Glossary

AttributeDescription
Approach

Guiding Philosophy

Comprehensive

Models taking a comprehensive approach postulate explicit processes for phenomena that cannot be directly observed. For example, growth rates for untreated pre-cancerous conditions are not typically observed but may be estimated (assuming a particular growth model) from autopsy data.

Observation Focused

Observation focused models are constructed using only observed data with no explicit modeling of unobserved processes behind the observed data.

Primary Purpose

While models are often multi-purpose, this property describes the effort's initial primary goal. Not subsequent or ancillary uses.

Screening evaluation

Screening evaluation includes both program evaluation (i.e.: selective screening targets, intervals, and related protocols) and evaluation of screening tests in the context of assumptions regarding natural history, incidence, prevalence and test dissemination.

Epidemiological analysis

Models with this primary purpose are focused on causes and effects of disease in individuals and populations and in forming public heath decisions via simulation that synthesizes and extends empirical data.

Policy evaluation

Models with this primary purpose are concerned with the evaluation of actual or proposed public health policies and their impact on a population's health.

Features

This property group addresses specific features modeling teams have chosen to include in models.

Intervention

Prevention

Features concerned with evaluation of methods and effects of approaches to cancer prevention.

Screening

Features concerned with detection of pre-clinical cancer in individuals or sub-populations.

Treatment

Features involving the treatment of detected disease, often differentiated by temporal variables (e.g.: calendar year, age) and/or mode of detection.

Natural History

Metastases

Features that account for new cancer in non-primary locations after initial diagnosis.

Recurrence

Features that explicitly allow for cancer to reoccur in the same individual after initial detection and possible treatment. Does not include models that implicitly account for recurrence via long-term survival statistics.

Tumor Growth

Models that include the concept of a defined tumor with a defined size that can change over time, possibly influencing detectability and treatability.

Biomarker

Models that include generation of marker levels such as tumor-specific antigens separate from tests for those biomarkers.

Precancer

Features that account for precancer

Virus

Features that account for a virus (such as HPV) after initial diagnosis

Other

Features other than dysplasia and virus that account for precancer after initial diagnosis

Construction

Approach

Micro Simulation

Computer models that operate at the level of individuals or smaller entities such as tumors or cells are considered 'micro simulations'. Micro simulations may involve sub-components that are analytical in nature. That is, micro simulations may involve sub-components that are strictly analytical, but the complete model cannot be represented as a closed form equation.

Macro Simulation

Computer models that operate at the level of populations (i.e. not individuals). Micro simulations may involve sub-components that are strictly analytical, but the complete model cannot be represented as a closed form equation.

Analytic

Analytic models are mathematical models based on closed form equations that represents a system in terms of logical and quantitative relationships.Software may be employed in automation of calibration and other result generation, but the core model is described in terms of equations not software.

Agent-based

An agent-based model is a micro simulation that allows for interaction between simulated entities.For example, smoking behavior in a simulated individual may be influenced by the behavior of other simulated individuals.

Dynamic Transmission

Dynamic Transmission models direct and indirect effects from communicable diseases that can't be effectively modeled through other methods. Dynamic Transmission models in the cervical sites model the risk of HPV infection depending on the number of new partners, the prevalence of HPV in the population, and the probability of HPV transmission per susceptible-infected partnership.

Methods

Longitudinal

Longitudinal models represent sequences of individual clinical observations or events, typically with time as independent variable, often with event probabilities conditional on previous event outcomes.

Likelihood optimization

Models using this approach estimate parameters by maximizing a defined likelihood function. That is, maximizes the agreement of the model's outputs with observed data.

Stochastic process

Models involving stochastic processes that employ probability distributions that change over time.

State Transition

State transition models posit a set of discrete states that the unit of analysis (typically an individual person) can be in at any time. The conditional probabilities of transitioning between states over a given interval of time are established as model parameters. An individual's longitudinal histories can be generated. Variants of Markov models fall into this category.

Time to Event

Time to event models utilize time-based event probability distributions to determine the time (typically indexed by age) of various events. These distributions may be conditional. A variant of Monte-Carlo methodology is typically employed in these models.

