Evidence map›Paper›PMID 32162564›Full record

ReviewCritical reviews in toxicology2020

Smoking, air pollution, and lung cancer risk in the Nurses' Health Study cohort: time-dependent confounding and effect modification.

Ellen T Chang, Edmund C Lau, Suresh H Moolgavkar

Open access · hybridAbstract readReview
In one paragraph

Review in Critical reviews in toxicology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 15% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Update in Association between Lung Cancer and Air Pollution.Tuberculosis and respiratory diseases · 2025
    Article
  7. Review
  8. Observational
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Review
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

3 authors at 3 institutions in 1 country.

Ellen T ChangCenter for Health Sciences, Exponent, Inc., Menlo Park, CA, USA.
Edmund C LauCenter for Health Sciences, Exponent, Inc., Menlo Park, CA, USA.
Suresh H MoolgavkarCenter for Health Sciences, Exponent, Inc., Bellevue, WA, USA.
Bellevue Hospital Center · USExponent (United States) · USStanford Cancer Institute

Funding

Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
NCI NIH HHS UM1 CA186107
6 · The paper itself

Abstract

The proportional hazards (PH) model is commonly used in epidemiology despite the stringent assumption of proportionality of hazards over time. We previously showed, using detailed simulation data, that the impact of a modest risk factor cannot be estimated reliably using the PH model in the presence of confounding by a strong, time-dependent risk factor. Here, we examine the same and related issues using a real dataset. Among 97,303 women in the prospective Nurses' Health Study cohort from 1994 through 2010, we used PH regression to investigate how effect estimates for cigarette smoking are affected by increasingly detailed specification of time-dependent exposure characteristics. We also examined how effect estimates for fine particulate matter (PM

Indexed as

AdultAir PollutionEnvironmental ExposureHumansLung NeoplasmsNursesRisk FactorsSmokingAir pollutionconfoundingCox proportional hazards regressioneffect modificationepidemiologylung cancerparticulate mattersmoking

Identifiers

PMID32162564
PMCPMC7269844
OpenAlexW3012281838

What OpenQuestion holds

Textmetadata
LicenceTDM
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.