CISNET-DFCI (Dana-Farber)
Cancer Intervention and Surveillance Modeling Network, Dana-Farber Cancer Institute
Model purpose
The purpose of the model is to predict the mortality associated with female breast cancer. The predictions may be by chronological year and/or age. Mortality may change by advances in treatment and/or changing dissemination of screening. The model incorporates the possibility that these latter two factors will change by chronological time and age. The model is general and enables the prediction of changes in mortality if technical advances are made …More details
Visit the model details pageMISCAN-Fadia (Erasmus)
MIcrosimulation SCreening Analysis (MISCAN) Fatal Diameter model
Model purpose
In the MISCAN-Fadia model knowledge on natural history, screening and adjuvant treatment and breast cancer risk derived from randomised controlled trials and observational studies are integrated. In this way, MISCAN-Fadia is helpful in analyzing and explaining results of cancer screening trials, predicting the (cost-)effectiveness of different screening policies and predicting the effect of interventions on future national trends.Host institution
Erasmus University Medical CenterContact
Nicolien van Ravesteyn (n.vanravesteyn@erasmusmc.nl)More details
Visit the model details pageSpectrum/G-E (Georgetown/Einstein)
Simulating Population Effects of Cancer Control Interventions -- Race and Understanding Mortality Georgetown-Einstein
Model purpose
The Georgetown-Einstein model is called 'Spectrum' (Simulating Population Effects of Cancer Control Interventions -- Race and Understanding Mortality). It is a continuous time parallel universes state transition model programmed in C++ object-oriented programming language. The model simulates breast cancer incidence and mortality by ER/HER2 in the absence of screening or adjuvant treatment and then overlays screening and/or treatment.Host institution
Georgetown University Medical Center / Albert Einstein College of MedicineContact
Jeanne Mandelblatt (mandelbj@georgetown.edu)More details
Visit the model details pageBayesian Simulation Model (MDACC)
Model purpose
The goal of our model is to provide estimates (and their associated uncertainties) of the relative contributions of screening mammography, hormonal therapy, and improvements in biologic- and chemotherapy to the observed decrease in U.S. breast cancer mortality since 1990. We consider subsets of disease defined by age and tumor characteristics, including stage and hormone receptor and Human Epidermal Growth Factor Receptor 2 (HER2) statuses. We address the potential impact on …Host institution
University of Texas / M.D. Anderson Cancer CenterContact
Donald Berry (dberry@mdanderson.org)More details
Visit the model details pageBCOS (Stanford)
Breast Cancer Outcomes Simulator
Model purpose
The Breast Cancer Outcomes Simulator (BCOS) was developed for four primary purposes. First, BCOS generates a virtual tumor registry of breast cancer patients diagnosed in the United States since 1975 and, at the individual level, specifies the patient's screening history, mode of detection, adjuvant treatment and survival; second, BCOS quantifies the impact of screening mammography and adjuvant therapy on breast cancer mortality trends from 1975 by molecular subtype; third, BCOS …More details
Visit the model details pageUWBCS (Wisconsin)
University of Wisconsin Breast Cancer Simulation Model
Model purpose
The UWBCS simulates breast cancer in a population over time generating cancer registry-like data sets. By manipulating parametric input assumptions about natural history, screening, and treatment, the model can be used to address a number of important policy questions related to breast cancer screening and treatment.More details
Visit the model details pagePolicy1-Cervix (CCNSW)
Cervical Cancer Model, Cancer Council NSW
Model purpose
The model platform known as ‘Policy1-Cervix’ was developed to address several questions related to cervical cancer, including the impact of cervical screening and vaccination on incidence and mortality, the predicted impact on resource utilisation (such as impact on colposcopy referrals, treatment procedures) and the cost-effectiveness of these interventions across a range of settings. The model has been used for a number of HPV vaccine evaluations, including effectiveness and cost-effectiveness of …More details
Visit the model details pageMISCAN-STDSIM (Erasmus)
MIcrosimulation SCreening Analysis (MISCAN) Sexually Transmitted Diseases Simulation (STDSIM) Model
Model purpose
MISCAN-STDSIM describes the transmission, consequences, and intervention possibilities for multiple sexually transmitted infections. This microsimulation model was originally developed for decision support in STD control. MISCAN-STDSIM was originally developed to model the natural history of cervical disease. It can be used to estimate the costs and effects of screening policies.More details
Visit the model details pageHSPH-Cervical (Harvard)
Cervical Cancer Model
Model purpose
The purpose of the Harvard HPV and cervical cancer models are: to integrate the most up-to-date evidence on the epidemiology of HPV and cervical cancer, as well as clinical practice and delivery of vaccination and screening interventions; to simulate the burden of HPV infection and cervical cancer in populations of interest; to respond to priority policy questions regarding the effectiveness and cost-effectiveness of prevention and control strategies against HPV-related cancers …More details
Visit the model details pageUMN-Cervical (Minnesota)
Model purpose
