Statistical Modeling via Bootstrapping and Weighted Techniques Based on Variances


  • W. M. A. W. Ahmad School of Dental Sciences, Universiti Sains Malaysia, Malaysia
  • N. A. Aleng School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia
  • Z. Ali School of Mathematical Sciences, Universiti Sains Malaysia, Malaysia
  • M. S. M. Ibrahim School of Dental Sciences, Universiti Sains Malaysia, Malaysia
Volume: 8 | Issue: 4 | Pages: 3135-3140 | August 2018 |


Multiple logistic regression is a methodology of handling dependent variables with a binary outcome. This method is becoming increasingly widespread as a statistical technique that represents a discrete probability model. Many studies have focused on the application but less on the methodology building. This study aims to provide an applied method for multiple logistic regression which is called modified Bayesian logistic regression modeling as an alternative technique for logistic regression analysis that focuses on a combination of the bootstrap method using SAS macro and weighted techniques based on variances using SAS algorithm. Data on oral cancer were applied to illustrate a real scenario of oral health data. This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. Results from both cases are strongly supported by clinical studies. Through the proposed algorithm, the researcher will have an option whether to analyze the data with the usual or an alternative method. Final results indicate that the modified procedure can provide more efficient results especially for the case which involves statistical inferences.


multiple logistic regression, bootstrap, Bayesian and weighted techniques


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D. W. Hosmer, S. Lemeshow, R. X. Sturdivant, Applied Logistic Regression, 3rd ed, John Wiley & Sons, 2013 DOI:

B. Efron, R. J. Tibshirani, An Introduction to the Bootstrap, Chapman and Hall/CRC, 1993 DOI:

G. E. Higgins, “Statistical Significance Testing: The Bootstrapping Method and an Application to Self-Control Theory”, The Southwest Journal of Criminal Justice, Vol. 2, No. 1, pp. 54-76, 2005

A. Gelman, J. B Carlin, H. S. Stern, D. B. Rubin, Bayesian Data Analysis, Chapman and Hall/CRC, 2004 DOI:

M. Stokes, F. Chen, F. Gunes, “An Introduction to Bayesian Analysis with SAS/STAT Software”, SAS Global Forum 2014 Conference, Washington DC, USA, Paper SAS400-2014, March 23-26, 2014

J. Mickey, S. Greenland, “The Impact of Confounder-Selection Criteria on Effect Estimation”, American Journal of Epidemiology, Vol. 129, No. 1, pp 125-137, 1989 DOI:

I. Leibovitch, S. C. Huilgol, D. Selva, S. Richards, R. Paver, “Basal cell carcinoma treated with Mohs surgery in Australia III. Perineural invasion”, Journal of the American Academy of Dermatology, Vol. 53, No. 3, pp. 458-463, 2005 DOI:

C. T. Liao, C. J. Tung-Chieh, H. M. Wang, I. H. Chen, C. Y. Lin, T. M. Chen, L. L. Hsieh, A. J. Cheng, “Telomerase as an independent prognostic factor in head and neck squamous cell carcinoma”, Head & Neck, Vol. 26, No. 6, pp. 504–512, 2004 DOI:

C. T. Liao, J. T. Chang, H. M. Wang, S. H. Ng, C. Hsueh, L. Y. Lee, C. H. Lin, I. H. Chen, S. F. Huang, A. J. Cheng, T. C. Yen, “Analysis of risk factors predictive of local tumor control in oral cavity cancer”, Annals of Surgical Oncology, Vol. 15, No. 3, pp. 915–922, 2008 DOI:

C. T. Liao, C. J. Kang, J. T. Chang, H. M. Wang, S. H. Ng, C. Hsueh, L. Y. Lee, C. H. Lin, A. J. Cheng, I. H. Chen, S. F. Huang, T.C. Yen, “Survival of second and multiple primary tumors in patients with oral cavity squamous cell carcinoma in the betel quid chewing area”, Oral Oncology, Vol. 43, No. 8, pp. 811–819, 2007 DOI:


How to Cite

W. M. A. W. Ahmad, N. A. Aleng, Z. Ali, and M. S. M. Ibrahim, “Statistical Modeling via Bootstrapping and Weighted Techniques Based on Variances”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 4, pp. 3135–3140, Aug. 2018.


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