- Using the printout for Model I, compute the point
estimate and a 95% confidence interval for the odds
ratio for the effect of CAT on CHD controlling for the
other variables in the model. - Now consider Model II: Carry out the likelihood ratio
test for the effect of the product term CC on the out-
come, controlling for the other variables in the model.
Make sure to state the null hypothesis in terms of a
model coefficient, give the formula for the test statis-
tic and its distribution and degrees of freedom under
the null hypothesis, and report theP-value. Is the test
result significant? - Carry out the Wald test for the effect of CC on out-
come, controlling for the other variables in Model II.
In carrying out this test, provide the same information
as requested in Exercise 10. Is the test result signifi-
cant? How does it compare to your results in Exercise
10? Based on your results, which model is more
appropriate, Model I or II? - Using the output for Model II, give a formula for the
point estimate of the odds ratio for the effect of CAT
on CHD, which adjusts for the confounding effects of
AGE, CHL, ECG, SMK, and HPT and allows for the
interaction of CAT with CHL. - Use the formula for the adjusted odds ratio in Exercise
12 to compute numerical values for the estimated odds
ratio for the following cholesterol values: CHL¼ 220
and CHL¼240. - Give a formula for the 95% confidence interval for the
adjusted odds ratio described in Exercise 12 when
CHL¼220. In stating this formula, make sure to
give an expression for the estimated variance portion
of the formula in terms of variances and covariances
obtained from the variance–covariance matrix.
Test The following printout provides information for the fitting
of two logistic models based on data obtained from a
matched case-control study of cervical cancer in 313
women from Sydney, Australia (Brock et al., 1988). The
outcome variable is cervical cancer status (1¼present,
0 ¼absent). The matching variables are age and socio-
economic status. Additional independent variables not
matched on are smoking status, number of lifetime
sexual partners, and age at first sexual intercourse. The
independent variables not involved in the matching are
listed below, together with their computer abbreviation
and coding scheme.
Test 159