Surveillance Research Program

Strategic Plan

2010

National Cancer Institute

U.S. DEPARTMENT

OF HEALTH AND

HUMAN SERVICES

National Institutes

of Health

Division of Cancer Control and Population Sciences
“... collect, analyze, and disseminate all data useful in the

prevention, diagnosis, and treatment of cancer ...”

(The National Cancer Act of 1971 [P.L. 92-218]).

The National Cancer Institute’
Contents

Executive Summary 1

Chapter 1: Introduction 7

	 The Surveillance Research Program 8

	 Development of the Vision for the NCI Surveillance Research Program 10

Chapter 2: NCI’s Surveillance Research Program: Where Are We Now? 13

Collection and Management of Data 14

Developing Statistical Methods and Modeling 16

Methods To Analyze Population Registration Data 16

Modeling and Interpreting Trends 21

The Surveillance Research Program’s Biostatistical Methodology Portfolio 21

	 Reporting and Interpreting Data 23

Chapter 3: The Strategy: Where Do We Go From Here? 25

Priority Area 1: Provide a more complete depiction of the burden of cancer in 

the United States. 28

Priority Area 2: Collect better quality data on the burden of cancer in the United 

States more efficiently through effective utilization of electronic tools. 31

Priority Area 3: Improve understanding of the differences and disparities in the 

burden of cancer in the United States. 33

Priority Area 4: Better understand the continuum in the burden of cancer in the 

United States from risk to prognosis. 35

Priority Area 5: Communicate cancer statistics more effectively to researchers and users 

and make the data more accessible and understandable to all. 37

Appendix A: Acknowledgements 41

Appendix B: NCI Resources and Tools for Cancer Surveillance Research 42

Appendix C: List of Acronyms 43

1

Cancer accounts for nearly one-fourth of all deaths in the 

United States and ranks high among causes of mortality. 

Monitoring cancer trends and related factors is critical to reducing the burden of cancer. The National Cancer Institute 

(NCI) supports cancer surveillance research to study these 

trends and quantitatively measure cancer incidence, morbidity, survival, and mortality for persons with cancer in 

the United States. Cancer surveillance also assesses genetic 

predisposition, environmental and behavioral risk factors, 

screening practices, and the quality of care from prevention 

through palliation. In short, cancer surveillance measures 

progress made toward reducing the burden of cancer and 

provides a basis for advancing research and interventions 

across the cancer control continuum in prevention, early 

detection, diagnosis, treatment, and outcome. Appropriate 

decisions in science and public health depend on reliable 

data about the impact of efforts to control cancer, and 

cancer surveillance research provides a basis for the public 

to assess the efficacy of National efforts to detect and treat 

cancer in the general population.

NCI’s Surveillance Research Program (SRP), housed within 

the Division of Cancer Control and Population Sciences 

(DCCPS), directs the collection and dissemination of information on cancer incidence, prevalence, mortality, and 

survival in the United States. SRP collaborates with other 

DCCPS Programs in these efforts, particularly with the 

Applied Research Program (ARP), which addresses risk 

factors, health behaviors, health care services, economics, 

and outcomes, including patient-reported outcomes. In addition, the Behavioral Research Program (BRP) complements SRP and ARP efforts in cancer surveillance research 

through a portfolio of behavioral interventions, such as tobacco use, screening, dietary behavior, and sun protection. 

The programs, priority areas, and action plans described in 

this Strategic Plan are focused on activities managed by SRP.

SRP also works with other entities within NCI, the National 

Institutes of Health (NIH), and the Centers for Disease 

Control and Prevention (CDC), and with other federal 

agencies, and has developed strong partnerships with cancer registry and surveillance organizations. SRP’s rigorous 

quality standards have made it a model for cancer surveillance activities throughout the world.

One of SRP’s premier projects is the Surveillance, 

Epidemiology, and End Results (SEER) Program, which 

collects and manages high-quality data from cancer registries in specific geographic areas that represent 28 percent 

of the U.S. population. These data are used increasingly to 

Executive Summa
Executive Summary

Executive Sum

2

answer questions about cancer etiology, prevention, treatment, and control. SEER collaborates with other agencies 

and programs to link with additional datasets and enhance 

cancer care and outcomes data. These include Medicare, 

Medical Health Outcomes Survey (MHOS), Consumer 

Assessment in Healthcare Providers and Systems (CAHPS), 

and the National Longitudinal Mortality Study (NLMS). 

The SEER Residual Tissue Repository (RTR) Program also 

plays an important role in data collection and management.

During the past decade, SRP has fostered standardization 

practices across the cancer surveillance field. The standardization of cancer registry data elements has allowed investigators to compare cases, treatments, and outcomes to support cancer research activities and promote public health. 

In addition, SRP has promoted the adoption and use of a 

standardized vocabulary across the surveillance arena. NCI 

is an active participant in the American Joint Committee on 

Cancer’s (AJCC) collaborative staging efforts, works with 

numerous partners to standardize data items across the cancer registry community, and works to develop infrastructure 

and quantitative methodologies. 

During the past 10 years, SRP also has built a substantial collection of methodologies for statistical analysis software, applications, and modeling, including the 

Cancer Intervention and Surveillance Modeling Network 

(CISNET), SEER*Stat, Joinpoint, and DevCan. These data 

and tools are used to distill cancer trends in populations, 

and to track health disparities among subpopulations. SRP’s 

grant portfolio for biostatistical cancer research is available 

online through the StatFund resource.

Integrating SRP activities with other NCI and NIH efforts 

helps to advance the battle against cancer. For example, 

the SEER Data Management System (SEER*DMS), used 

by cancer registries across the United States, complies with 

the philosophy and guidelines of the cancer Biomedical 

Informatics Grid (caBIG®), which provides an information 

network that allows members of the cancer community to 

share data and knowledge openly, in a standardized manner, using a common vocabulary. In addition, NCI’s Center 

for Bioinformatics and SRP collaborate on electronic data 

capture activities, including by co-sponsoring the Tools for 

Electronic Data request for proposals (RFP). 

SRP communicates cancer statistics to a variety of data 

users through online tools and annual and biennial reports. 

Cancer Control P.L.A.N.E.T. (Plan, Link, Act, Network 

with Evidence-based Tools), State Cancer Profiles, Cancer 

Statistics Review (CSR), and FastStats are among the Webbased resources instituted by SRP during the past decade. 

The Annual Report to the Nation describes cancer trends in the 

United States, and the Cancer Trends Progress Report highlights 

progress made against cancer in relation to Healthy People 

2010 initiative targets. 

