childbirths referring to the rural and urban health centers of Agh-Ghala County since 2011 until 2014. Data analyses and groupings of breastfeeding survival were performed using survival tree model with conditional inference algorithm in R Software. A separation criterion (SEP) confirmed the relevance of the model.
Results: Survival tree model results revealed that the type of consumed milk with the complementary nutrition, ethnicity and the time interval between current childbirth and the previous delivery were the most important factors affecting the duration of breastfeeding. The SEP's criterion was 2.082. Thus, due to the significant difference between the subgroups and the value of more than 1 for SEP criterion, the efficiency of the model was confirmed.
Conclusions: Survival tree model could be introduced as a suitable and powerful method for ranking the duration of breastfeeding rate which presents four homogeneous subgroups for analysis in addition to identifying the predictive variables.
Keywords: Survival Tree, Conditional Inference Algorithm, Homogeneous Subgroups, Duration of Breastfeeding
Type of Study: Original Article |
Subject:
Biostatistics
References
1. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied linear statistical models. Vol 4: Irwin Chicago; 1996.
2. Kleinbaum DG, Klein M. Survival analysis. Vol 3: Springer; 2010.
3. Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and regression trees. CRC press; 1984.
4. Segal MR. Regression trees for censored data. Biometrics 1988; 44(1):35- 47. [DOI:10.2307/2531894]
5. LeBlanc M, Crowley J. Relative risk trees for censored survival data. Biometrics. 1992:411-25. [DOI:10.2307/2532300] [PMID]
6. Banerjee M, Noone AM. Advances in the Biomedical Sciences. New Jersey: John Wiley& Sons; 2008.
7. Noon AM, Banerjee M. Computational Methods in Biomedical Research. 2008:77-101. [DOI:10.1201/9781420010923.ch3]
8. Zhou Y, McArdle JJ. Rationale and applications of survival tree and survival ensemble methods. Psychometrika. 2015;80(3):811-833. [DOI:10.1007/s11336-014-9413-1] [PMID] [PMCID]
9. Bou-Hamad I, Larocque D, Ben-Ameur H. A review of survival trees. Stat Surv. 2011; 5:44-71. [DOI:10.1214/09-SS047]
10. World Health Organization, Global Strategy on Infant and Young Child Feeding, World Health Organization, Geneva, Switzerland, 2003.
11. Robert E, Coppieters Y, Swennen B and Dramaix M. Breastfeeding Duration: A Survival Analysis-Data from a Regional Immunization Survey. BioMed Research International. Volume 2014, Article ID 529790, 8 pages. [DOI:10.1155/2014/529790] [PMID] [PMCID]
12. World Health Organization. Guiding principles for complementary feeding of the breastfed child. Division of Health Promotion and Protection. Geneva. 2003. available at http://apps.who.int/iris/ bitstream/10665/42590/1/9241562218.pdf
13. Kasahun A W, Wako W G, Gebere M V and Neima G H. Predictors of exclusive breastfeeding duration among 6-12 month aged children in gurage zone, South Ethiopia: a survival analysis. International Breastfeeding Journal. 2017 [DOI:10.1186/s13006-017-0107-z] [PMID] [PMCID]
14. Teresa S, Abada J, Trovato F, Lalu N. Determinants of breast feeding in the Philippines: a survival analysis. Soc Sci Med. 2001; 52:71-81. [DOI:10.1016/S0277-9536(00)00123-4]
15. Chaves RG, Lamounier JA, César CC. Factors associated with duration of breastfeeding. J Pediatr (Rio J). 2007;83(3):241-246.
https://doi.org/10.1590/S0021-75572007000400009 [DOI:10.2223/JPED.1610]
16. Mohamadpour R, Behnampour B, abdollahi F, Sheykholeslami A, Mehrbakhsh Z, Barzanuni S. Determination of effective factors in breastfeeding duration using survival analysis. J Res Dev Nurs Midwifery. 2017;14(2):45-50. [DOI:10.29252/jgbfnm.14.2.45]
17. Schumacher M, Holl¨ander N, Schwarzer G, Sauerbrei W. Handbook of Statistics in Clinical Oncology. In J Crowley (ed.), Prognostic Factor Studies. New York: Marcel Dekker Basel; 2001
18. Zhang H, Singer B. Recursive partitioning in the health sciences: Springer Science & Business Media; 2013.
19. Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical statistics. 2006;15(3):651-674. [DOI:10.1198/106186006X133933]
20. Holfprd TR. Multivarite Methods in Epidemiology. New York: Oxford University Press; 2002.
21. Hothorn T. "Package party. 2009; Available from: htpp://carn.R-projec.org
22. Hothorn T. Hornik K. Strobl C. Zeileis A. Package 'party' Version 1.3-7: A Laboratory for Recursive Partitioning. URL http://party.R-forge.R-project.org 2021-03-03 17:10:13 UTC
23. Schlosser L, Hothorn T, Zeileis A (2019). "The Power of Unbiased Recursive Partitioning: A Unifying View of CTree, MOB and GUIDE", arXiv:1906.10179, arXiv.org E-Print Archive. https://arXiv. org/abs/1906.10179
24. Tarone RE, Ware J. On distribution-free tests for equality of survival distributions. Biometrika 1977; 64: 156-60. Google Scholar, Crossref [DOI:10.1093/biomet/64.1.156]
25. Saki Malehi A, Hajizadeh E, Fatemi R. Evaluation of prognostic variables for classifying the survival in colorectal patients using the decision tree. Iranian Journal of Epidemiology. 2012;8(2):13-19.
26. Parizadeh D, Ramezankhani A, Momenan AA, Azizi F, Hadaegh F. Exploring risk patterns for incident ischemic stroke during more than a decade of follow-up: a survival tree analysis. Computer methods and programs in biomedicine. 2017; 147:29-36. [DOI:10.1016/j.cmpb.2017.06.006] [PMID]
27. Ramezankhani A, Tohidi M, Azizi F, Hadaegh F. Application of survival tree analysis for exploration of potential interactions between predictors of incident chronic kidney disease: a 15-year follow-up study. Journal of translational medicine. 2017;15(1):240. [DOI:10.1186/s12967-017-1346-x] [PMID] [PMCID]
28. Valera VA, Walter BA, Yokoyama N, et al. Prognostic groups in colorectal carcinoma patients based on tumor cell proliferation and classification and regression tree (CART) survival analysis. Annals of surgical oncology. 2007;14(1):34-40. [DOI:10.1245/s10434-006-9145-2] [PMID]
29. Shimokawa A, Kawasaki Y, Miyaoka E. Comparison of splitting methods on survival tree. Int J Biostat. 2015 May;11(1):175-88. [DOI:10.1515/ijb-2014-0029] [PMID]
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