Evidence map›Paper›PMID 34642734›Full record

ArticleAmerican journal of epidemiology2022

Impact of Reverse Causation on Estimates of Cancer Risk Associated With Radiation Exposure From Computerized Tomography: A Simulation Study Modeled on Brain Cancer.

Mark P Little, Ankur Patel, Choonsik Lee, Michael Hauptmann, Amy Berrington de Gonzalez, Paul Albert

Open access · greenAbstract read
In one paragraph

Article in American journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

7 citing papers in PubMed, 15 citations in OpenAlex.

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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

6 authors.

Mark P Little
Ankur Patel
Choonsik Lee
Michael Hauptmann
Amy Berrington de Gonzalez
Paul Albert

Funding

Studies of Populations Exposed to Environmental Sources of RadiationZIACP010132 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI CAHOON, ELIZABETH · 2009 to 2025
$35.2M
6 · The paper itself

Abstract

Use of computed tomography (CT) scanning has increased substantially since its introduction in the 1990s. Several authors have reported increased risk of leukemia and brain tumors associated with radiation exposure from CT scans. However, reverse causation is a concern, particularly for brain cancer; in other words, the CT scan may have been taken because of preexisting cancer and therefore not have been a cause. We assessed the possibility of reverse causation via a simulation study focused on brain tumors, using a simplified version of the data structure for recent CT studies. Five-year-lagged and unlagged analyses implied an observed excess risk per scan up to 70% lower than the true excess risk per scan, particularly when more than 10% of persons with latent cancer had increased numbers of scans or the extra scanning rate after development of latent cancer was greater than 2 scans/year; less extreme values of these parameters imply little risk attenuation. Without a lag and when more than 20% of persons with latent cancer had increased scans-an arguably implausible scenario-the excess risk per scan was increased over the true excess risk per scan by up to 35%-40%. This study suggests that with a realistic lag, reverse causation results in downwardly biased risk, a result of induced classical measurement error, and is therefore unlikely to produce a spurious positive association between cancer and radiation dose from CT scans.

Indexed as

CausalityBrain NeoplasmsComputer SimulationEpidemiologic MethodsHumansNeoplasms, Radiation-InducedRisk AssessmentTomography, X-Ray Computedbrain tumorscomputed tomographyionizing radiationreverse causationsimulation studies

Identifiers

PMID34642734
PMCPMC8898001
OpenAlexW3205834625

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.