Evidence map›Paper›PMID 29757775›Full record

ArticleJournal of acquired immune deficiency syndromes (1999)2018

Development and Calibration of a Mathematical Model of Anal Carcinogenesis for High-Risk HIV-Infected Men.

Emily A Burger, Michael A Dyer, Stephen Sy, Joel M Palefsky, Alexandra de Pokomandy, François Coutlee, Michael J Silverberg, Jane J Kim

Abstract read
In one paragraph

Article in Journal of acquired immune deficiency syndromes (1999), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact, top 95% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  2. Article
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

8 authors at 6 institutions in 3 countries.

Emily A BurgerDepartment of Health Policy and Management, Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA.
Michael A DyerDepartment of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA.
Stephen SyDepartment of Health Policy and Management, Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA.
Joel M PalefskyDepartment of Medicine, University of California, San Francisco, CA.
Alexandra de PokomandyChronic Viral Illness Service, McGill University Health Centre, Montreal, Quebec, Canada.
François CoutleeUniversité de Montréal, Département de Microbiologie et Infectiologie, Montreal, Quebec, Canada.
Michael J SilverbergDivision of Research, Kaiser Permanente, Oakland, CA.
Jane J KimDepartment of Health Policy and Management, Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA.
Decision Sciences (United States) · USBoston University · USKaiser Permanente · USMcGill University Health Centre · CAUniversité de Montréal · CAUniversity of California, San Francisco · US

Funding

Antiretroviral Therapy Strategies to Lower Cancer Risk in HIV-Infected PersonsR01CA165937 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI DUBROW, ROBERT D, SILVERBERG, MICHAEL J · 2012 to 2015
$2.4M
Comparative and Cost-Effectiveness of Preventing HPV-Related DiseasesR01CA160744 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI KIM, JANE JOOYUN · 2012 to 2015
$1.2M
NCI NIH HHS R01 CA160744NCI NIH HHS R01 CA165937
6 · The paper itself

Abstract

objectivesMen who have sex with men who are living with HIV are at highest risk for anal cancer. Our objective was to use empirical data to develop a comprehensive disease simulation model that reflects the most current understanding of anal carcinogenesis, which is uniquely positioned to evaluate future anal cancer screening strategies and provide insight on the unobservable course of the disease.

settingNorth America.

methodsThe individual-based simulation model was calibrated leveraging primary data from empirical studies, such as a longitudinal HIV-positive men who have sex with men cohort study [Human Immunodeficiency and Papilloma Virus Research Group (HIPVIRG); n = 247] and the North American AIDS Cohort Collaboration on Research and Design [(NA-ACCORD); n = 13,146]. We used the model to infer unobservable progression probabilities from high-grade precancer to invasive anal cancer by CD4 nadir and human papillomavirus (HPV) genotype.

resultsThe calibrated model had good correspondence to data on genotype- and age-specific HPV prevalence; genotype frequency in precancer and cancer; and age- and nadir CD4-specific cancer incidence. The model-projected progression probabilities differed substantially by HPV genotype and nadir CD4 status. For example, among individuals with CD4 nadir <200, the median monthly progression probability from a high-grade lesion to invasive cancer was 0.054% (ie, 6.28% 10-year probability) and 0.004% (ie, 0.48% 10-year probability) for men with an HPV-16 infection versus without a detectable HPV infection, respectively.

conclusionsWe synthesized existing evidence into a state-of-the-art anal cancer disease simulation model that will be used to quantify the tradeoffs of harms and benefits of alternative strategies, understand critical uncertainties, and inform national anal cancer prevention policy.

Indexed as

Models, StatisticalAdultAlphapapillomavirusAnti-HIV AgentsAnus NeoplasmsCalibrationCD4 Lymphocyte CountHIV InfectionsHumansMaleMiddle AgedRisk FactorsTumor Virus InfectionsAnti-HIV Agents

Identifiers

PMID29757775
PMCPMC6092220
OpenAlexW2799814767

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.