Superiority of Bayesian Model Averaging to Stepwise Model in Selection of Factors Related to the Incidence of Type II diabetes in Pre-diabetic Women
Archives of Advances in Biosciences,
Vol. 10 No. 1 (2019),
12 January 2019
,
Page 3-12
https://doi.org/10.22037/jps.v10i1.18370
Abstract
Introduction: The world prevalence of type 2 diabetes and its related increment mortality rate which needs high controls cost has attracted high scientific attention. Early detection of individuals who face this disease more than the others can prevent getting sick or at least reduce the disease consequences on public health. Regarding the costs and limitations of diagnostic tests, a statistical model is presented that helps predict the time of diabetes incidence and determines its risk factors. Furthermore, this model determines the significant predictor variables on response and considers them as model equation parameters.Materials and Methods: In this study, 803 pre-diabetic women in the age range of more than 20 years were selected from Tehran lipid and glucose study (TLGS) to examine the predictor variables on time of diabetes incidence. They were entered into the study in the phases 1 and 2 and were followed up to the phase 4. The predictor variables selection was performed using the Stepwise Model (SM) and the Bayesian Model Averaging (BMA). Then, the predictive discrimination was used to compare the results of both models. The Log-rank test was performed and the Kaplan-Meier Curve was plotted. The statistical analyses were performed using R software (version 3.1.3).Results: The Backward Stepwise Model (BSM), the Forward Stepwise Model (FSM) and the BMA have used 9, 10 and 6 variables, respectively. Although the BMA selected predictor variables number is much lower than the SM, the prediction ability remains nearly constant.Conclusions: The BMA has averaged on the supported models using dataset. This model has shown nearly constant accuracy despite the selection of lower predictor variables number in comparison to the SM.
- Bayesian model averaging
- stepwise methods
- Tehran Lipid and Glucose Study
- women pre-diabetic
- Cox regression
How to Cite
References
Keshavarz M, Cheung NW, Babaee GR, Moghadam HK, Ajami ME, Shariati M. Gestational diabetes in Iran: incidence, risk factors and pregnancy outcomes. Diabetes research and clinical practice. 2005;69(3):279-86.
Rashidi M, Afkhami-Ardakani M. Risk factors for type 2 diabete. Journal of Shahid Sadoughi University of Medical Sciences 2011( 19):266- 80.
Azimi-Nezhad M, Ghayour-Mobarhan M, Parizadeh M, Safarian M, Esmaeili H, Parizadeh S, et al. Prevalence of type 2 diabetes mellitus in Iran and its relationship with gender, urbanisation, education, marital status and occupation. Singapore medical journal. 2008;49(7):571.
Roche MM, Wang PP. Factors associated with a diabetes diagnosis and late diabetes diagnosis for males and females. Journal of Clinical & Translational Endocrinology. 2014;1(3):77-84.
Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes estimates for the year 2000 and projections for 2030. Diabetes care. 2004;27(5):1047-53.
Hadaegh F, Shafiee G, Ghasemi A, Sarbakhsh P, Azizi F. Impact of metabolic syndrome, diabetes and prediabetes on cardiovascular events: Tehran lipid and glucose study. Diabetes research and clinical practice. 2010;87(3):342-7.
Annest A, Bumgarner RE, Raftery AE, Yeung KY. Iterative bayesian model averaging: A method for the application of survival analysis to high-dimensional microarray data. BMC bioinformatics. 2009;10(1):1.
Wiegand RE. Performance of using multiple stepwise algorithms for variable selection. Statistics in medicine. 2010;29(15):1647-59.
Amini SM, Parmeter CF. Bayesian model averaging in R. Journal of Economic and Social Measurement. 2011;36(4):253-87.
Volinsky CT. Bayesian model averaging for censored survival models: University of Washington; 1997.
Genell A, Nemes S, Steineck G, Dickman PW. Model selection in medical research: a simulation study comparing Bayesian model averaging and stepwise regression. BMC medical research methodology. 2010;10(1):108.
TAY PL. Iterative Bayesian Model Averaging For Patients Survival Analysis: Universiti Teknologi Malaysia; 2010.
Azizi F, Madjid M, Rahmani M, Emami H, Mirmiran P, Hadjipour R. Tehran Lipid and Glucose Study (TLGS): rationale and design. Iranian journal of endocrinology and metabolism. 2000;2(2):77-86.
Bagherzadeh-Khiabani F, Ramezankhani A, Azizi F, Hadaegh F, Steyerberg EW, Khalili D. A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results. Journal of clinical epidemiology. 2016;71:76-85.
Kleinbaum DG, Klein M. Survival analysis: a self-learning text. Springer Science & Business Media; 2006.
Madigan D, Raftery AE. Model selection and accounting for model uncertainty in graphical models using Occam's window. Journal of the American Statistical Association. 1994;89(428):1535-46.
- Abstract Viewed: 347 times
- PDF Downloaded: 540 times