ArticleThe annals of applied statistics2017
FLEXIBLE RISK PREDICTION MODELS FOR LEFT OR INTERVAL-CENSORED DATA FROM ELECTRONIC HEALTH RECORDS.
Article in The annals of applied statistics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
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.
Who cites it
20 citing papers in PubMed, 21 citations in OpenAlex.
- Article
- A Bayesian Prevalence-Incidence Mixture Model for Screening Outcomes With Misclassification.Statistics in medicine · 2026Article
- On Window Mean Survival Time With Interval-Censored Data.Statistics in medicine · 2026Article
- An Augmented Likelihood Approach Incorporating Error-Prone Auxiliary Data Into a Survival Analysis.Statistics in medicine · 2025Article
- Risk assessment in a Chinese cohort of 96 318 females undergoing opportunistic cervical cancer screening.The oncologist · 2025Article
- DYNAMIC RISK PREDICTION FOR CERVICAL PRECANCER SCREENING WITH CONTINUOUS AND BINARY LONGITUDINAL BIOMARKERS.The annals of applied statistics · 2024Article
- Frequency of pathogenic germline variants in pediatric medulloblastoma survivors.Frontiers in oncology · 2024Article
- 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
- 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
- Association of HPV35 with cervical carcinogenesis among women of African ancestry: Evidence of viral-host interaction with implications for disease intervention.International journal of cancer · 2020Article
- Sample-weighted semiparametric estimation of cause-specific cumulative risk and incidence using left- or interval-censored data from electronic health records.Statistics in medicine · 2020Article
- A study of type-specific HPV natural history and implications for contemporary cervical cancer screening programs.EClinicalMedicine · 2020Article
- Risk Estimates Supporting the 2019 ASCCP Risk-Based Management Consensus Guidelines.Journal of lower genital tract disease · 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
- 2019 ASCCP Risk-Based Management Consensus Guidelines: Methods for Risk Estimation, Recommended Management, and Validation.Journal of lower genital tract disease · 2020Article
- Role of Screening History in Clinical Meaning and Optimal Management of Positive Cervical Screening Results.Journal of the National Cancer Institute · 2019Article
- Automated Cervical Screening and Triage, Based on HPV Testing and Computer-Interpreted Cytology.Journal of the National Cancer Institute · 2018Article
- Challenges in risk estimation using routinely collected clinical data: The example of estimating cervical cancer risks from electronic health-records.Preventive medicine · 2018Article
- FLEXIBLE RISK PREDICTION MODELS FOR LEFT OR INTERVAL-CENSORED DATA FROM ELECTRONIC HEALTH RECORDS.The annals of applied statistics · 2017Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Electronic health records are a large and cost-effective data source for developing risk-prediction models. However, for screen-detected diseases, standard risk models (such as Kaplan-Meier or Cox models) do not account for key issues encountered with electronic health record data: left-censoring of pre-existing (prevalent) disease, interval-censoring of incident disease, and ambiguity of whether disease is prevalent or incident when definitive disease ascertainment is not conducted at baseline. Furthermore, researchers might conduct novel screening tests only on a complex two-phase subsample. We propose a family of weighted mixture models that account for left/interval-censoring and complex sampling via inverse-probability weighting in order to estimate current and future absolute risk: we propose a weakly-parametric model for general use and a semiparametric model for checking goodness of fit of the weakly-parametric model. We demonstrate asymptotic properties analytically and by simulation. We used electronic health records to assemble a cohort of 33,295 human papillomavirus (HPV) positive women undergoing cervical cancer screening at Kaiser Permanente Northern California (KPNC) that underlie current screening guidelines. The next guidelines would focus on HPV typing tests, but reporting 14 HPV types is too complex for clinical use. National Cancer Institute along with KPNC conducted a HPV typing test on a complex subsample of 9258 women in the cohort. We used our model to estimate the risk due to each type and grouped the 14 types (the 3-year risk ranges 21.9-1.5) into 4 risk-bands to simplify reporting to clinicians and guidelines. These risk-bands could be adopted by future HPV typing tests and future screening guidelines.
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Registered trials
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.