Ten years following the Cancer Surveillance Research 

Implementation Plan of 1999, SRP convened extramural scientists and NCI staff with expertise in cancer surveillance 

to develop a vision for the next 5 to 7 years. In 2008, staff 

from SRP, DCCPS’ Applied Research Program (ARP), and 

SEER-supported registries began a process to determine 

the future direction for SEER through a series of meetings entitled “SEER: Visioning the Future.” The consensus 

was that the SEER Program should assess its impact on 

the field of population surveillance, especially in terms of 

the quality and depth of its data, accessibility and usability 

of data, leadership in defining data elements, innovation 

in data collection, and development of analytical methods 

applied to surveillance data. 

SRP also held a more comprehensive strategic planning 

workshop in October 2009 to address scientific opportunities, challenges, and gaps in the cancer surveillance arena, 

with an emphasis on incidence, prevalence, mortality, and 

survival. Extramural participants and NCI staff considered 

the scope and content of cancer surveillance, analytic methods and models, integrated health informatics, populations 

with regard to differences in risk and prognosis, and health 

communication. 
3

Priority Areas and Action Plan 2

Priority Area1 Provide a more complete depiction of the burden of cancer in the United 

past, present, and future.

Action Plan

1. Link with other data sourc

2. Develop processes to obtain and use other types of variables and d

3. Collect more detailed individual data from a limited number of registr

Priority Area 2 Collect better quality data on the burden of cancer in the United Stat

efficiently through effective utilization of electronic tools.

Action Plan

4. Identify and evaluate relevant data sourc

5. Automate the capture of informat

6.    Expand interoperability and standardizati

surveillance data.

7. Use data-capture techniques and statistical modeling to improve the timelines

cancer statistics reporting.

Priority Area 3 Improve understanding of the differences and disparities in the bu

cancer in the United States.

Action Plan

8.   Enhance measurements of health disparit

9.   Expand data visualization and interpretat

Priority Area 4 Better understand the continuum in the burden of cancer in the United

from risk to prognosis.

Action Plan

10.   Evaluate the potential impact of prevention, early detection, and treatment on ca

statistics to inform cancer research and policy development.

11. Identify and track a cohort of cancer patients to obtain data and monitor impacts at t

population level.

Priority Area 5 Communicate cancer statistics more effectively to researchers and us

make the data more accessible and understandable to all.

Action Plan

12. Conduct needs assessment to identify data needs and audiences and develop a plan 

communicate cancer statistics and interpret data for audiences.

13. Produce standardized communication materials.

14. Disseminate information on existing resources to researchers and evaluate their effectivenes

15. Develop and disseminate methods and software for the analysis and presentation of Nation

cancer statistics.

Table 1. Cancer Surveillance Priority Areas and Action Plan, 2010

3

Cancer Surveillance Program Straticlan 
4

Executive Sum

Results from the SEER Visioning and SRP strategic planning efforts resulted in this Strategic Plan for the future 

direction of NCI-supported cancer surveillance research. 

This Executive Summary presents an overview of the 

Strategic Plan’s four priority areas and 15 research opportunities in these areas (Table 1). The full report provides 

more detailed information about NCI-supported cancer 

surveillance research efforts and future directions. 

Priorit

Provide a more complete depiction of the 

burden of cancer in the United States, past, 

present, and future.

Action Plan 1: Link with other data sources. New 

technologies such as geographic information systems (GIS) 

and the electronic health record (EHR) provide an opportunity to increase data collection in a cost-effective manner. 

Expansion should be accomplished by establishing linkages 

between SEER, the NCI Cancer Human Biobank (caHUB®) 

and caBIG®, and the National Cancer Data Base (NCDB), 

as well as deeper data mining of Centers for Medicare and 

Medicaid Services’ (CMS) Medicare data. Other data linkages might be established with larger insurance systems and 

professional societies. The relationship between reporting 

sources and the contents of the registries’ summary records 

should be explored to maximize the benefits achieved from 

linked data. 

Action Plan 2: Develop processes to obtain and use 

other types of variables and data. As understanding 

of risk, prognosis, and population differences advances, 

more individual data should be collected to support this 

progression. Processes that are developed to collect and 

use these data should be sensitive to privacy, patient consent, and legal issues, and be designed with the flexibility 

to accommodate changes in scientific advances. Better ways 

to support an expanded collection of patients’ treatment 

history, disease status, and followup, as well as genomic 

data, should be established to facilitate access to multilevel 

data from multiple sources while maintaining patient data 

confidentiality. A process for using U.S. Census information to link surveillance data to other data types, such as 

biomarker data, should be developed.

Action Plan 3: Collect more detailed individual data 

from a limited number of registries. The collection of 

quality data should be expanded by capturing more detailed 

individual data and specimens on a limited basis. A select 

number of registries should focus on acquiring and maintaining a high-quality collection in a specific area for several 

years, with relevant information about the process applied 

when planning future collection efforts. Partnerships also 

could be established with other successful collection efforts.

Priority

Collect better quality data on the burden of 

cancer in the United States more efficiently 

through effective utilization of electronic tools.

Action Plan 4: Identify and evaluate relevant data 

sources. Data sources are needed that expand the current 

boundaries of the SEER dataset and support the emphasis on 

population-based cancer surveillance. Relevant sources should 

be examined for the availability of electronic data that can be 

linked. Ongoing collaborations, such as those with CMS for 

the SEER-Medicare dataset, should be evaluated for the potential to obtain further information, including treatment data.

Action Plan 5: Automate the capture of information. 

The nationwide movement toward adopting EHRs provides 

a prime occasion to further automate the capture of cancerrelated information. In addition, use of natural language processing (NLP) should be explored to ensure greater accuracy 

in the extraction of information from hospital reports.
5

Cancer Surveillance Program Straticlan 

Action Plan 6: Expand the interoperability and 

standardization of surveillance data. Standards for 

the collection and coding of health data from a variety of 

sources are needed to ensure the consistent quality and interoperability of data. Cancer surveillance organizations, 

researchers, and clinicians should form mutually beneficial 

collaborations to impose a structure for data collected at 

the point of care. Standardization should help to simplify 

the process of recording medical information by clinicians. 

Action Plan 7: Use data-capture techniques and 

statistical modeling to improve the timeliness of cancer statistics reporting. Methods should be developed to 

reduce the current 3-year time period between diagnosis, 

notification of the registry about a case, and reporting of 

incidence rates by the registry. New data-capture techniques 

and statistical modeling could provide a means to expedite 

the reporting of cancer statistics, such as through a delayadjustment model. In addition, a data-alert system should 

be designed to inform registrars about new information 

added to a case chart and lessen the need to review data 

that were evaluated previously. 

Priorit

Improve understanding of the differences 

and disparities in the burden of cancer in the 

United States.

Action Plan 8: Enhance measurement of health disparities. Methods for measuring health disparities and encouraging the use of tools such as the health disparities calculator (HD*Calc) should be improved. Census tract data 

on area socioeconomic status and other disparities indicators should be evaluated for their potential to link to cancer 

surveillance data and use in analyzing cancer disparities. 