Unit of Analysis

This property defines the smallest entity the model is concerned with. While models may aggregate to person or population levels, those aggregations may be generated on the basis of explicit simulation of individual tumors or cells. This property clarifies the lowest level of explicit simulation.

Data Source

BCSC

Breast Cancer Surveillance Consortium 
http://breastscreening.cancer.gov/

Two County Study

Two County Trial Data (Sweden)

Malmo

Mammographic screening and mortality from breast cancer: The Malmo mammographic screening trial

CNBSS

Canadian National Breast Cancer Screening Studies

WCRS

Wisconsin Cancer Reporting System

NHS

Nurses' Health Studies
http://www.nurseshealthstudy.org/

HPFS

Health Professionals' Follow-up Study
https://www.hsph.harvard.edu/hpfs/

CPS I

Cancer Prevention Study I

CPS II

Cancer Prevention Study II
http://www.cancer.org/research/researchtopreventcancer/currentcancerpreventionstudies/cancer-prevention-study

PALEBA

Nationwide network and registry of histo- and cytopathology in the Netherlands

PROSPR

Population-based Research Optimizing Screening through Personalized Regimens
http://healthcaredelivery.cancer.gov/prospr/

KPNC

Kaiser Permanente Northern California

Census

Cancer Registry

SEER

Surveillance, Epidemiology, and End Results
http://seer.cancer.gov/

NMHPVPR

New Mexico HPV Pap Registry
http://hpvprevention.unm.edu/NMHPVPR/

FCR

Finnish Cancer Registry
http://www.cancer.fi/syoparekisteri/en/

NSW-CCR

New South Whales (NSW) Central Cancer Registry (CCR)
http://www.cancerinstitute.org.au/data-and-statistics/cancer-registries/nsw-cancer-registry

ACD

Australian Cancer Database
http://www.aihw.gov.au/australian-cancer-database/

NCR

Netherlands Cancer Registry

NMD

National Mortality Database
http://www.aihw.gov.au/deaths/aihw-deaths-data/#nmd

VCCR

Victorian Cervical Cytology Register

Other

Other Cancer Registry not listed

Linked

A linked data set is a combination of otherwise separate data sets whereby records have been linked via matching of attributes or common identifiers.

SEER-Medicare

The SEER Medicare data consists of a linkage of the clinical data collected by the SEER registries with claims for health services collected by Medicare for its beneficiaries.

Other

Other Linked data source not listed

Clinical Trial

ERSPC

European Randomized Study of Screening for Prostate Cancer
http://www.erspc-media.org/

PCPT

Prostate Cancer Prevention Trial
http://www.cancer.gov/clinicaltrials/noteworthy-trials/pcpt/Page1

PLCO

Prostate, Lung, Colorectal, and Ovarian trial
http://prevention.cancer.gov/plco

SPCG-4

Scandinavian Prostate Cancer Group-4 randomized trial
http://www.ncbi.nlm.nih.gov/pubmed/23271778

HIP

Health Insurance Plan Study

WHI PHT

Women’s health Initiative (WHI) Postmenopausal Hormone Therapy Trials
http://www.nhlbi.nih.gov/whi/background.htm

National Polyp Study

National Polyp Study

NLST

National Lung Screening Trial
http://www.cancer.gov/clinicaltrials/noteworthy-trials/nlst

POBASCAM

Population Based Screening Study Amsterdam
http://www.ncbi.nlm.nih.gov/pubmed/15054873

NTCC

New Technologies for Cervical Cancer screening trial

Other

Other Clinical trial not listed

Survey

NCCN

National Comprehensive Cancer Network
http://www.nccn.org/

SEER POC

NCI SEER Patterns of Care
http://appliedresearch.cancer.gov/poc/

NHIS

National Health Interview Surveys
http://www.cdc.gov/nchs/nhis.htm

NHANES

National Health and Nutrition Examination Surveys
http://www.cdc.gov/nchs/nhanes.htm

BRFSS

Behavioral Risk Factor Surveillance System
http://www.cdc.gov/brfss/

NHDS

National Hospital Discharge Survey
http://www.cdc.gov/nchs/nhds/

ASHR

Australian Study of Health and Relationships
http://www.ashr.edu.au/

Other

Other survey not listed

Meta Analysis

EBCTCG

Early Breast Cancer Trialists' Collaborative Group
http://ipdmamg.cochrane.org/early-breast-cancer-trialists-collaborative-group

Assumptions

Benefit Factors

Screening

Stage Shift

Benefit from early detection is realized, in part or in whole, by reduction of stage at time of detection and treatment.

