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

(vip2019) #1
The reason why the model contains all fiveVs
at this point is because we have not yet done
any analysis to evaluate which of theVs can be
eliminated from the model.

However, because we have found two signifi-
cant interaction terms, we need to use the hier-
archy principle to identify certain Vs that
cannot be eliminated from any further models
considered.

The hierarchy principle says that all lower order
components of significant product terms must
remain in all further models.

In our example, the lower order components of
CATCHL are CAT and CHL, and the lower
order components of CATHPT are CAT and
HPT. Now the CAT variable is our exposure
variable, so we will leave CAT in all further
models regardless of the hierarchy principle.
In addition, we see that CHL and HPT must
remain in all further models considered.

This leaves theVvariables AGE, SMK, and
ECG as still being eligible for elimination at
the confounding stage of the strategy.

As we show in Chap. 7, we will not find sufficient
reason to remove any of the above three vari-
ables as nonconfounders. In particular, we will
show that decisions about confounding for this
example are too subjective to allow us to drop
any of the threeVterms eligible for elimination.

Thus, as a result of our modeling strategy, the
final model obtained contains the variables
CAT, AGE, CHL, SMK, ECG, and HPT as
main effect variables, and it contains the two
product terms CATCHL and CATHPT.

EXAMPLE (continued)
All fiveVs in model so far

Hierarchy principle
identifyVs thatcannotbe
eliminated
EVisignificant
+
EandVimust remain

CATCHL)CAT and CHL remain
CATHPT)CAT and HPT remain


Thus,
CAT (exposure) remains
plus
CHL and HPT remain

AGE, SMK, ECG
eligiblefor elimination
Results (details in Chap. 7):

Cannot remove AGE, SMK, ECG
(decisions too subjective)

Final model variables:
CAT, AGE, CHL, SMK, ECG, HPT,
CATCHL, and CATHPT

Presentation: X. An Example 191
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