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

Estimating confidence regions of common measures of (baseline, treatment effect) on dichotomous outcome of a population