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

TL;DR

This paper proposes a method to estimate confidence regions for common measures of baseline and treatment effect in observational studies with dichotomous outcomes, using a logistic model to simplify the process.

Contribution

It introduces a logistic model-based approach to derive confidence regions for baseline and treatment effect measures, avoiding complex traditional methods.

Findings

Efficient estimation of confidence regions using a single logistic model.

Applicability to various measures like odds ratio, risk difference, and attributable fraction.

Simplifies the process compared to normal approximation and bootstrap methods.

Abstract

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.