Evidence map›Paper›PMID 35399057›Full record

Observational studyBMC medical research methodology2022

Estimation of treatment effects in observational stroke care data: comparison of statistical approaches.

Marzyeh Amini, Nikki van Leeuwen, Frank Eijkenaar, Rob van de Graaf, Noor Samuels, Robert van Oostenbrugge, Ido R van den Wijngaard, Pieter Jan van Doormaal, Yvo B W E M Roos, Charles Majoie and 5 more

Open access · goldAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 28% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Evaluating large-scale propensity score adjustment when sample size is small.Journal of the American Medical Informatics Association : JAMIA · 2026
    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

15 authors at 6 institutions in 2 countries.

Marzyeh AminiDepartment of Public Health, Erasmus University Medical Center, Erasmus MC, P.O. Box 2040, CA, Rotterdam, the Netherlands. m.amini@erasmusmc.nl.
Nikki van LeeuwenDepartment of Public Health, Erasmus University Medical Center, Erasmus MC, P.O. Box 2040, CA, Rotterdam, the Netherlands.
Frank EijkenaarErasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, the Netherlands.
Rob van de GraafDepartment of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands.
Noor SamuelsDepartment of Public Health, Erasmus University Medical Center, Erasmus MC, P.O. Box 2040, CA, Rotterdam, the Netherlands.
Robert van OostenbruggeMaastricht University Medical Center, Maastricht, the Netherlands.
Ido R van den WijngaardDepartment of Neurology, Haaglanden Medical Center, the Hague, the Netherlands.
Pieter Jan van DoormaalDepartment of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands.
Yvo B W E M RoosAmsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands.
Charles MajoieDepartment of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands.
Bob RoozenbeekDepartment of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands.
Diederik DippelDepartment of Neurology, Erasmus University Medical Center, Rotterdam, the Netherlands.
James BurkeDepartment of Neurology, University of Michigan, Ann Arbor, MI, USA.
Hester F LingsmaDepartment of Public Health, Erasmus University Medical Center, Erasmus MC, P.O. Box 2040, CA, Rotterdam, the Netherlands.
the, MR CLEAN Registry Investigators
Erasmus MC · NLAmsterdam University Medical Centers · NLMaastricht University · NLMedisch Centrum Haaglanden · NLUniversity of Michigan · USErasmus University Rotterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionVarious statistical approaches can be used to deal with unmeasured confounding when estimating treatment effects in observational studies, each with its own pros and cons. This study aimed to compare treatment effects as estimated by different statistical approaches for two interventions in observational stroke care data. PATIENTS AND

methodsWe used prospectively collected data from the MR CLEAN registry including all patients (n = 3279) with ischemic stroke who underwent endovascular treatment (EVT) from 2014 to 2017 in 17 Dutch hospitals. Treatment effects of two interventions - i.e., receiving an intravenous thrombolytic (IVT) and undergoing general anesthesia (GA) before EVT - on good functional outcome (modified Rankin Scale ≤2) were estimated. We used three statistical regression-based approaches that vary in assumptions regarding the source of unmeasured confounding: individual-level (two subtypes), ecological, and instrumental variable analyses. In the latter, the preference for using the interventions in each hospital was used as an instrument.

resultsUse of IVT (range 66-87%) and GA (range 0-93%) varied substantially between hospitals. For IVT, the individual-level (OR ~ 1.33) resulted in significant positive effect estimates whereas in instrumental variable analysis no significant treatment effect was found (OR 1.11; 95% CI 0.58-1.56). The ecological analysis indicated no statistically significant different likelihood (β = - 0.002%; P = 0.99) of good functional outcome at hospitals using IVT 1% more frequently. For GA, we found non-significant opposite directions of points estimates the treatment effect in the individual-level (ORs ~ 0.60) versus the instrumental variable approach (OR = 1.04). The ecological analysis also resulted in a non-significant negative association (0.03% lower probability). DISCUSSION AND

conclusionBoth magnitude and direction of the estimated treatment effects for both interventions depend strongly on the statistical approach and thus on the source of (unmeasured) confounding. These issues should be understood concerning the specific characteristics of data, before applying an approach and interpreting the results. Instrumental variable analysis might be considered when unobserved confounding and practice variation is expected in observational multicenter studies.

Indexed as

Endovascular ProceduresStrokeFibrinolytic AgentsHumansThrombolytic TherapyTreatment OutcomeFibrinolytic AgentsAcute ischemic strokeConfounding by indicationEcological-analysisGeneral anesthesiaInstrumental variableIntravenous thrombolysisStatistical approachesUnmeasured confounding

Identifiers

PMID35399057
PMCPMC8996562
OpenAlexW4223438663

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.