Evidence map›Paper›PMID 41641624›Full record

ArticleStatistics in medicine2026

Assessing the Benefits and Burdens of Preventive Interventions.

Yi Xiong, Kwun C G Chan, Malka Gorfine, Li Hsu

Abstract read
In one paragraph

Article in Statistics in medicine, 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.

Yi XiongDepartment of Biostatistics, University at Buffalo, Buffalo, New York, USA.ORCID https://orcid.org/0009-0007-7688-0138
Kwun C G ChanDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.
Malka GorfineDepartment of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel.
Li HsuBiostatistics Program, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Marian Esvelt · 1985 to 2026
$296.4M
Statistical Methods for Analysis of Tumor Heterogeneity in Genetic EpidemiologyR01CA189532 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HSU, LI · 2015 to 2023
$4.2M
High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases ResearchS10OD028685 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRADLEY, PHILIP · 2020 to 2020
$2.0M
Statistical Methods for Prospective Evaluation of BiomarkersR01CA236558 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI ZHENG, YINGYE · 2019 to 2024
$1.9M
Statistical Methods for Genetic Epidemiology StudiesR01CA195789 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HSU, LI · 2016 to 2019
$1.6M
Statistical Methods for Precision PreventionR01CA297681 · NCI · FRED HUTCHINSON CANCER CENTER · PI Li Hsu · 2025 to 2026
$1.0M
NCI NIH HHS P30 CA015704NCI NIH HHS R01 CA189532NCI NIH HHS R01 CA195789NCI NIH HHS R01 CA297681NHLBI NIH HHSNHLBI NIH HHS HHSN268201600001CNHLBI NIH HHS HHSN268201600002CNHLBI NIH HHS HHSN268201600003CNHLBI NIH HHS HHSN268201600004CNHLBI NIH HHS HHSN268201600018CNIH HHS P30 CA015704NIH HHS R01 CA189532NIH HHS R01 CA195789NIH HHS R01 CA236558NIH HHS S10 OD028685NIH HHS U01 CA86368ORIP NIH HHS S100D028685
6 · The paper itself

Abstract

Cancer prevention is recognized as a key strategy for reducing disease incidence, mortality, and the overall burden on individuals and society. However, determining when to begin preventive interventions presents a significant challenge: starting too early may lead to more interventions and increased lifetime burdens due to repeated administrations, while delaying may miss opportunities to prevent cancer. Evidence-based recommendations require a benefit-burden analysis that weighs life-years gained against the burden of interventions. With the growing availability of large-scale observational data, there is now an opportunity to empirically evaluate these trade-offs. In this paper, we propose a causal framework for assessing the benefit and burden of cancer prevention, using an illness-death model with semi-competing risks. Extensive simulations demonstrate that the proposed estimators are unbiased, with robust inference across realistic scenarios. We apply this approach to a benefit-burden analysis of the preventive screening for colorectal cancer, utilizing data from the large-scale Women's Health Initiative. Our findings suggest that initiating screening at age 50 years achieves the highest life-year gains with an acceptable incremental burden-to-benefit ratio compared to no screening, contributing valuable real-world evidence to the field of preventive cancer interventions.

Indexed as

Colorectal NeoplasmsComputer SimulationEarly Detection of CancerFemaleHumansMiddle AgedModels, StatisticalRisk Assessmentcausal inferenceillness‐death modelsurvival analysistime‐varying intervention

Identifiers

PMID41641624
PMCPMC13231249

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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.