Action Plan 9: Expand data visualization and interpretation. To better identify and quantify cancer risk and 

prognosis, data about subpopulations should be identified 

geographically and analyzed. Methods of data visualization 

and interpretation should be more fully explored, including 

geocoding techniques, spatial data, and spatial-temporal data, 

to determine relevant factors influencing risk or prognosis. 

Priority

Better understand the continuum in the 

burden of cancer in the United States, from 

risk to prognosis.

Action Plan 10: Evaluate the potential impact of 

prevention, early detection, and treatment on cancer 

statistics to inform cancer research and policy development. Through the understanding of risk factor trends, 

use of early detection and treatment, and information on 

risks and efficacy from clinical trials, models can predict the 

impact on cancer incidence and mortality trends, as well as 

provide estimates of potential impact if interventions were 

more widely implemented in the population. 

Action Plan 11: Identify and track a cohort of cancer 

patients to obtain data and monitor impacts at the 

population level. Tracking a cohort of cancer patients 

through the registry system will provide a comprehensive 

approach to population studies that is unavailable through 

multiple independent cancer registries and individual investigator-initiated studies. Guidance is needed for developing 

collaborative cohorts in the registry system, with access to 

cohort data available to investigators who are not participating in the cohort. The registry system would provide 

nationally representative samples to supplement data from 

more localized groups, allowing the opportunity to examine 

socioeconomic factors and other health disparities variables.
6

Executive Sum

Priorit

Communicate cancer statistics more effectively 

to researchers and users and make the data 

more accessible and understandable to all.

Action Plan 12: Conduct a needs assessment to identify data needs and audiences, and develop a plan to 

communicate cancer statistics and interpret data for 

audiences. A strategy should be developed to communicate cancer statistics and help researchers and other audiences interpret and report data with accuracy. The needs of 

various audiences, ranging from scientists to policymakers 

to the general public, should be evaluated and a plan to 

communicate cancer statistics should be developed that 

distinguishes approaches for these audiences. The needs 

assessment should identify existing products, ascertain the 

need for new materials, and recommend refinements to 

existing resources to encourage more effective use. The 

plan should outline steps to promote existing resources as 

well as advise on the translation of data and preparation of 

other communication materials for lay audiences.

Action Plan 13: Produce standardized communications materials. To present a coherent message about cancer rates and help reduce the misinterpretation of statistical 

data, standardized communication materials are needed. 

Standards should be adopted by leading organizations regarding a clear and unified message about understanding 

and using surveillance statistics. Consensus about common 

data elements and the creation of standardized templates 

that present data to different audiences should be initial 

steps toward this goal. Explanation of data limitations and 

cross-links between the SEER Web site and other credible 

Web resources should be provided to help researchers use 

data more effectively. 

Action Plan 14: Disseminate information on existing resources to researchers and evaluate their effectiveness. Better methods to promote existing resources to 

researchers are needed. A systematic, heuristic review of 

existing resources and how they are promoted and accessed 

should be conducted to provide a structured approach that 

promotes these resources to the wider research community. 

SEER-Medicare has been used widely by the research community and provides a model of how research resources 

can be used to address a range of research questions. NCI 

also could facilitate access to local cancer surveillance data 

and make data from SEER and other registries more readily available. 

Action Plan 15: Develop and disseminate methods 

and software for the analysis and presentation of national cancer statistics. Development of new methods 

and software to analyze and present national cancer statistics will help invigorate the field and inform research conducted on earlier stages of the cancer control continuum to 

increase the impact of research on cancer mortality in the 

United States. One such tool is a cancer survival query system that could help researchers and other audiences obtain 

easier access to surveillance survival data.
7

The National Cancer Institute’s charge: 

“... collect, analyze, and disseminate all data useful in the prevention, diagnosis, and treatment of cancer ...” 

(The National Cancer Act of 1971 [P.L. 92-218]).

Cancer surveillance provides a portrait of cancer over time 

across a population. It offers a means to measure progress 

made against a disease that is estimated will affect one in 

every two people in the United States during their lifetime 

(http://seer.cancer.gov/statfacts/html/all.html#risk). Decisions 

in science and public health depend on reliable information about the impact of efforts to control cancer. Cancer 

surveillance research describes how well cancer has been 

controlled through the identification of risk factors and 

their prevention, disease detection and diagnosis, delivery 

of interventions to populations, and the effectiveness of 

those interventions. 

Monitoring trends in the national cancer burden is important to determine what research is needed and to identify 

the best ways to build knowledge and translate results of 

cancer etiology and molecular science. Tracking cancer rates 

and trends also helps policymakers make sound decisions 

that promote public health and reduce the burden of cancer, and informs the U.S. public about the impact of cancer 

across the country. 

Reductions in cancer incidence and mortality during the 

past 40 years have been realized through the amalgamated 

effect of a multifaceted campaign: programs to educate the 

public about adopting healthy lifestyles and the importance 

of screening for tumors; significant improvements in the 

treatment of cancers; and advances in biological, molecular, 

and genetic sciences that have provided the foundation for 

the development of effective agents and other therapies. 

Gene-environment interactions, genome mapping, proteomics, imaging, and bioinformatics are but a few fields 

that have burgeoned in the past 10 years to spur new discoveries, novel interventions, and sophisticated technologies, all 

in pursuit of helping cancer patients attain healthy status 

and preventing cancer. Cancer surveillance research has 

supported these advances by capturing data and analyzing 

rates for cancer incidence, prevalence, mortality, and survivorship in the United States. Researchers, physicians, and 

policymakers alike rely on this information to refine their 

investigations, treatment of patients, and health policies.

Chapter 1

Introductio
Chapter

8

Cancer surveillance data show overall reduction or increases in cancer incidence and mortality in the United States. 

Societal effects—such as the effect of tobacco smoking 

bans in public locations on lung cancer incidence or the 

reduction of hormone replacement therapy and ensuing decline in breast cancer—can be tracked in tandem with peaks 

and valleys in incidence and mortality rates. Effects due to 

diagnosis of celebrities’ cancers followed by a dramatic rise 

in screening also are documented, as seen in the case of 

President Ronald Reagan and prostate cancer. The Annual 

Report to the Nation provides cancer trends in the United 

States. For example, the 2009 report described the decrease 

for mortality rates overall—and specifically for male lung, 

male bronchus, cervical, and colorectal cancers—between 

1975 and 2006 for all races/ethnicities monitored. 

The Surveillance Research Program

The National Cancer Institute’s (NCI) Surveillance Research 

Program (SRP) is one of the most authoritative sources of 

information on cancer incidence, prevalence, mortality, and 

survival in the United States. Housed within NCI’s Division 

of Cancer Control and Population Sciences (DCCPS), SRP 

characterizes the cancer burden borne in the United States 

over time by integrating medical record data on persons diagnosed with cancer with a range of other relevant data from 

organizations such as the Social Security Administration, 

National Center for Health Statistics (NCHS), Centers 

for Medicare and Medicaid Services (CMS), and the U.S. 

