  In this article we estimate confidence regions of the common measures of
(baseline, treatment effect) in observational studies, where the measure of
baseline is baseline risk or baseline odds while the measure of treatment
effect is odds ratio, risk difference, risk ratio or attributable fraction, and
where confounding is controlled in estimation of both baseline and treatment
effect. To avoid high complexity of the normal approximation method and the
parametric or non-parametric bootstrap method, we obtain confidence regions for
measures of (baseline, treatment effect) by generating approximate
distributions of the ML estimates of these measures based on one logistic
model.
