Logistic Regression: A Self-learning Text, Third Edition (Statistics in the Health Sciences)

Introduction In this chapter, we consider five issues on modeling Strat-
egy, which were not covered in the previous two chapters
on this topic:

1. Modeling strategy when there are two or more
   exposure variables


2. Screening variables when modeling


3. Collinearity diagnostics


4. Influential observations


5. Multiple testing

Each of these issues represent important features of any

regression analysis that typically require attention when

determining a “best” model, although our specific focus

concerns a binary logistic regression model.

Abbreviated Outline

Detailed Outline

The outline below gives the user a preview of the material

to be covered by the presentation. A detailed outline for

review purposes follows the presentation.

I. Overview (page 244)

II. Modeling strategy involving several exposure

variables (pages 244–262)

III. Screening variables (pages 263–270)

IV. Collinearity diagnostics (pages 270–275)

V. Influential observations (pages 275–279)

VI. Multiple testing (pages 280–282)

VII. Summary (pages 283–285)

242 8. Additional Modeling Strategy Issues