Work

Modeling the Effects of Smoking on Lung Cancer

Year: 2026

Type: article

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.

Source: Observational Studies

DOI: 10.1353/obs.00006

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

Institutions: University of Waterloo, National Institutes of Health, National Cancer Institute

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W7208834601

FWCI:

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