Size at diagnosis

Benefit from early detection is conveyed implicitly based on reduced tumor size at the time of detection and treatment independent from that which may already be accounted for in stage shift benefit.

Cure Rate

Benefit is attributed based on a cure/no-cure rate that is advantageous for screen-detected cancers

Lead Time

Lead-time is included as a survival benefit

Modality

Benefit varies depending on method of screening, separate from screening performance characteristics. This factor can be used to capture benefits from within-stage shifts.

Prevention

Benefit is accrued due to prevention of cancer as a result of screening.

Treatment

Estrogen receptor (ER) Status

Treatment benefit is allocated based on estrogen receptor (ER) status

Modality

Benefit varies depending on treatment method, which may in turn depend on dissemination of treatment modality.

Precancer

Benefit is accrued due to treatment of precancer lesions

Vaccination

Prevention

Benefit is accrued due to prevention of cancer as a result of vaccination

Inputs

Incidence

Cancer incidence

Other Conditions

Typically non-cancerous conditions that affect screening. For example, gastroesophageal reflux disease and Barrett's esophagus.

Screening

Attendance

Model inputs concerned with screening attendance and the uptake of disseminated screening opportunity. Models that simulate this aspect of screening often employ economic aspects of promotion and human behavior.

Dissemination

Inputs concerned with the availability of screening and possibly specific screening modality.

Test Performance

Inputs concerned with the performance of screening at the individual test level.

Effect

Inputs concerned with the effects of screen detection on an individual's treatment and survival.

Risk Adaptive Factors

Inputs to inform modification of regular screen schedules (test interval, thresholds etc) based on test results (eg marker levels).

Incidental Finding Surveillance

Certain pre-cancerous findings during screening may trigger non-routine surveillance for which attendance parameters may be an input to the model. For example, post-adenoma follow-up for Colorectal cancer, or follow-up for Esophageal Adenocarcinoma (EAC) after diagnosis of Barrett's Esophagus (BE).

Diagnosis

Precancer

e.g. Diagnosis of CIN 1, CIN2/3

Attendance

Inputs associated with attendence for CIN diagnosis and update of treatment

Test Performance

Inputs associated with test performance for CIN diagnosis

Treatment

Dissemination

Model inputs concerned with the availability of specific treatments. For example, treatment availability may depend on calendar year and/or geographic region.

Efficacy

Model inputs concerned with the capacity for beneficial change in laboratory studies or clinical trials.

Effect

Model inputs concerned with the capacity for beneficial change in clinical practice.

Precancer

Model inputs associated with the treatment of precancer lesions

Attendance

Model inputs associated with the attendance of precancer treatment appointments

Efficacy

Model inputs associated with the efficacy of treatment of precancer lesions

Survival

Observed

The actual percentage of patients still alive at some specified time after diagnosis of cancer. It considers deaths from all causes, cancer or otherwise.

Relative

An estimate of the percentage of patients who would be expected to survive the effects of their cancer.

Mortality

Other cause

Inputs concerned with the probability of death at a certain age from causes other than the disease under investigation.

Disease-specific

Inputs concerned with the probability of cause of death being due to the disease under investigation vs other general causes.

Tumor Attributes

Inputs concerned with distribution of certain attributes of tumors. For example, growth rates, diffusion, and treatability.

Smoking History

Inputs concerned with factors that may influence incidence or treatment of disease. For example, smoking history, obesity, diet.

Risk Factor

Inputs concerned with risk factors the model considers specifically as opposed to implicitly in other model inputs.

