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12.3.2 Accelerated failure time model (AFT) models.12.3.1 Proportional hazard parametric models.12.3 Parametric survival analysis model.12.2.1 Advantages of parametric survival analysis models.11.14 The proportional hazard assumption.11.11 Estimation from Cox proportional hazards regression.11.10.2 Advantages of the Cox proportional hazards regression.11.10.1 Cox proportional hazards regression.11.10 Semi-parametric models in survival analysis.11.9 Comparing Kaplan-Meier estimates across groups.11 Survival Analysis: Kaplan-Meier and Cox Proportional Hazard (PH) Regression.10.6 Quasi-Poisson Regression for Overdispersed Data.10.5.1 About Poisson regression for rate.10.4.1 About Poisson regression for count.10.3 Prepare R Environment for Analysis.9.11 Presentation of multinomial regression model.9.7.3 Model with interaction term between independent variables.9.6.5 Create new categorical variable from fbs.9.5 Estimation for Multinomial logit model.9.4 Models for multinomial outcome data.9.3 Examples of multinomial outcome variables.8.18 Presentation of logistic regression model.8.16 Prediction from binary logistic regression.8.12 Convert the log odds to odds ratio.8.11 Multiple binary logistic regression.6.6.4 Three variables: Plotting a numerical and two categorical variables.