ArticleStatistics in medicine2017
Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 2019 ASCCP Risk-Based Management Consensus Guidelines for Abnormal Cervical Cancer Screening Tests and Cancer Precursors.Journal of lower genital tract disease · 2020Guideline
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- A Bayesian Prevalence-Incidence Mixture Model for Screening Outcomes With Misclassification.Statistics in medicine · 2026Article
- Impact of repeatedly screening negative on cervical cancer risk.Journal of the National Cancer Institute · 2026Article
- Risk assessment in a Chinese cohort of 96 318 females undergoing opportunistic cervical cancer screening.The oncologist · 2025Article
- Primary human papillomavirus testing vs cotesting: clinical outcomes in populations with different disease prevalence.Journal of the National Cancer Institute · 2024Article
- Cervical cancer screening using DNA methylation triage in a real-world population.Nature medicine · 2024Article
- DYNAMIC RISK PREDICTION FOR CERVICAL PRECANCER SCREENING WITH CONTINUOUS AND BINARY LONGITUDINAL BIOMARKERS.The annals of applied statistics · 2024Article
- Untreated cervical intraepithelial neoplasia grade 2 and subsequent risk of cervical cancer: population based cohort study.BMJ (Clinical research ed.) · 2023Article
- The combined finding of HPV 16, 18, or 45 and cytologic Atypical Glandular Cells (AGC) indicates a greatly elevated risk of in situ and invasive cervical adenocarcinoma.Gynecologic oncology · 2023Article
- Absolute Risk of Oropharyngeal Cancer After an HPV16-E6 Serology Test and Potential Implications for Screening: Results From the Human Papillomavirus Cancer Cohort Consortium.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2022Article
- Unsupervised probabilistic models for sequential Electronic Health Records.Journal of biomedical informatics · 2022Article
- Multistate models for the natural history of cancer progression.British journal of cancer · 2022Article
- A comparison of high-grade cervical abnormality risks in women living with and without human immunodeficiency virus undergoing routine cervical-cancer screening.Preventive medicine · 2022Article
- The Improving Risk Informed HPV Screening (IRIS) Study: Design and Baseline Characteristics.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
- On Suitability of Mixture of Generalized Exponential Models in Modeling Right-Censored Medical Datasets Using Conditional Expectations.Computational and mathematical methods in medicine · 2022Article
- Risk of cervical precancer and cancer among uninsured and underserved women from 2009 to 2017.American journal of obstetrics and gynecology · 2021Article
- A study of type-specific HPV natural history and implications for contemporary cervical cancer screening programs.EClinicalMedicine · 2020Article
- A Study of Partial Human Papillomavirus Genotyping in Support of the 2019 ASCCP Risk-Based Management Consensus Guidelines.Journal of lower genital tract disease · 2020Article
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8 authors.
Funding
Abstract
For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
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