---
title: >-
  Pragmatic considerations for negative control outcome studies to guide
  non-randomized comparative analyses: A narrative review
description: >-
  Explore this publication on real-world evidence: Pragmatic considerations for
  negative control outcome studies to guide non-randomized comparative analyses:
  A…
date: '2023-01-01'
author: M. Alan Brookhart, PhD, David Pritchard
category: Publications
tags:
  - University of North Carolina at Chapel Hill
  - Abstract/Manuscript
  - Pharma Partner
  - Approval & Commercialization
  - 'Yes'
  - Health System Partner
  - Pharmacoepidemiology & Drug Safety
  - Amgen
  - Methods/Pharmacoepi
  - R&D
canonical_url: >-
  https://www.headwaterscience.com/resources/publications/acad-university-of-north-carolina-at-chapel-hill/pragmatic-considerations-for-negative-control-outcome/
source: Headwater Science
license: © 2026 Headwater Science. All rights reserved.
slug: pragmatic-considerations-for-negative-control-outcome
id: 4p4kdtl1okU6i5HiEFKDRd
contentType: article
---

## Challenge

Negative control outcome studies are increasingly used to evaluate unmeasured confounding in comparative effectiveness research, but practical guidance on how to select, implement, and interpret NCOs in real-world pharmacoepidemiologic settings was lacking.

## Solution

Target RWE researchers co-authored a narrative review in Pharmacoepidemiology & Drug Safety synthesizing pragmatic considerations for NCO study design, including domain-based confounding identification, NCO selection criteria, and interpretation frameworks for non-null findings.

## Impact

Providing an accessible, practice-oriented guide to NCO studies accelerates their adoption as a standard validity check in Target RWE's comparative effectiveness portfolio, strengthening study credibility for pharma partners and regulatory reviewers.

## Use Cases / Links

NCO study design guidance for comparative effectiveness validity in pharma partnerships, Practical framework for identifying and interpreting negative control outcomes in real-world data