Census Bureau. Its best-known project is the Surveillance, 

Epidemiology, and End Results (SEER) Program, which 

collects and manages high-quality data that provide a means 

to measure the progress made in reducing the Nation’s cancer burden. These data are used by researchers in the areas 

of cancer prevention, treatment, and control. Through its 

statistical and modeling activities, particularly the Cancer 

Intervention and Surveillance Modeling Network (CISNET), 

SRP provides information and feedback about trends over 

time and location across the cancer control continuum. 

Other statistical methodologies and tools developed by SRP 

staff to analyze trends and evaluate the impact of cancer 

control interventions and other factors on the cancer burden include SEER*Stat and Joinpoint. SRP’s data provide a 

snapshot that illustrates to the public how the war on cancer 

is faring, particularly with regard to health disparities and the 

effect of cancer on minority populations. Appropriate decisions in science and public health depend on reliable data 

about the impact of efforts to control cancer. 

To accomplish its mission, SRP is organized into four 

branches that address surveillance systems, data analysis and 

interpretation, data modeling, and statistical methodology 

research (Figure 1). Each branch enables the growth of surveillance research through the management of grant portfolios and development of research infrastructure through 

cooperative agreements. Since 1999, SRP’s portfolio has 

doubled in size. SRP also responds to more than 2,000 requests from investigators for data each year.

SRP collaborates with other extramural programs within 

DCCPS and other NCI Divisions to collect and analyze population-based data across the cancer continuum. Synergistic 

Cancer Surveillance and R

Surveillance of disease trends is an important

component of public health research.

Surveillance research about these trends, including prevention, treatment, and outcome

of disease, offers a means to interpret and

understand surveillance trends. It provides an

avenue to create and validate new ways of conducting surveillance.

NCI has built a premier cancer surveillance

research enterprise since the passage of the

National Cancer Act of 1971, nearly 40 years

ago. Based on a population science approach,

NCI’s surveillance research programs and activities serve as the most authoritative sources of

information about cancer incidence, prevalence,

mortality, and survival in the United States.
9

collaborations occur particularly between DCCPS’ Applied 

Research Program (ARP) and Behavioral Research Program 

(BRP), which focus on risk factors, health behaviors, health 

care services, economics, and outcomes, including patientreported outcomes (ARP); and behavioral interventions, 

such as tobacco use, screening, dietary behavior, and sun 

protection (BRP). 

SRP has developed close partnerships with many cancer surveillance organizations, including other federal agencies, and 

private sector and academic institutions, in all aspects of cancer data and statistics. In turn, partners and researchers alike 

depend on SRP for SEER’s high-quality data as well as for assistance in analyzing data and interpreting cancer statistics. As 

a comprehensive, population-based reporting system, SEER 

routinely collects cancer data from designated populationbased cancer registries in various areas of the country (see 

Figure 2). Areas not covered directly by SEER are covered 

by the Centers for Disease Control and Prevention’s (CDC) 

National Program of Cancer Registries (NPCR). NPCR and 

SEER data are integrated and presented in summary form 

in periodic reports produced by the CDC and International 

Association of Cancer Registries (IACR). In addition, these 

data are presented in the Annual Report to the Nation, produced through collaboration between NCI, the American 

Cancer Society (ACS), and the North American Association 

of Central Cancer Registries, Inc. (NAACCR). Investigators 

in the United States as well as worldwide rely on the SEER’s 

high-quality cancer data and statistics. 

Cancer Surveillance Program Straticlan 

Figure 1. SRP Organizatio

 SRP Mission

SRP directs the collection and analysis of pertinent data in order to answer key

questions about cancer incidence, morbidity, mortality, and cancer-related health

status in diverse regions and populations in the United States.

Surveillance

Systems Branch

Data Analysis and

Interpretation Branch Data Modeling Branch Statistical Methodology andApplications Branch

Surveillance Research Program

Office of the Associate Director

SRP’s Major Part

Federal Agencies 

• CDC’s National Program of Cancer Registries (NPCR)

• CDC’s National Center for Health Statistics (NCHS)

• Centers for Medicare and Medicaid Services (CMS)

• U.S. Census Bureau

Professional and Private Organizations 

• North American Association of Central Canc

Registries, Inc. (NAACCR)

• National Cancer Registrars Association (NCRA)

• American College of Surgeons (ACoS)

• Commission on Cancer (COC

• American Cancer Society (ACS)

• National Coordinating Council for Canc

Surveillance (NCCCS)

• International Association of Cancer Registries (IACR)
10

Chapter

Development of the Vision for the 

NCI Surveillance Research Program

Cancer Surveillance Research Implementation 

Plan of 1999

In 1999, NCI’s Cancer Surveillance Research Program 

(CSRP) prepared NCI’s Cancer Surveillance Research 

Implementation Plan of 1999 (hereafter the 1999 Strategic 

Plan). The Surveillance Implementation Group (SIG) led 

the planning process, identified research directions and priorities, and produced an implementation plan for a comprehensive, focused, and coherent vision for NCI-funded 

surveillance research. Composed of 42 leading scientists 

and experts representing NCI, other federal agencies, and 

the extramural community, the SIG identified research opportunities within five priority areas (Table 2) to expand 

and enhance cancer surveillance research supporting the 

control of cancer.

Based on the Plan’s recommendations, additional data on 

risk factors, screening, quality of life, patterns of care, 

treatment effectiveness, and biologic samples have been 

collected, and the Annual Report to the Nation has been published in collaboration with the CDC, ACS, and NAACCR, 

providing a narrative and visual overview of the state of 

cancer trends each year.

2005 National Framework for Cancer Surveillance

NCI’s 2005 National Framework for Cancer Surveillance provided additional direction to the cancer surveillance field, 

describing the cancer burden from diagnosis to dying with 

cancer at the national, state, and local levels.

SEER Visioning Process 

In April 2008, NCI staff from SRP and ARP met with 

staff from 15 SEER registries to discuss a vision for SEER, 

including new directions and strategies. Participants concluded that the SEER mission should continue to focus 

on the collection of high-quality, timely, population-based 

data that describe the cancer burden—specifically, cancer 

incidence, outcomes, trends, and patterns of care. New directions for SEER included an expansion of data collection 

to cover the entire cancer continuum (prevention through 

survivorship, including quality of life), and dissemination of 

those data to a wider range of stakeholders in the area of 

cancer control and prevention. Visioning participants also 

emphasized the need for SEER to support the collection 

and analysis of cancer data over time and location, and to 

support infrastructure for surveillance research. 

