ArticleRisk analysis : an official publication of the Society for Risk Analysis2017
A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models.
Article in Risk analysis : an official publication of the Society for Risk Analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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
13 citing papers in PubMed.
- Modeling Early-Onset Cancer Kinetics Reveals Changes in Underlying Risk and the Impact of Population Screening.Cancer research · 2025Article
- Modeling early-onset cancer kinetics to study changes in underlying risk, detection, and impact of population screening.medRxiv : the preprint server for health sciences · 2025Article
- Mathematical modeling for mutator phenotype and clonal selection advantage in the risk analysis of lung cancer.Theory in biosciences = Theorie in den Biowissenschaften · 2022Article
- Structural identifiability analysis of age-structured PDE epidemic models.Journal of mathematical biology · 2022Article
- SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth.Frontiers in molecular biosciences · 2022Article
- Identifiability analysis for stochastic differential equation models in systems biology.Journal of the Royal Society, Interface · 2020Article
- Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer.Physical biology · 2020Article
- Mathematical modelling the pathway of genomic instability in lung cancer.Scientific reports · 2019Article
- The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems.Mathematical biosciences · 2019Article
- Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect.PLoS biology · 2019Article
- Identification of the Fraction of Indolent Tumors and Associated Overdiagnosis in Breast Cancer Screening Trials.American journal of epidemiology · 2019Article
- A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study.Journal of theoretical biology · 2017Article
- Parameter estimation for multistage clonal expansion models from cancer incidence data: A practical identifiability analysis.PLoS computational biology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Multistage clonal expansion (MSCE) models of carcinogenesis are continuous-time Markov process models often used to relate cancer incidence to biological mechanism. Identifiability analysis determines what model parameter combinations can, theoretically, be estimated from given data. We use a systematic approach, based on differential algebra methods traditionally used for deterministic ordinary differential equation (ODE) models, to determine identifiable combinations for a generalized subclass of MSCE models with any number of preinitation stages and one clonal expansion. Additionally, we determine the identifiable combinations of the generalized MSCE model with up to four clonal expansion stages, and conjecture the results for any number of clonal expansion stages. The results improve upon previous work in a number of ways and provide a framework to find the identifiable combinations for further variations on the MSCE models. Finally, our approach, which takes advantage of the Kolmogorov backward equations for the probability generating functions of the Markov process, demonstrates that identifiability methods used in engineering and mathematics for systems of ODEs can be applied to continuous-time Markov processes.
Indexed as
Identifiers
What OpenQuestion holds
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.