Attribute Category | Attribute |
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Approach | |
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Primary Purpose | Screening evaluation,
Epidemiological analysis,
Policy evaluation,
Population trends,
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Features | |
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Intervention | Prevention,
Screening,
Treatment,
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Natural History | Metastases (We model the American Joint Commission on Cancer (AJCC) 6th edition stage criteria (which incorporate tumor growth and metastases)),
Tumor Growth (We model the American Joint Commission on Cancer (AJCC) 6th edition stage criteria (which incorporate tumor growth and metastases)),
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Construction | |
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Approach | Micro Simulation,
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Methods | Likelihood optimization (Model fit was optimized using the Nelder-Mead optimization algorithm),
Stochastic process,
Time to Event,
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Unit of Analysis | Tumor,
Person (We generate person-level data, which can be used to compute (sub-) populations),
Population (We generate person-level data, which can be used to compute (sub-) populations),
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Data Source | NHS (The two-stage clonal expansion model is based on Nurses' Health Studies (NHS) and Health Professionals' Follow-up Study (HPFS) estimates (Meza, 2008, Cancer Causes Control)),
HPFS (The two-stage clonal expansion model is based on Nurses' Health Studies (NHS) and Health Professionals' Follow-up Study (HPFS) estimates (Meza, 2008, Cancer Causes Control)),
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Census | |
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Cancer Registry | SEER,
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Linked | |
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Clinical Trial | PLCO,
NLST,
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Survey | NHIS (Used indirectly, as these were used to develop the Smoking History Generator used by our model),
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Meta Analysis | |
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Assumptions | |
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Benefit Factors | |
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Screening | Size at diagnosis (We do not specifically model size at diagnosis, but model the American Joint Commission on Cancer (AJCC) 6th edition stages. The benefit is based on the detection at a specific stage, through a stage specific cure rate),
Cure Rate (We do not specifically model size at diagnosis, but model the American Joint Commission on Cancer (AJCC) 6th edition stages. The benefit is based on the detection at a specific stage, through a stage specific cure rate),
Modality,
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Treatment | |
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Vaccination | |
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Inputs | |
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Screening | Attendance,
Dissemination,
Effect (Screening effects were estimated from data (output) and later given as an input, however the final effect (output) depends on the characteristics of the population, attendance etc),
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Diagnosis | |
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Precancer | |
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Treatment | Efficacy,
Effect (Treatment effects were estimated from data (output) and later given as an input, however the final effect (output) depends on the characteristics of the population, attendance etc),
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Precancer | |
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Survival | Observed,
Relative,
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Mortality | Other cause,
Disease-specific,
Tumor Attributes,
Smoking History,
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Risk Factor | Smoking,
Demography,
Natural History,
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Vaccination | |
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Outputs | Incidence,
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Disease | Stage Distribution,
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Prevalence | |
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Treatment | Effect (Treatment effects were estimated from data (output) and later given as an input, however the final effect (output) depends on the characteristics of the population, attendance etc),
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Precancer | |
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Screening | Effect (Screening effects were estimated from data (output) and later given as an input, however the final effect (output) depends on the characteristics of the population, attendance etc),
Tumor Attributes,
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Risk Factor | Smoking (Incorporates the NCI's Smoking History Generator),
Demography,
Natural History,
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Outcomes | Survival,
Life years,
Cause-specific Mortality,
All-cause Mortality,
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Screening | False Positives (Indirectly considered),
True Positives,
False Negatives (Indirectly considered),
True Negatives (Indirectly considered),
Biopsies performed (Indirectly considered),
Unnecessary biopsies (Indirectly considered),
Overdiagnoses,
History,
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Treatment | |
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Implementation | |
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Development | |
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Tested Platforms | Windows,
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Language | Delphi,
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