- Chapter 4: Linear regression. Reviewing the basic properties of the multiple linear regression model; Testing for trend and association between multiple categorical predictors; Discussion about confounding, mediation and interaction (rarely found in other textbooks!); Checking model assumption (linearity, normality, homoscedasticity, outlying, transformation) and model fit./li>
- Chapter 5: Predictor selection. Causal model and rules (e.g. RMSE, CART) for selecting single or multiple predictor of interest; Dealing with collinearity and Backward selection; Assessing model selection./li>
- Chapter 6: Logisitic regression. Use of single and multiple predictors with a binary response; Case-control studies and matched pairs analysis; Checking logistic model fit; Alternatives to standard logistic regression (including other response variable like relative risk, nonparametric model, ordinal outcome)./li>
- Chapter 7: Survival analysis. Overview of survival data; Cox proportional hazards model (parametric vs. semi-parametric model, binary vs. multiple response outcome, categorical vs. continuous predictor) and its extension (time-depedent covariates and stratification); Checking model fit; Other details related to censored data (bootstraped confidence interval, independent or interval censoring, left-censoring)./li>
- Chapter 8: Repeated measures analysis. Hierarchical and longitudinal design; GEE approach; Random-effects models (including prediction with binary response); Deriving confidence intervals by bootstrap./li>
- Chapter 9: Generalized linear models. Three case studies illustrating the general approach when using an GLM (specifying a linear combination of predictors related to the mean response, choosing an appropriate link function, accounting for variance-mean relationship, interpreting the parameters estimates and model fit)./li>
- Chapter 10: Complex surveys. A nice introduction to sampling issues and survey methodology; Probability weighting and variance estimation in classical survey design.
This really is a must-have one book for every practician interested in regression modelling for the life sciences.