The group of UMN models (a standalone Markov model, a microsimulation model and a transmission model) were developed to reflect HPV-induced cervical carcinogenesis stratified by HPV 16, 18 and other high-risk HPV types.More details
Visit the model details pageCervical (UW)
Dynamic, compartmental, HPV 16 model
Model purpose
A dynamic compartmental model of HPV 16 infection and progression to cervical cancer (including LSIL and HSIL progression and regression). The model includes both men and women, sexual behavior, smoking, and vaccination strategies of varying coverage and duration as well as screening and treatment. This model was used to estimate the reduction of cervical cancer associated with varying assumptions of HPV 16 vaccine efficacy, duration, and population coverage. Comparative CC …More details
Visit the model details pageMISCAN-Colon (Erasmus/MSK)
MIcrosimulation SCreening Analysis (MISCAN) Colorectal Cancer Model
Model purpose
To simulated CRC incidence and mortality according to observed figures, to estimate the absolute and relative contribution of CRC screening, risk factors and improved therapy on observed cancer incidence and and mortality, to predict how changes in lifestyle, CRC screening and treatment practices will impact on future incidence and mortalityHost institution
Erasmus University Medical Center / Memorial Sloan KetteringContact
Iris Lansdorp-Vogelaar (i.vogelaar@erasmusmc.nl)More details
Visit the model details pageCRC-SPIN (FHCC)
Colorectal Cancer Simulated Population model for Incidence and Natural history
Model purpose
Simulate the natural history of colorectal cancer, including diagnosis and survival after diagnosis, to evalute screening strategies.More details
Visit the model details pageSimCRC (Minnesota/MGH)
Model purpose
The purpose of the SimCRC model is to address questions related to cancer control and prevention. The model has a natural history component that represents the adenoma-carcinoma process; parameters that describe that process were estimated by calibrating to observed data on adenoma prevalence and cancer incidence. To evaluate the impact of a screening program we allow for an underlying adenoma to be detected and removed - potentially interrupting the adenoma-carcinoma …More details
Visit the model details pageEACMo (CUIMC)
Esophageal AdenoCarcinoma Model
Model purpose
The purpose of EACMo is to inform our understanding of esophageal adenocarcinoma (EAC), its epidemiology and natural history, and the efficacy of screening and other interventions to reduce the morbidity and mortality of EAC. EACMo aims to project future trends in EAC incidence and mortality and to explore the impact of hypothetical screening scenarios on EAC incidence and mortality.Host institution
Columbia University Irving Medical CenterContact
Chin Hur (ch447@cumc.columbia.edu)More details
Visit the model details pageMISCAN-ESO (Erasmus/UW)
MIcrosimulation SCreening Analysis (MISCAN) Esophagus Cancer Model
Model purpose
The MISCAN/UW Esophagus adenocarcinoma (EAC) model is constructed for multiple purposes. First, we intend to gain better insight into the natural history of EAC, especially with regards to the process by which cancer develops from Barrett's Esophagus (BE). Secondly, the model will be used to identify the driving factors for the substantial increase in EAC incidence over the last several decades. The model will be able to inform investigators which …Host institution
Erasmus University Medical Center / University of WashingtonContact
Amir Omidvari (a.omidvari@erasmusmc.nl)More details
Visit the model details pageMSCE-EAC (FHCC)
MultiStage Clonal Expansion model for Esophageal AdenoCarcinoma
Model purpose
To provide a mathematical and computational framework for multiscale modeling of the natural history of progression from normal esophageal squamous epithelium to esophageal adenocarcinoma (EAC). Fundamental scales in the MSCE-EAC (MSEAC) model include the cell, crypt, clonal patch, tissue [normal, Barrett's esophagus (BE), high grade dysplasia (HGD), and EAC], individual, and population levels. The development of BE is recognized as an early step in progression to EAC, with an enhanced …More details
Visit the model details pageMISCAN-Lung (Erasmus)
MIcrosimulation SCreening Analysis (MISCAN) Lung Model
Model purpose
The MISCAN-lung model was designed to simulate population trends in lung cancer (LC) for comprehensive surveillance of the disease, to relate past exposure to risk factors to (observed) LC incidence and mortality, and to estimate the impact of cancer-control interventions. MISCAN-lung employs the technique of stochastic microsimulation of life histories affected by risk factors. It includes the two-stage clonal expansion model for carcinogenesis and a detailed LC progression model; the …More details
Visit the model details pageLMO (FHCC)
Longitudinal Multistage Observation Model
Model purpose
To provide a mechanistic mathematical model of lung cancer development by histology for estimating the impact of alternative screening protocols to reduce lung cancer mortality in the US. The longitudinal multistage observation (LMO) model of lung cancer development and detection includes six pathways representing distinct histological subtypes (bronchioloalveolar, adenocarcinoma, large cell, squamous, other non-small cell, and small cell). On each pathway, the model represents five stages that may occur during …Host institution
Fred Hutchinson Cancer Research CenterContact
William Hazelton (hazelton@fhcrc.org)More details
Visit the model details pageSimSmoke (Georgetown)
SimSmoke Tobacco Control Policy Simulation Model
Model purpose