Obesity

Inputs concerned with obesity possibly measured by any standard index (e.g. Body Mass Index)

Age

Inputs that drive age-based risk in addition to typical age-specific parameters such as incidence and survival

Family History

Inputs relating to the medical history of the individual's family (not the individual themselves)

Personal History

Inputs relating only to the individual (not the individual's family)

Parity

The number of times an individual has given birth

OC use

Oral contraceptives use

Other

Other inputs concerning factors not covered above. For example: Hereditary nonpolyposis colorectal cancer (HNPCC), Irritable Bowel Syndrome (IBS) and workplace exposure to carcinogens.

Demography

Demographic inputs concerned with the simulated population.

Natural History

Inputs concerned with the underlying natural history of the disease under investigation.

Cost

Inputs concerned with tracking costs of screening, vaccination, treatment. Costs may be relative to society, provider, or patient, and may be nominal or adjusted.

Vaccination

Quadrivalent

Quadrivalent (4v) vaccine

Bivalent

Bivalent (2v) vaccine

Nonavalent

Nonavalent (9v) vaccine

Efficacy

Level and duration of vaccination

Outputs

Cost

Outputs concerned with reporting costs of alternative interventions.

Tumor size

Outputs involving measurement of tumor size over time or at specific time points such as clinical or screen diagnosis

Cell Dynamics

Outputs providing data on simulated cells. For example: cell kinetics, Temporal trends on cell kinetics, spatial growth of High grade dysplasia (HGD) and Esophageal Adenocarcinoma (EAC) clones

Incidence

Outputs concerning in incidence of disease

HPV Infection

Presence of Human Papillomavirus (HPV) Infection

Prevalence

Other Conditions

Other conditions other than precancer and cancer (such as HPV Infection)

Treatment

Effect

Outputs regarding the effect of treatments, often conditioned on age, period, and state of disease progression at the time of treatment.

Effect

Outputs regarding the effect of the treatment of precancer lesions

Precancer

Screening

Effect

Outputs regarding the effect of screening

Test Performance

Outputs tracking the test performance as generated by the model. Can be conditioned on follow up time and censoring, or represent actual (often unobservable for negative tests) performance metrics.

Tumor Attributes

Outpus concerned with distribution of certain attributes of tumors. For example, growth rates, diffusion, and treatability.

Risk Factor

Smoking

Outputs pertaining to the patterns of smoking in the simulated population, possibly in the context of simulated interventions.

Natural History

Results drawn from simulated individual's disease including, onset, transitions to malignancy, stage transitions, metastasis, growth rates etc.

Outcomes

Outcomes are the population effects deemed interesting in the context of the interventions or exposures being modeled. Typical outcomes are survival, morbidity, mortality and other such results one would expect to be affected by interventions and trends in risk.

Survival

Measurements of time between diagnosis and death.

Life years

Measurements of difference of life years

QALY

Quality adjusted years of life.

Cause-specific Mortality

Cause of death is accounted for, at least in terms of target disease vs other causes.

All-cause Mortality

Mortality from any cause is provided

Vaccination effect

Effect from vaccination on population, including proportion of cervical disease attributable to vaccination HPV types, vaccination coverage, and duration of vaccine-induced immunity

Screening

False Positives

Allows for an outcome in which the screening event indicates a positive disease state in the absence of the disease.

True Positives

Allows for an outcome in which the screening event indicates a positive disease state in the presence of the disease.

False Negatives

Note that negative disease state may be expressed in terms of a defined follow-up time (observed) and/or be based on simulation data typically unobserved in real world studies. See specific model notes for details.

True Negatives

Note that negative disease state may be expressed in terms of a defined follow-up time (observed) and/or be based on simulation data typically unobserved in real world studies. See specific model notes for details.

Overdiagnoses

Provides measures of the event (which typically cannot be observed) where screening detects cancer that would have otherwise gone undetected with lifetime follow up. That is, would not have surfaced in the person's lifetime.

History

The screening history of individuals and/or sub-populations

Treatment

History

The treatment history of individuals and/or sub-populations

Pregnancy

Pregnancy outcomes from Cervical intraepithelial neoplasia (CIN) treatment

Tested Platforms

Language