Evidence map›Paper›PMID 35035932›Full record

ArticleClinical kidney journal2022

An introduction to inverse probability of treatment weighting in observational research.

Nicholas C Chesnaye, Vianda S Stel, Giovanni Tripepi, Friedo W Dekker, Edouard L Fu, Carmine Zoccali, Kitty J Jager

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

Article in Clinical kidney journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06666647 (Ambulance and Helicopter Response Times in Danish Emergency Medical Services), which is not on this map. Cited by 500 papers.

0numbers the graph read from it
0cells of the map it votes in
500citing 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.

NCT06666647 active not recruitingnot on this mapstarted 2025, after this paper: background citation

Ambulance and Helicopter Response Times in Danish Emergency Medical Services: Protocol for an Epidemiological Study. The AHRTEMIS Study

TypeobservationalSponsorPeter Martin HansenRan2025 to 2026Enrolled2,500,000ConditionsEmergency Medical Services, Response, Triage, Survival OutcomesArmsTreatment by ambulance or helicopter services with response times
3 · Its place in the literature

Who cites it

500 citing papers in PubMed.

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  18. The evolution of symptom burden in older men and women with advanced CKD.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2026
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  20. MES: A Multi-Agent Evidence Synthesis System for Medical Decision-Making.medRxiv : the preprint server for health sciences · 2026
    Article

440 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

7 authors.

Nicholas C ChesnayeERA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
Vianda S StelERA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
Giovanni TripepiCNR-IFC, Center of Clinical Physiology, Clinical Epidemiology of Renal Diseases and Hypertension, Reggio Calabria, Italy.
Friedo W DekkerDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Edouard L FuDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.ORCID https://orcid.org/0000-0002-9698-6825
Carmine ZoccaliCNR-IFC, Clinical Epidemiology of Renal Diseases and Hypertension, Reggio Calabria, Italy.
Kitty J JagerERA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article we introduce the concept of inverse probability of treatment weighting (IPTW) and describe how this method can be applied to adjust for measured confounding in observational research, illustrated by a clinical example from nephrology. IPTW involves two main steps. First, the probability-or propensity-of being exposed to the risk factor or intervention of interest is calculated, given an individual's characteristics (i.e. propensity score). Second, weights are calculated as the inverse of the propensity score. The application of these weights to the study population creates a pseudopopulation in which confounders are equally distributed across exposed and unexposed groups. We also elaborate on how weighting can be applied in longitudinal studies to deal with informative censoring and time-dependent confounding in the setting of treatment-confounder feedback.

Indexed as

chronic renal insufficiencydialysisepidemiologyguidelinessystematic review

Identifiers

PMID35035932
PMCPMC8757413

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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.