The SimSmoke tobacco control simulation model is a discrete Markov model that projects smoking prevalence and smoking-attributable deaths in the absence of policy change, and then estimates the effect of tobacco control policies on those outcomes. The model has been described in a series of more than 50 articles, and has been shown to predict well at the nation (more than 20) and state (10) level. The policy effects are …More details
Visit the model details pageSmoking-Lung Cancer Model (Georgetown)
Smoking-Lung Cancer Model
Model purpose
The Georgetown University Smoking-Lung Cancer Model is a statistically-based macro level model that considers the relationship uses between lung cancer and current, former and never smoking prevalence data from the NHIS aggregated by age and gender.More details
Visit the model details pageLCPM (MGH)
Lung Cancer Policy Model
Model purpose
The original single-cohort LCPM was designed to evaluate the effectiveness, costs, and cost-effectiveness of helical computed tomography (CT) screening for lung cancer in the U.S. The single-cohort model can also be used to evaluate both future screening technologies and advances in treatment effectiveness.Host institution
Massachusetts General Hospital Institute for Technology AssessmentContact
Joey Kong (joey@mgh-ita.org)More details
Visit the model details pageUM-LCSc (Michigan)
University of Michigan Lung Cancer Screening Model
Model purpose
Assess the impact of lung cancer screening strategies in the USMore details
Visit the model details pageUM-LCSm (Michigan)
University of Michigan Lung Cancer Smoking Model
Model purpose
The UM-LCSm model was developed to evaluate the impact of changing tobacco consumption due to tobacco control policy on lung cancer mortality in US population.Host institution
University of MichiganContact
Jihyoun Jeon; Suresh Moolgavkar (jihjeon@umich.edu; moolgavkar@gmail.com)More details
Visit the model details pageLCOS (Stanford)
Lung Cancer Outcomes Simulator
Model purpose
We developed a microsimulation model to evaluate the effectiveness of various lung cancer screening strategies by modeling lung cancer development, progression, detection and survival by estimating model parameters using NCI SEER data.More details
Visit the model details pageYLCM (Yale)
Yale Lung Cancer Model for population rates
Model purpose
This population based model provides estimates of trends in lung cancer mortality rates using quantitative formulae derived from analytical epidemiology studies for the effect of cigarette smoking.More details
Visit the model details pageMISCAN-PRO (Erasmus)
MIcrosimulation SCreening Analysis (MISCAN) Prostate Cancer Model
Model purpose
The MISCAN simulation model has been developed for estimating the effect of prostate cancer screening in a dymamic population, to explain results of prostate cancer screening trials and to predict and compare the cost-effectiveness of different screening policies. Also, the model can be used to estimate unobservable processes and variables (such as the natural history of the disease, overdiagnosis and lead time).Host institution
Erasmus University Medical CenterContact
Eveline Heijnsdijk (e.heijnsdijk@erasmusmc.nl)More details
Visit the model details pagePSAPC (FHCRC)
A joint model of prostate-specific antigen (PSA) growth and prostate cancer (PC) progression
Model purpose
The original objective behind modeling prostate cancer trends was to disentangle the roles of PSA screening and changes in primary treatment patterns in US prostate cancer incidence and mortality trends. While both prostate cancer incidence and mortality rates have continued to fall since the early 1990s, the relative contributions of screening and treatment to the observed declines in mortality remain intensely debated. Additional applications of the model, supporting by its …More details
Visit the model details pageSCANS (Michigan)
Self-Consistency Analysis of Surveillance
Model purpose
This is a model of prostate cancer initiation, incidence, disease progression and presentation at diagnosis, survival and mortality. The model provides a quantitative functional link between the history of prostate cancer brom birth to death and screening and treatment as control interventions. It is used to understand, predict and optimize the impact of dynamic sceening and treatment. Its purpose is to unravel the myriad causes and relationships that underlie recent …More details
Visit the model details pageInstructions: Filter by cancer site using buttons below and then rank models by relevance based on selected attributes and indicate models for side-by-side comparisons. Note that you may also select a single model to view in a grid.
Help
Instructions: The details of the models you chose in the search tool are shown below. Rows highlighted in green indicate attributes that you selected in the attribute tree. Hover over model title and click for more details
Tip: Hover your cursor over the dashed attribute links or the model titles for more information. Click a model title to view more details without leaving this page.
1. Filter by cancer site
2. Select attributes
How to use this tool
Steps
- Filter by cancer site using the buttons below (Breast, Colorectal, ...) to narrow the results of your search.
- Select attributes of your desired model in the in one of two ways:
- Navigate the attribute tree
- Use the text-box and its typeahead features to populate the attribute tree
- Select models to compare in the pane to the right (ranked by their match quality)
- Select at least one and at most eight models by checking their checkboxes to the right of each package
- When you are ready, click the Compare button to view the comparison table
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