The SEER Visioning effort generated four priority areas:

•	 Timeliness, quality, and relevance of SEER data.

•	 Efficiency and flexibility of SEER registry processes.

•	 Improvement of the visibility of SEER activities and 

services. Visioning participants agreed that SEER 

needed to better define its unique contribution to the 

field of population surveillance.

•	 Expansion of SEER data sources and users.

Four work groups were formed to tackle these 

priority areas:

•	 The Core Data Timeliness Work Group, charged with 

(1) examining SEER data to determine elements that 

might be eliminated, and (2) identifying strategies for 

making SEER data available to the public sooner.

•	 The Registry Operations Work Group, charged with 

examining the operations of each registry to determine 

options for reducing complexity in data processing as 

well as unnecessary activities. 

•	 The Data Use and Marketing Work Group, charged 

with examining ways to improve SEER’s relevance and 

visibility, with the long-term goal of maximizing data 

utility/value to the community and other stakeholders.

•	 The Health Informatics Work Group, charged with 

(1) assessing the state of automation across registries, 

and (2) assessing ways to use automation to streamline 

processes and reduce operating costs.
11

Table 2. Cancer Surveillance Research Implementation Plan March 1999–2009

Cancer Surveillance Program Straticlan 

Research Opportunities Implemntation:  Acc

Priority Area1 Expand the scope of surveillance research through additional data collection and methods devel

1. Collect data on patterns of care, quality of lif

morbidity in cohorts of registered cancer patients.

• SEER Patterns of Care Studies year

• HEAL (breast cancer coho

• Rapid Response Surveillance Studi

2. Collect data on risk factors and screen

defined populations.

• Rapid Response Surveillance Studi

• National Health Interview Surv

• California Health Interview Surv

• Breast Cancer Surveillance Consorti

3. Develop research methods to improve measurement of cancer burden t

• CISNET (breast, prostate, colorectal, lu

• SEER

• NLMS linkag

• Combining survey for small area estimati

• Prevalence measur

4.    Explore geographic information

(GIS) methods.

• State Cancer Profiles (P.L.A.N.E.

• Geospatial and data visualization metho

Priority Area 2 Expand the scope of surveillance to improve the representativeness of cancer burden e

5.    Expand to improve representation of ethnic minority and underserved popul

• 2000 SEER expansions increase coverage to

• Publications: Hispanics, AI/AN, API; surv

• Link to Indian Health Servi

6.    Explore methods for improved national es

of cancer burden.

•  Estimation of # new cancer cases in U.S. this year; projections; delay adjustment; Joinpoint; cure models; prevalence by phase of c

• HD*Ca

7. Work with partners on National 

Surveillance Plan.

• National Coordinating Council for Cancer Surveillance (framework

NAACCR; CDC; ACS; C Change

Priority Area 3 Produce and disseminate a national report card on the cance

8. Proceed with production of Report Ca

Year 2000

• Annual Report to the Nation (1998–200

• Cancer ProgressTrends Repo

9. Develop plan to improve disseminat

surveillance data.

• Web-based, interactive SEER rep

• Cancer Fact Sheets updated annual

• 2005 Workshop for Advocat

• 2000+ requests for SEER research 

Priority Area 4 Support molecular and genetics research for surv

10. Develop tools to assess family history and prevalence of familial cancers. • Survey on family h

11.   Explore the feasibility of collecting biospe

to support population-based molecular and

genetic research.

• ResidualTissue Reposito

Priority Area 5 Develop a training strategy for cancer surveillance 

12. Identify training needs and develop a pl

incorporate surveillance sciences into training

mechanisms.

• Surveillance Institute 20

• 1-week summer course (Johns Hopkins Universit

• Workshops at NAAC

• SEER*Stat trai

* This list includes a number of initiatives that are managed by Programs across DCCPS, notably ARP.
12

Chapter

Each of these groups developed several recommendations 

for SEER within the assigned focus areas. Specific activities 

of the work groups to date include:

•	 Development of a process diagram for evaluating 

data items.

•	 Analysis of SEER cost study data to determine common and best practices across registries.

•	 Working with a communications specialist to assess 

the brand awareness of SEER.

•	 Conducting an online survey of registry principal 

investigators and managers to determine levels and 

types of automation at the 18 SEER registries.

2010 Strategic Planning Process for 

NCI-Supported Cancer Surveillance Research

On October 22–23, 2009, SRP held a Cancer Surveillance 

Research Workshop to examine potential directions for surveillance research during the next 3 to 7 years, building on 

achievements from the past decade and from the strategic 

planning efforts outlined in the 1999 Strategic Plan. 

The workshop covered five topic areas: (1) scope and content of cancer surveillance; (2) development of analytic 

methods and models to support surveillance research; (3) 

integrated health informatics; (4) population subgroups to 

understand differences in risk and prognosis; and (5) communications and dissemination of data to users. NCI staff 

and guest speakers presented on each topic, and participants 

broke into small groups to develop, discuss, and prioritize 

recommendations. Discussions covered the upsurge in 

information available and its growing complexity, as well 

as challenges presented in an era of collaborative efforts, 

pooled data, and shared resources, with greater uncertainties related to funding and staffing. 

Specific considerations during the workshop included: data 

items for specific purposes; building and analyzing multilevel data systems; analysis and interpretation of integrated 

information; development and application of analysis tools 

for new data submissions and incomplete datasets; and application of advanced statistical methods, including classical 

and Bayesian. 

Based on discussions from the October 2009 workshop 

and from ongoing conversations between NCI staff and 

SEER registries that commenced with the SEER Visioning 

meeting in 2008, as well as dialogues with other principal 

investigators and colleagues, priority areas and research 

opportunities have been identified as future directions for 

NCI-supported cancer surveillance research. These are described in detail in Chapter 3. 
13

Chapter 2 

NCI’s Surveillance Re

Program: Where Are We N

Nearly 40 years after the passage of the National Cancer 

Act of 1971, SRP serves as a leader in the Nation’s robust cancer surveillance enterprise. At the heart of its efforts, SRP measures the impact made on the cancer burden through shifts in cancer incidence, morbidity, survival, 

and mortality, particularly by evaluating data about genetic 

predisposition, environmental and behavioral risk factors, 

screening practices, and the patterns and quality of care 

provided throughout the cancer disease process, from prevention through outcome. 

SRP has evolved over the years as the demand for new and 

expanded data and more sophisticated analyses increases, 

refinements to the collaborative staging of cancers result 

in additional data elements to be collected, advances are 

made in cancer biology and etiology, and populations desire 

more information about the public health impact of science 

research. SRP’s approach is to anticipate research needs and 

cancer trends, integrate data into the SEER dataset collection, and remain both focused and flexible in terms of 

resources, infrastructures, and priorities. Health disparities 

figure largely in monitoring the cancer burden as cancers 

differ among racial/ethnic groups, and population diversity 

is increasing in the United States. 

