Abstract
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.
Highlights
The logistic regression, analyzes the relationship between multiple independent variables and categorical dependent variables [1]
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
Summary
The logistic regression, analyzes the relationship between multiple independent variables and categorical dependent variables [1]. The multiple logistic regression models can, be stated as follows: Yi are independent Bernoulli random where: variables with expected values. The second step is to copy the original sample a number of times in order to create a pseudo-population It draws several samples considering random sampling approach providing a new comprehensive sample from the original sample. It stores the new set of data from the original dataset and creates a new distribution for further analysis [2, 3]. Ods graphics on; proc logistic descending data=cancer; model Nerv_inv(event='1') = Gen Bet Tum_site.
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