Evidence map›Paper›PMID 31591592›Full record

ArticleNature medicine2019

Avoidable flaws in observational analyses: an application to statins and cancer.

Barbra A Dickerman, Xabier García-Albéniz, Roger W Logan, Spiros Denaxas, Miguel A Hernán

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06645990 (Early Left Ventricular unLoading by Impella), which is not on this map. Cited by 185 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
185citing papers in PubMed, 6 pooled it
–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.

NCT06645990 recruitingnot on this map

Early Left Ventricular unLoading by Impella (ECMELLA) or Intra-aortic Balloon Pump for Cardiogenic Shock in Patients on VA-ECMO

TypeobservationalSponsorUniversity Hospital, MontpellierRan2010 to 2025Enrolled500ConditionsCardiogenic ShockArmsImpella, IABP
3 · Its place in the literature

Who cites it

185 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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125 more citing papers are in PubMed but not listed here.

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

5 authors.

Barbra A DickermanDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA. bad788@mail.harvard.edu.ORCID http://orcid.org/0000-0003-2843-687X
Xabier García-AlbénizDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Roger W LoganDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Spiros DenaxasInstitute of Health Informatics Research, University College London, London, UK.
Miguel A HernánDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

Funding

Statistical Methods for the Spatio-Temporal Assessment of Social Disparities in CancerP01CA134294 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANEUSE, SEBASTIEN, HERNAN, MIGUEL · 2008 to 2017
$6.8M
Medical Research Council MR/K006584/1NCI NIH HHS P01 CA134294
6 · The paper itself

Abstract

The increasing availability of large healthcare databases is fueling an intense debate on whether real-world data should play a role in the assessment of the benefit-risk of medical treatments. In many observational studies, for example, statin users were found to have a substantially lower risk of cancer than in meta-analyses of randomized trials. Although such discrepancies are often attributed to a lack of randomization in the observational studies, they might be explained by flaws that can be avoided by explicitly emulating a target trial (the randomized trial that would answer the question of interest). Using the electronic health records of 733,804 UK adults, we emulated a target trial of statins and cancer and compared our estimates with those obtained using previously applied analytic approaches. Over the 10-yr follow-up, 28,408 individuals developed cancer. Under the target trial approach, estimated observational analogs of intention-to-treat and per-protocol 10-yr cancer-free survival differences were -0.5% (95% confidence interval (CI) -1.0%, 0.0%) and -0.3% (95% CI -1.5%, 0.5%), respectively. By contrast, previous analytic approaches yielded estimates that appeared to be strongly protective. Our findings highlight the importance of explicitly emulating a target trial to reduce bias in the effect estimates derived from observational analyses.

Indexed as

Electronic Health RecordsDisease-Free SurvivalHumansHydroxymethylglutaryl-CoA Reductase InhibitorsNeoplasmsRandomized Controlled Trials as TopicRisk AssessmentRisk FactorsHydroxymethylglutaryl-CoA Reductase Inhibitors

Identifiers

PMID31591592
PMCPMC7076561

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Registered trials

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 OpenQuestion graph.