Evidence map›Paper›PMID 19262389›Full record

ArticleEpidemiology (Cambridge, Mass.)2009

Latency models for analyses of protracted exposures.

David B Richardson

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Prediagnostic Smoking Is Associated with Binary and Quantitative Measures of ER Protein andCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2018
    Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Classification of drug use patterns.Pharmacology research & perspectives · 2020
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Attributable risk from distributed lag models.BMC medical research methodology · 2014
    Article
  12. Article
  13. Article
  14. Article
  15. 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

1 author.

David B RichardsonDepartment of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA. david.richardson@unc.edu

Funding

Cohort analysis methods for occupational cancer studiesR01CA117841 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI RICHARDSON, DAVID B · 2007 to 2012
$1.1M
NCI NIH HHS R01 CA117841NCI NIH HHS R01-CA117841
6 · The paper itself

Abstract

The effect of an increment of exposure on disease risk may vary with time-since-exposure. If the pattern of temporal variation is simple (eg, a peak and then a decline in excess risk of disease) then this may be modeled efficiently via a parametric latency function. Estimation of the parameters for such a model can be difficult because the parameters are not a function of a simple summary of the exposure history. Typically, such parameters are estimated via an iterative search that requires calculating a different time-weighted exposure for each combination of the latency function parameters. This article describes a simple approach to fitting logistic regression models that include a parametric latency function. This approach is illustrated using data from a study of the association between radon exposure and lung cancer mortality among underground uranium miners. This approach should facilitate fitting models to assess variation with time since exposure in the effect of a protracted environmental or occupational exposure.

Indexed as

Models, StatisticalReaction TimeEpidemiologic StudiesHumansLung NeoplasmsMiningOccupational ExposureOdds RatioUraniumUranium

Identifiers

PMID19262389
PMCPMC2668725

What OpenQuestion holds

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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.