Preface
I. Cluster Models
1. Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys
2. Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data
3. Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships
II. Linear Models
4. Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data
5. Generalized Linear Models for Outcome Prediction with Paired Data
6. Generalized Linear Models for Predicting Event-Rates
7. Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction
8. Optimal Scaling of High-sensitivity Analysis of Health Predictors
9. Discriminant Analysis for Making a Diagnosis from Multiple Outcomes
10. Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread
11. Partial Correlations for Removing Interaction Effects from Efficacy Data
12. Canonical Regression for Overall Statistics of Multivariate Data
III. Rules Models
13. Neural Networks for Assessing Relationships that are Typically Nonlinear
14. Complex Samples Methodologies for Unbiased Sampling
15. Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple
Groups
16. Decision Trees for Decision Analysis
17. Multidimensional Scaling for Visualizing Experienced Drug Efficacies
18. Stochastic Processes for Long Term Predictions from Short Term Observations
19. Optimal Binning for Finding High Risk Cut-offs
20. Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to be Developed
Index.
