Logistic regression modeling

Estimates from logistic regression models, conditioned on sex, age, study center, and adjusted for education, alcohol consumption, tobacco smoking, body mass index, and nonalcohol energy intake. b Reference category. 

Estimates from multiple logistic regression models adjusted for age, sex, study center, education, alcohol consumption, body mass index, physical activity, family history of colorectal cancer and energy intake, according to the residual model. b Reference category. 

Uchini, M., Hirano, T., Satoh, H., Nakagawa, M. and Wakamiya, J. (2005) The severity of Minamata disease declined in 25 years Temporal profile of the neurological findings analyzed by multiple logistic regression model. Tohoku J. Exp. Med. 205, 53-63. 

Figure 3. The logistic regression model used to estimate LD50 is represented on the left where 7C represents the proportion of dead plants. The logistic curve can be linearized by using the logit transformation shown on the right. LD50 values were estimated with the regression coefficients for logit 7C=0.0, as shown in the inset box.

Logistic regression modeling is used for predicting the probability of occurrence of an event by fitting data to a logistic curve.28 It describes the relationship between the categorical response variable and one or more continuous variables.29 Such a model can be described in Equation 3 ... 

When experiments results in ordinate categorical responses, say C categories, a general modeling approach based on logistic regression model can be described as... 

Table 17-2. Logistic regression model in AUC and Cmax with moribundity ...

Lee JH, Landrum PF, Field LJ, Koh CH. Application of a sigmapolycyclic aromatic hydrocarbon model and a logistic regression model to sediment toxicity data based on a species-specific, water-only LC50 toxic unit for Hyalella azteca. Environ Toxicol Chem 2001 20 2102-13. 

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