Logistic regression model for

J. Whitehead and D. WUhamson, Bayesian decision procedures based on logistic regression models for dose-finding studies. J Biopharm Stat 8 445—467 (1998). 

Table 5.12 Multivariate logistic regression models for 1SS9+, ISS16+, and ISS25+, age group 4+ (GIDAS)...

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. 

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 ... 

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. 

The theory and techniques described in this chapter focus on the application of logistic regression to binary outcome data and the development of models to describe the relationship between binary endpoints and one or more explanatory variables (covariates). While many software options are available for fitting fixed or mixed effects logistic regression models, this chapter endeavors to illustrate the use of nonlinear mixed effects modeling to analyze binary endpoint data as implemented in the NONMEM software. 

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