Evidence map›Paper›PMID 42781629›Full record

ArticleObservational studies2026

Modeling the Effects of Smoking on Lung Cancer.

Richard J Cook, Laura S Bumbulis, Sung Duk Kim, Paul S Albert

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Observational studies, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Richard J CookDepartment of Statistics and Actuarial Science University of Waterloo.
Laura S BumbulisDepartment of Statistics and Actuarial Science University of Waterloo.
Sung Duk KimBiostatistics Branch National Cancer Institute National Institutes of Health.
Paul S AlbertBiostatistics Branch National Cancer Institute National Institutes of Health.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modeling the effect of smoking on the risk of lung cancer in cohort studies requires specification of time-dependent exposure variables and appropriate handling of confounding. This can be challenging as data on both exposure and confounders are typically incompletely observed. We discuss population exposure and disease processes and review modeling challenges when exposures are dynamic and challenging to summarize. Biases arising from failure to address time-dependent confounding are highlighted, with particular reference to a paradox related to the effect of quitting on risk of lung cancer. We stress the utility of comprehensive joint models.

Indexed as

Competing risksFailure time analysisIncomplete dataTime-dependent exposure

Identifiers

PMID42781629
PMCPMC13600457

What OpenQuestion holds

Textmetadata
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.