# An Epidemiologic Approach for Estimating Risk Reduction and Asymptotic Power on the Log-Difference Scale

## TL;DR

This paper explains a statistical method to compare the effectiveness or harm of two groups using a common reference group, helping to reduce bias in studies.

## Contribution

The paper introduces a framework for estimating risk reduction and test power using a log-difference scale with a common control group.

## Key findings

- A large-sample framework for conditionally independent comparisons is reviewed.
- Methods for estimating test power based on sample size are demonstrated.

## Abstract

When comparing the efficacy or harmfulness of two groups (e.g., drugs, devices, assays, interventions, environmental toxins), it is important to minimize bias by making this comparison with respect to a common referent-control group, assuming random allocation. Under such a scenario, one can estimate risk reduction for a new therapy on a log-difference, relative effect scale. The current manuscript reviews the large-sample framework for this conditionally independent comparison and demonstrates how to estimate test power for a given sample size.