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

Because CAT is the exposure variable, we must leave CAT in all further models regardless of the hierarchy principle. In addition ...

In scanning the above table, it is seen for each coefficient separately (that is, by looking at the values in a given column) th ...

For example, if CHL equals 200 and HPT equals 0, the computed odds ratio is 3.16, whereas if CHL equals 220 and HPT equals 1, th ...

Note that because we cannot drop either of the variables AGE, SMK, or ECG as nonconfoun- ders, we do not need to consider possib ...

Tests based on CIs aretwo-tailed In EPID, most tests of E–D relationship areone-tailed One-tailed tests: Use large sample Z¼ est ...

VI. SUMMARY Chap. 6  Overall guidelines for three stages  Focus: variable specification  HWF model Chap. 7 Focus: interaction ...

Chapters Introduction Special Cases   3 7. Interaction and Confounding Assessment Additional Modeling Strategy Issues This ...

Detailed Outline Abbreviated Outline I. Overview(pages 206–207) Focus:  Assessing confounding and interaction  Obtaining a val ...

D. Control for the subset that gives largest gain in precision, i.e., tighter confidence interval around odds ratio. E. Example. ...

Suppose the following initial model is specified for assessing the effect of type of hospital (HT), consid- ered as the exposur ...

briefly how you would assess whether the variable CT needs to be controlled for precision reasons. What problems are associated ...

the end of the interaction stage? Which of theVvari- ables in the model cannot be deleted from any further models considered? Ex ...

Using a backward elimination procedure, one first determines which of the two product terms HT AGE and HT SEX is the least si ...

should be retained in the model if precision is not gained by dropping them. The odds ratio formula is given by exp(bþd 1 AGEþ ...

8 Additional Modeling Strategy Issues n Contents Introduction 242 Abbreviated Outline Objectives Presentation Detailed Outline A ...

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

Objectives Upon completing this chapter, the learner should be able to: Given a binary logistic model involving two or more exp ...

Presentation I. Overview Focus Modeling issues not considered in previous chapters Goal: determine “best” model Binary logistic ...

EXAMPLE (continued) Outcome:D¼MRSA status (0¼no, 1¼yes) Predictors: PREVHOSP (0¼no, 1¼yes) PAMU (0¼no, 1¼yes) AGE (continuous) G ...

EXAMPLE Our example assumes:  AGE and GENDER risk factors  AGE and GENDER potential effect modifiers of interest  Interaction ...