Evidence map›Paper›PMID 40689869›Full record

ArticleStatistics in medicine2025

A Simple Diagnostic for the Positivity Assumption for Continuous Exposures.

Erica E M Moodie, Juliana Schulz

Abstract read
In one paragraph

Article in Statistics in medicine, 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. A Framework for the Estimation of Quality-Adjusted Life-Years Using Joint Models of Longitudinal and Survival Data.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    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

2 authors.

Erica E M MoodieDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Quebec, Canada.ORCID https://orcid.org/0000-0002-7225-3977
Juliana SchulzDepartment of Decision Sciences, HEC Montréal, Quebec, Canada.ORCID https://orcid.org/0000-0001-7312-0167

Funding

CIHRFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of Canada RGPIN-2022-05118
6 · The paper itself

Abstract

The positivity or experimental treatment assignment assumption is a fundamental requirement in causal analyses, invoked to ensure that identifiability holds without extrapolating beyond what the observed data can reveal. Positivity is well understood in the context of binary and categorical treatments, and has been thoroughly discussed-from how the assumption can be assessed to approaches that may be used when the assumption is suspected not to hold. Positivity extends to the context of continuous exposures, such as doses, however it has been given very little formal consideration. In this manuscript, we propose a method for assessing whether the positivity assumption is violated in a given dataset, relying on a principled concept in regression analysis. We demonstrate the diagnostic tool in various simulated settings, as well as in an application involving warfarin dosing.

Indexed as

Models, StatisticalAnticoagulantsCausalityComputer SimulationData Interpretation, StatisticalHumansRegression AnalysisWarfarinAnticoagulantsWarfarincausal inferencedoseexperimental treatment assignment assumptiongeneralized propensity scorehat‐value

Identifiers

PMID40689869
PMCPMC12279005

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.