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The recent article of Daniel Wayne has been recently published in the journal Bioestadistica. This article discusses the main questions about Current Drug Trial Analysis using Logistic Regression Modeling. It is still not argued yet if this method is actually more accurate and reliable than other methods like The Mantel-Haenszel, and Cochran-Mantel-Haenszel (CMH). Furthermore, it discussed how well these two models perform when compared to each other. The outcomes of this research indicated that CMH performed much better than Logistic Regression Modeling as it predicted number events as accurate as 70%, while Logistic Regression Modeling predicted number events as accurate as 59%. This suggests that Logistic Regression Modeling yields undesirable results when compared to the other methods.53. Daniel Wayne, "Bioestadistica", 2(1), 2009, Secretaria de Estado do Trabalho e Sociedade, Brazil(2)See the table below for the examples of three studies in this article. These studies demonstrate that logistic regression can be used in drug trials to determine how important variables become when dividing compound trials into two groups. The method is very quick and easy to use, meaning it will not cause any delays in drug development or production for pharmaceutical companies, which will allow them to invest more time and money into patient treatment processes. Furthermore, logistic regression uses the same method as the Cochran-Mantel-Haenszel (CMH) and it is suggested that (CMH) can be used to discover evidence of association between categorical and numerical variables. Moreover, CMH has been proved to be one of the most significant methods in meta-analysis; however, it has its own limitations such as: Instead of using conditional logits as dependent variable as proposed by Daniel Wayne in its article, logistic regression can also use conditional maximum likelihoods as dependent variables. In contrast, Conditional Maximum Likelihood Method does not have those restrictions mentioned before. This provides a more transparent model for readers to understand what is being done. It is known that if a variable is poorly measured, it can be ignored from any potential analysis. In addition to the previous models, there are other methods used in logistic regression such as: classification trees and generalized linear models. Classification Tree shows preference for a qualitative variable over others and it is a widely used method in prediction of survival trials. The main advantage of this method is that it allows the modeling of continuous variables as ordinal ones. On the other hand, generalized linear model provides a fit for multiple quantitative variables through the addition of the interaction effect between two or more independent variables. In summary, using auxiliary information to improve accuracy and reliability of model fit have been proven as useful tools to improve model diagnostics. The number of events or failures can be calculated by using the formula below, where formula_1 is the total of events or failures, formula_2 is the coefficient of the logistic regression model transformation, and then compare this with what was predicted by the logistic regression model. The difference then gives an indication of how well the model fits. The purpose of this article was to discuss Current Drug Trial Analysis using Logistic Regression Modeling. It also discussed how well these two models performed when compared to each other. cfa1e77820
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