Titlebook: Logistic Regression; A Self-Learning Text David G. Kleinbaum Textbook 19941st edition Springer Science+Business Media New York 1994 class.d

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Important Special Cases of the Logistic Model,ding odds ratio expressions. In particular, focus is on defining the independent variables that go into the model and on computing the odds ratio for each special case. Models that account for the potential confounding effects and potential interaction effects of covariates are emphasized.

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Maximum Likelihood Techniques: An Overview, We also distinguish between two alternative ML methods, called the unconditional and the conditional approaches, and we give guidelines regarding how the applied user can choose between these methods. Finally, we provide a brief overview of how to make statistical inferences using ML estimates.

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Analysis of Matched Data Using Logistic Regression,stratification to carry out a matched analysis. Our primary focus is on case-control studies. We then introduce the logistic model for matched data and describe the corresponding odds ratio formula. Finally, we illustrate the analysis of matched data using logistic regression with an application tha

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Computing the Odds Ratio in Logistic Regression,In this chapter, the .. is extended to consider other coding schemes for a single exposure variable, including ordinal and interval exposures. The model is further extended to allow for several exposure variables. The formula for the odds ratio is provided for each extension, and examples are used to illustrate the formula.

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