Uses of

SEER is used in many research studies to understand disease trends and as a source for etiologic cancer research. It serves as a major pill

for many research infrastructures (e.g., SEERMedicare) that enable a wide range of resear

studies on cancer-related topics and SRP statistical methods research techniques used for many

other diseases. SEER provides a gold-standa

cancer surveillance methodology for the United

States and the world.

Researchers rely on SEER to:

	 •Track population trends.

	 • Understand factors influencing trend

	 • Analyze cancer incidence, prevalence, surviva

and mortality at the state/local level.

	 • Understand factors related to cancer incidence, prevalence, survival, and mortalit

	 • Describe health disparities amo

subpopulations.

	 • Validate cancer statistics from other source

	 • Identify cancer patients for approved studie

	 • Compile cancer statistics for policy decisi

making.
14

Figure 2. SEER Coverage Across the United States

Cherokee Nation

Seattle - Puget Sound

Alaska Natives

San Jose - Monterey

Los Angeles

Greater California

San Francisco - Oakland

CA

UT

AZ NM

AK

HI

LA

IA

KY

GA

OK

WA

MI

NJ

CT

California

Washington

Iowa

Hawaii

Michigan

Louisiana

Connecticut

New Jersey

New Mexico

Arizona

Alaska

Utah

Oklahoma

Georgia

Kentucky

Arizona Indians

Atlanta 

Rural Georgia

Detroit

Cherokee Nation

Seattle - Puget Sound

Alaska Natives

San Jose - Monterey

Los Angeles

Greater California

San Francisco - Oakland

CA

UT

AZ NM

AK

HI

LA

IA

KY

GA

OK

WA

MI

NJ

CT

California

Washington

Iowa

Hawaii

Michigan

Louisiana

Connecticut

New Jersey

New Mexico

Arizona

Alaska

Utah

Oklahoma

Georgia

Kentucky

Arizona Indians

Atlanta 

Rural Georgia

Detroit

Chapter

SRP’s cancer surveillance research programs and activities 

are best described in three broad activity areas: (1) collecting and managing data; (2) developing statistical methods 

and modeling; and (3) reporting and sharing data.

Collection and Management of Data

The SEER Program (http://seer.cancer.gov) serves as the primary vehicle for compiling data about incidence, prevalence, mortality, and survival, which researchers who are 

interested in cancer control, etiology, molecular genetics, 

health services, rates/trends, and other areas can use. SEER 

supports multiple population-based cancer registries, encompassing 26 percent of the U.S. population. During the 

past decade, SEER significantly expanded its data collection activities through its registries and through partnerships with the CDC and other data-collection efforts to 

capture incidence data across the United States (Figure 2). 

NAACCR data from 2002 to 2006 encompass 85 percent 

of the Nation; the U.S. Cancer Statistics (USCS) data in 

2005 cover 96 percent of the United States.

Links with other datasets have enhanced cancer care and 

outcomes information. These include: SEER-Medicare 

data linkage (http://healthservices.cancer.gov/seermedicare); 

SEER-Medical Health Outcomes Study (MHOS) (http://

outcomes.cancer.gov/surveys/seer3-mhos); and SEER-Consumer 

Assessment in Healthcare Providers and Systems (CAHPS), 

a database designed to improve understanding of how 

Medicare beneficiaries with cancer perceive the quality of 

their health care. Additional partnerships that have been 

pivotal in SEER’s data collection efforts and in advancing 

cancer surveillance research include the SEER-National 

Longitudinal Mortality Study (NLMS) database and 

Residual Tissue Repository (RTR) Program.
15

Cancer Surveillance Program Straticlan 

Specific programs and activities that facilitate 

data collection and management include:

SEER-Medicare. A linkage between the SEER database 

and Medicare claims database provides a large, populationbased source of information for cancer-related epidemiologic and health services research, particularly relevant for cancer 

care and outcomes (http://healthservices.cancer.gov/seermedicare). 

The SEER-Medicare data include information about all patients reported to the SEER registries who are Medicare 

eligible. The data have expanded from a focus on assessing 

the costs of care in 1992 to now including large numbers of 

cases, spanning from initial Medicare coverage until death, 

encompassing most clinical areas, representing a diversity 

of geographic areas, being population-based, and including 

data on multiple disease conditions. To ensure that patient/

provider confidentiality is safeguarded, SEER-Medicare data 

are de-identified, and investigators are required to obtain approval before accessing or acquiring data. 

NLMS Database and SEER-NLMS Linked Dataset. 

Data from SEER (1973–2003) have been linked with data 

from the NLMS dataset (1979–2002) to provide additional 

individual-level socioeconomic and demographic information on the SEER cancer cases, including cancer incidence, 

survival, and tumor characteristics based on self-reported factors (http://surveillance.cancer.gov/disparities/nlms/). The NLMS 

database includes records on approximately 3 million people 

as well as cause of death on more than 250,000 in the study 

population. Individual-level socioeconomic data collected by 

the U.S. Census Bureau through annual Current Population 

Surveys (CPS) are combined with cause of death information 

from official state death certificates for the period 1979–2002. 

The NLMS database includes information about: race/ethnicity, income, industry, nativity/immigrant status, marital 

status, employment status, veteran status, smoking, education, occupation, household size, and health status. Analyses 

of the data can be used to investigate mortality differentials 

across various socioeconomic and demographic groups. 

RTR Program. The SEER RTR Program (http://

seer.cancer.gov/biospecimen), established in 2003, maintains 

biospecimens obtained from three of SEER’s populationbased cancer registries (Iowa, Hawaii, and Los Angeles). 

RTR covers more than 60,000 cancer cases from diverse 

geographic regions (urban/rural) and racial/ethnic groups. 

It enables studies on rare cancers for which no single registry has enough cases to allow statistically valid conclusions to be drawn, allows validation studies on specimens 

from population-based registries, and allows comparisons 

of cases with biospecimens from all cases in the registry for 

the purpose of assessing population-based representativeness. RTR also allows analysis of trends in incidence and 

the potential for correlation with treatment trends based on 

the history and diversity of the SEER registries. Updates to 

survival data are possible after a tissue microarray is formed 

without violating confidentiality and privacy protections. 

Rapid Response Surveillance Studies (RRSS). This 

vehicle allows studies to move expeditiously from the initial 

concept through completion in approximately 2 years. SRP 

has used RRSS to address various cancer surveillance issues, 

beginning with the SEER Patterns of Care Study in 1987 

and extending to a broad range of topics related to cancer 

prevention and control (http://seer.cancer.gov/rapidresponse). 

