Using routinely collected data for research purposes: challenges and mitigation strategies

2026/06/02 by S Hoffmann, Tim Morris, Moritz Herrmann +24 · 2 voices · 1 citation
Mathematics · Social Sciences · #Census and Population Estimation #Data Analysis and Archiving #Data collection #Key (lock) #MEDLINE #Survey Methodology and Nonresponse #Text mining

paper · doi:10.1136/bmj-2025-087812

published in BMJ 393, e087812

openalex publication_date 2026/06/02 · openalex created_date 2026/06/03 · openalex updated_date 2026/07/28

Abstract

Increasing availability of large routinely collected datasets presents many possibilities to answer more questions about health and disease, and at a faster pace. These opportunities are exciting, but, without the necessary expertise, well intentioned researchers can unwittingly fall into traps that make their work indistinguishable from that of less well meaning researchers. This article describes important challenges that arise in the analysis of routinely collected data and offers mitigation strategies to improve the trustworthiness of experimental and observational studies with the aim of explanation or prediction using classical statistical methods or AI algorithms. It also provides a roadmap to support researchers in conducting analyses on routinely collected data that produce more reliable estimates.

Citations

• Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.
• Sensitivity Analysis in Observational Research: Introducing the E-Value

Discussions

• "Real world data" is an almost meaningless phrase. Our new @bmj.com paper pushes the more helpful phrase "routinely collected data" and explains the many limitations of such for research purposes. [bsky, 47 points, 3 comments]
• “Simply doing the best we can using routinely collected data is not good enough. No evidence for a research question may be preferable to evidence from a low quality routinely collected data study” d [bsky, 4 points, 0 comments]

Related

• Explosion of formulaic research articles, including inappropriate study designs and false discoveries, based on the NHANES US national health database
• Analyses and Concerns in Precision Medicine: A Statistical Perspective
• Statistical methods: Basic concepts, interpretations, and cautions
• Common Misconceptions about Data Analysis and Statistics
• Methodological Precedence in Health Tech: Why ML/Big Data Analysis Must Follow Basic Epidemiological Consistency. A Case Study