Evidence map›Paper›PMID 24027094›Full record

ArticleStatistics in medicine2014

Modeling exposure-lag-response associations with distributed lag non-linear models.

Antonio Gasparrini

Erratum issuedAbstract read
In one paragraph

Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 364 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
364citing papers in PubMed, 9 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.

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

364 citing papers in PubMed, 9 syntheses or guidelines pooled it.

  1. Pooled it
  2. Building the model: a review of input structures in extreme temperature-health.Environmental health : a global access science source · 2026
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  13. Prenatal PMRespirology (Carlton, Vic.) · 2026
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304 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

1 author.

Antonio GasparriniMedical Statistics Department, London School of Hygiene and Tropical Medicine, London, U.K.

Funding

Medical Research Council G1002296
6 · The paper itself

Abstract

In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure-lag-response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others.

Indexed as

Data Interpretation, StatisticalModels, StatisticalRiskTime FactorsAdultAgedAged, 80 and overComputer SimulationHumansLung NeoplasmsMiddle AgedOccupational ExposureRadonRadondelayed effectsdistributed lag modelsexposure-lag-responselatencysplines

Identifiers

PMID24027094
PMCPMC4098103

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.