Research areas addressed by these studies include evaluation 

of cancer surveillance methodologies and cancer treatment 

outcomes, and monitoring of screening practices linked to 

cancer outcomes. In addition, these studies have supported 

pilot and feasibility studies that address the development of 

procedures for enhanced monitoring of health behaviors 

and risk factors, expanded utility of SEER data through 

linkage with other databases, and improved cancer registry 

operations. RRSS studies have provided a foundation for 

larger research initiatives funded by other federal and private mechanisms. 

SEER*DMS. The SEER Data Management System (http://

seer.cancer.gov/seerdms/) is a hardware/software application 

that provides support for all core cancer registry functions. 

Centralized development has proved cost effective and resulted in improved data quality, quicker data reporting, and 

expansion of collected fields. Refinements are distributed 
16

Chapter

simultaneously to all registry locations, and the program 

can be customized at the local level, such as site-specific 

data fields and choice of matching algorithms. SEER*DMS 

reduces duplication of effort and ensures data quality and 

consistency among registries by facilitating the collection 

and reporting of high-quality cancer incidence, treatment, 

and survival data, and by enabling data sharing among users. 

E-Path. Electronic Pathology (E-Path) software (http://

www.aim.on.ca/products/ePath.jsp) provides a means to collect complete and accurate patient data from hospital and 

other pathology laboratories that report to SEER’s cancer 

registries. E-Path reports cancers that are diagnosed through 

histology and comprise approximately 95 percent of all cancers. E-Path helps reduce errors and provides cost and labor 

savings by standardizing and automating the registries’ work 

in identifying new cases and determining key characteristics 

required for classification standards of a cancer. NCI began 

installing E-Path within SEER registries in 2004. Currently, 

the software has been implemented in more than 200 reporting hospitals and laboratories throughout the United States. 

Developing Statistical 

Methods and Modeling 

Research methods have been developed and modeling tools 

designed and refined to improve national estimates and 

measurement of cancer burden trends. Cancer statistical 

data have been incorporated into models to help synthesize, 

interpret, and assess current and potential trends in cancer 

rates. SRP’s efforts during the past decade encompass:

•	 Developing methods to analyze population registration data.

•	 Modeling and interpreting trends.

•	 Promoting biostatistical cancer research.

Methods To Analyze Population Registration Data

SRP has developed a number of statistical tools for population surveillance researchers to use in analyzing cancer 

data. These methods help scientists generate hypotheses 

that form the basis for cancer research and interventions for 

cancer prevention and control. The results of these analyses also are used to re-inform and redirect earlier stages of 

cancer research.

Many of the methods developed are included in SEER*Stat, 

a standard software program used for analyzing registry 

data, and other publically available software programs that 

work in conjunction with SEER*Stat, which has approximately 5,000 users nationally and internationally. Other statistical applications software programs have been designed 

to interface with SEER*Stat for ease of use with registry 

data. Figure 4 highlights the major areas of methods developed to analyze population registry data and how software 

developed to apply these methods work together. Three 

examples that play a critical role in reporting annual statistics to the Nation are outlined below.

SEER*Stat. SEER*Stat (http://seer.cancer.gov/seerstat/) is a 

PC-based statistical software that provides a convenient, 

intuitive mechanism for the analysis of SEER and other 

cancer-related databases. It is a powerful tool for viewing 

individual cancer records and for producing statistics to 

study the impact of cancer on a population. SEER*Stat 

software is distributed with SEER Limited-Use Data and 

access to the data is necessary before using the software. 

Joinpoint. Joinpoint (http://srab.cancer.gov/joinpoint/) is a 

statistical software program that analyzes trends using joinpoint models, that is, models where several different lines 

are connected together at the “joinpoints.” Cancer trends 

reported in NCI publications are calculated using the 

Joinpoint Regression Program to analyze rates calculated 

by SEER*Stat software. The software takes trend data (e.g., 

cancer rates) and fits the simplest joinpoint model that the 

data allow. Figure 3 provides an example of cancer incidence 

and mortality analyses resulting from Joinpoint software.

DevCan. DevCan (http://srab.cancer.gov/devcan/) is a statistical modeling software program that computes the probability 

of developing and dying of cancer from birth or conditional 
17

Cancer Surveillance Program Straticlan 

Figure 3. Sample Joinpoint Analyses

600

White

Black

APIb

AI/ANc

Hispanicd

Rate Per 100,000 Rate Per 100,000

Incidence Mortality

Year of Diagnosis Year of Death 

500

400

300

200

100

1975 1980 1985 1990 1995 2000 2006 1975 1980 1985 1990 1995 2000 2006

0

600

500

400

300

200

100

0

SEER Incidence and US Death Rate

All Cancer Sites, Both Sexes

Joinpoint Analyses for Whites and Blacks from 1975 -2006

Source: Incidence data for whites and blacks are from the SEER 9 areas (San Francisco, Connecticut, Detroit, Hawaii, Iowa, New Mexico, Seattl

Utah, Atlanta).

Incidence data for Asian/Pacific Islanders, American Indians/Alaskan Natives and Hispanics are from the SEER 13 Areas (SEER 9 Areas. San JoseMonterey, Los Angeles, Alaska Native Registry and Rural Georgia). Mortality data are from US Mortality Files. National Center for Health Statistics, 

a

Rates are age-adjusted to the 2000 US Std Population (19 age groups – Census P25-110

Regression lines are calculated using the Joinpoint Regression Program Version 3.3.1. April 2008, National Cancer Institute. Joinpoint analyses for

Whites and Blacks during the 1975-2006 period allow a maximum of 4 joinpoints. Analyses for other ethnic groups during the period 1992-2006 allow

a maximum of 2 joinpoints.

bAPI = Asian/Pacific Islander.

c AI/AN = American Indian/Alaskan Native. Rates for American Indian/Alaskan Native are based on the CHSDA (Contract Health Service Delive

Area) counties.

d Hispanic is not mutually exclusive from whites, blacks, Asian/Pacific Islanders, and American Indians/Alaska Natives. Incidence data for Hispanics

are based on NIHA and Exclude cases from the Alaska Native Registry. Mortality data for Hispanics exclude cases from Connecticut, the District 

Columbia, Main, Maryland, Minnesota, New Hampshire, New York, North Dakota, Oklahoma, and Verm

on a certain age. DevCan takes cross-sectional counts of incident cases from the standard areas of SEER plus mortality 

counts for the same areas from data collected by the NCHS 

and uses them to calculate the probabilities of developing or 

dying from cancer for a hypothetical population.

