Evidence map›Paper›PMID 40935907›Full record

ArticleOncology and therapy2025

A Practical Approach to Understanding Real-World Study Methodology in Cancer Research: A Vodcast.

Adam Brufsky, Winson Cheung, Joanne C Ryan

Abstract read
In one paragraph

Article in Oncology and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Adam BrufskyUPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA. brufskyam@upmc.edu.
Winson CheungDepartment of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Joanne C RyanPfizer Inc, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Real-world studies have become more common in clinical literature in recent years, but many clinicians remain unfamiliar with real-world study design and statistical approaches. This vodcast intends to be a practical guide for clinicians by clarifying aspects of real-world study methodology. As both practicing oncologists and researchers with extensive real-world data experience, the hosts discuss types of study designs and real-world data source considerations. An overview of statistical techniques for mitigating treatment-selection bias is also provided, including propensity score matching, inverse probability of treatment weighting, and multivariable analysis. By combining high-quality data sources, careful sample size considerations, and rigorous statistical techniques, real-world studies can offer valuable insights into therapeutic effectiveness in routine clinical practice that supplement learnings from randomized clinical trials. This vodcast is designed to equip clinicians with the knowledge to critically evaluate real-world evidence and potentially apply it to their practice.Vodcast and infographic available for this article. Vodcast (MP4 1207725 KB) INFOGRAPHIC.

Indexed as

Inverse probability of treatment weightingIPTWMultivariable analysisPropensity score matchingPSMReal-world study designTreatment-selection bias

Identifiers

PMID40935907
PMCPMC12647456

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.