Cluster Models
1 Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients)
2 Density-based Clustering to Identify Outlier Groups in Otherwise
Homogeneous Data (50 Patients)
3 Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients)
Linear Models
4 Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data (20, 55, and 60 Patients)
5 Generalized Linear Models for Outcome Prediction with Paired
Data (100 Patients and 139 Physicians)
6 Generalized Linear Models for Predicting Event-Rates (50 Patients)
Exact P-Values 7 Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction (250 Patients)
8 Optimal Scaling of High-sensitivity Analysis of Health Predictors
(250 Patients)
9 Discriminant Analysis for Making a Diagnosis from
Multiple Outcomes (45 Patients)
10 Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread (78 Patients)
11 Partial Correlations for Removing Interaction Effects from
Efficacy Data (64 Patients)
12 Canonical Regression for Overall Statistics of Multivariate Data (250 Patients) Rules Models
13 Neural Networks for Assessing Relationships that are Typically Nonlinear (90 Patients)
14 Complex Samples Methodologies for Unbiased Sampling
(9,678 Persons)
15 Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups (217 Patients) 16 Decision Trees for Decision Analysis (1004 and 953 Patients)
17 Multidimensional Scaling for Visualizing Experienced Drug Efficacies (14 Pain-killers and 42 Patients)
18 Stochastic Processes for Long Term Predictions from Short
Term Observations
19 Optimal Binning for Finding High Risk Cut-offs (1445 Families) 20 Conjoint Analysis for Determining the Most Appreciated
Properties of Medicines to Be Developed (15 Physicians)
Index
