Objectives Upon completing this chapter, the learner should be able to:
- Recognize the multivariable problem addressed by
logistic regression in terms of the types of variables
considered. - Identify properties of the logistic function that explain
its popularity. - State the general formula for the logistic model and
apply it to specific study situations. - Compute the estimated risk of disease development
for a specified set of independent variables from a
fitted logistic model. - Compute and interpret a risk ratio or odds ratio
estimate from a fitted logistic model. - Identify the extent to which the logistic model is
applicable to followup, case-control, and/or cross-
sectional studies. - Identify the conditions required for estimating a risk
ratio using a logistic model. - Identify the formula for the logit function and apply
this formula to specific study situations. - Describe how the logit function is interpretable in
terms of an “odds.” - Interpret the parameters of the logistic model in terms
of log odds. - Recognize that to obtain an odds ratio from a logistic
model, you must specifyXfor two groups being
compared. - Identify two formulae for the odds ratio obtained
from a logistic model. - State the formula for the odds ratio in the special case
of (0, 1) variables in a logistic model. - Describe how the odds ratio for (0, 1) variables is an
“adjusted” odds ratio. - Compute the odds ratio, given an example involving a
logistic model with (0, 1) variables and estimated
parameters. - State a limitation regarding the types of variables in
the model for use of the odds ratio formula for (0, 1)
variables.
Objectives 3