ArticleBritish journal of cancer2022
Multistate models for the natural history of cancer progression.
Article in British journal of cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Developing a Comprehensive Framework for Cost-Effectiveness Evaluation in Metastatic Castration-Sensitive Prostate Cancer: Insights from a Systematic Review.PharmacoEconomics · 2025Pooled it
- Application of multistate modeling to clinical data analysis.CPT: pharmacometrics & systems pharmacology · 2024Trial
- Predicting disease progression and mortality in prostate cancer using real-world data-driven time-inhomogeneous Markov models.iScience · 2026Article
- Cardiovascular events and mortality after breast cancer diagnosis: multistate models in a nationwide cohort study.BMJ open · 2026Article
- Longitudinal Transitions Between Internalizing and Externalizing Symptoms and Associations with Substance Use among US Young Adults, 2016-2023.Epidemiology (Cambridge, Mass.) · 2026Article
- A Comparison of Methods for Modeling Multistate Cancer Progression Using Screening Data with Censoring after Intervention.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Discrete-Event Simulation Modeling Framework for Cancer Interventions and Population Health in R (DESCIPHR): An Open-Source Pipeline.PharmacoEconomics · 2026Article
- Data-augmented multistate modeling for chronic disease processes using cross-sectional studies: application to HPV and cervical precancer.Biostatistics (Oxford, England) · 2026Article
- Methods of multi-indication meta-analysis for health technology assessment: A simulation study.Research synthesis methods · 2026Article
- Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams.Biostatistics (Oxford, England) · 2025Article
- Development and validation of a histology-specific natural history model of ovarian cancer.American journal of obstetrics and gynecology · 2025Article
- A stochastic modelling framework for cancer patient trajectories: combining tumour growth, metastasis, and survival.Journal of mathematical biology · 2025Article
- Statistical Power and Performance of Strategies to Analyze Composites of Survival and Duration of Ventilation in Clinical Trials.Critical care explorations · 2024Article
- Clinical courses of acute kidney injury in hospitalized patients: a multistate analysis.Scientific reports · 2023Article
- Echocardiography phenotypes of right ventricular involvement in COVID-19 ARDS patients and ICU mortality: post-hoc (exploratory) analysis of repeated data from the ECHO-COVID study.Intensive care medicine · 2023Observational
- Use of a multistate model in survival predictions in cardiology studies.REC, interventional cardiologyArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultistate models can be effectively used to characterise the natural history of cancer. Inference from such models has previously been useful for setting screening policies.
methodsWe introduce the basic elements of multistate models and the challenges of applying these models to cancer data. Through simulation studies, we examine (1) the impact of assuming time-homogeneous Markov transition intensities when the intensities depend on the time since entry to the current state (i.e., the process is time-inhomogenous semi-Markov) and (2) the effect on precancer risk estimation when observation times depend on an unmodelled intermediate disease state.
resultsIn the settings we examined, we found that misspecifying a time-inhomogenous semi-Markov process as a time-homogeneous Markov process resulted in biased estimates of the mean sojourn times. When screen-detection of the intermediate disease leads to more frequent future screening assessments, there was minimal bias induced compared to when screen-detection of the intermediate disease leads to less frequent screening.
conclusionsMultistate models are useful for estimating parameters governing the process dynamics in cancer such as transition rates, sojourn time distributions, and absolute and relative risks. As with most statistical models, to avoid incorrect inference, care should be given to use the appropriate specifications and assumptions.
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