Because the SEER program is the authoritative source of 

information on cancer survival for the U.S. population, SRP 

has focused on developing methods to describe survival 

and estimate prevalence of cancer survivors. Boxes 2.1 and 

2.2 highlight accomplishments to date in these areas of 

research.
18

Incidence

Mortality

Prevalence

Survival

Spatial

Delay Model

SEER*Stat

- Age-adjusted Rates

 - Confidence Intervals for Age-Adj. Rates

 - Trends in Rates

 - Risk-adjusted Incidence Rates

 - Multiple Primaries (MP-SIR)

 - Incidence-based Mortality

SEER*Stat

 - Limited-Duration Prevalence

SEER*Stat

- Observed Survival 

 - Net Survival 

 - Crude Survival 

 - Period Method

Head-Bang

Space-Time Analysis Tools

 - Under Development

SaTScan - Clusters

JoinPoint

- Trend Analysis

DevCan

- Lifetime Risk

ComPrev

- Complete Prevalence 

ProjPrev

- Projected Prevalence

CanSurv

- Survival Models

Software Uses

Output From

SEER*Stat

Statistical Methods and Software for Population-Based Cancer Statistics

Figure 4. NCI Statistical Methods and Software for Cancer Surveillance Research

Chapter
19

Cancer Survival Stat

Cancer survival statistics are concerned with the proportion of patients alive at some point subsequent to the

diagnosis of their cancer. Relative survival is an estimate of the percentage of patients who would be expected

to survive the effects of their cancer. Observed survival is the actual percentage of patients still alive at so

specified time after diagnosis of cancer. A number of tools and metrics are available to estimate populationbased cancer survival, depending on the purpose for using the data.

SEER*Stat Statistical Methods

• There are three measures of cancer survival that can be calculated in SEER*Stat software, includ

	 •  Observed all cause survival—survival using as end point all causes of dea

	 • Net cancer-specific survival—cancer survival in the absence of other causes of death (t

confounding effects of death from other causes are removed).

	 • Crude probability of death—probability of death from cancer and the probability of death fr

other causes, each estimated in the presence of the other.

Each of these measures can be calculated using relative survival by using the cause of death information liste

on the death certificate. An ongoing area of research has been to investigate how to best use the information

on the cause of death to minimize biases associated with misclassification or missing information.

Cancer Survival Analysis Software (Cansurv)

Cansurv is statistical software to analyze population-based survival data using mixture cure models and provide

graphs and tests for inference and diagnosis. It can fit parametric (cure) survival models to grouped data or individually-listed data. Cansurv uses population-based survival data extracted from a SEER*Stat survival sessio

Competing Risk Modeling

SRP is working with a variety of competing risk models to develop approaches to model the probability of dying

from cancer and other causes that includes information on the cancer diagnosis and existing comorbidities at

the time of diagnosis.These estimates are being developed as part of a Cancer Survival Query System (CSQS),

which will provide up-to-date estimates of survival as a function of factors influencing both cancer and other

causes of mortality for recently diagnosed patients.

Providing Up-To-Date Estimates of Survival

Survival estimates from cancer registry data usually are dated measures of current-year survival because of

the time needed to observe survival and the time lapse between available data and the current year. Four approaches are used for grouping survival experience: (1) cohort, which uses the observed survival for a coho

of patients diagnosed in a single calendar year; (2) multiple-year cohort, which includes all patients diagnosed

in the most recent years spanning the maximum duration to be estimated; (3) period, which uses only the

most recent interval survival estimate of cases diagnosed in different calendar years; and (4) projection, which

involves projecting survival trends to the current calendar year.

Box 2.1. 

Cancer Surveillance Program Straticlan 
Cancer Prevale

Cancer prevalence is the measure of the total number of

people alive in a population ever diagnosed with cancer

at a given time (referred to as complete prevalence).

Uses

Prevalence is used by government agencies, local health

administrators, the research community, and cancer

advocacy groups to:

	 • Describe the health of population

	 •  Establish public health goa

	 • Measure the burden of the diseas

	 • Understand the cancer survivor populatio

	 •  Evaluate and plan health resources allocatio

	 • Describe how rare a disease must be in order to 

used for the orphan drug development process.

	 •  Estimate costs of cancer ca

Methods of Estimation

	 • SEER*Stat software includes prevalence methods that estimate limited duration prevalence from ca

registry data. Limited duration prevalence represents the prevalence of people diagnosed with the disease for the maximum number of years that the registry has been collecting incidence data.

	 • COMPREV and PROJPREV are other software that allow researchers to estimate complete prev

and the prevalence of people ever diagnosed, and to extrapolate prevalence to different populations.

Research

Prevalence across the cancer continuum is more useful; however, it is difficult to estimate because

SEER collects information at the time of diagnosis and life status at the end of follow-u

	 • Research is being conducted to estimate:

	 	 - Survivors who will never die from t

cancer (Cure prevalence).

	 	 - Survivors who have recurrent canc

(Recurrence prevalence).

	 	 - Survivors by phase of care: initial,

year of life, and phase in between.

	 	 - Projections of cancer prevalence a

costs of cancer care by different scenario assumptions of incidence and

survival (http://costprojections.cancer.gov/).

	 	 - Methods to estimate complete prevalence from cancer registries with

years of incidence data.

20

Chapter

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19711973197519771979198119831985198719891991199319951997199920012003200520072009

2011

2013201520172019

Number in millions

Year

Estimated and Projected Number Projected

Cancer Survivors in the United States 

from 1971 to 2020 (excluding in situ)

Data sources: NCI SEER November 2006 Submission, U.S. Estimated Prevalence counts were estimated by applying U.S. populations to SEER-9 and 

historical Connecticut Limited Duration Prevalence proportions. Populations from January 2004 were based on the average of the July 2003 and July 

2004 population estimates from the U.S. Bureau of Census.

Box 2.2. 
21

Cancer Surveillance Program Straticlan 

Modeling and Interpreting Trends

Modeling has played an important role in SRP’s efforts to 

analyze data and interpret cancer trends. Mathematical and 

simulation modeling increase the usefulness of data for assessing progress in cancer control. SRP’s primary modeling 

efforts to better understand and project patterns in cancer 

data have been made through the CISNET program. 

CISNET. CISNET (http://cisnet.cancer.gov/) serves as SRP’s 

principal vehicle for the statistical modeling of cancer control interventions to better understand interventions and 

population trends, particularly the impact of prevention, 

screening, and treatment on cancer incidence and mortality. CISNET’s models have been used since 2000 to guide 

public health research and priorities by projecting future 

trends and determining optimal cancer control strategies for 

breast, prostate, colorectal, and lung cancers. CISNET was 

re-funded in 2010 and now also includes esophageal cancer. 

Risk factors, screening behaviors, and the diffusion of new 

treatments are analyzed to simulate potential effects and 

consider the costs versus benefits of interventions. 

CISNET uses simulation modeling of disease processes 

and the impact of interventions on these processes to bring 

the most sophisticated evidence-based planning tools to 

the areas of population health and public policy. CISNET 

models can translate evidence from randomized trials and 

epidemiologic studies to the population health setting by 

extrapolating evidence beyond study protocols to the general population, accounting for actual usage in less controlled 

settings. Modeling real and hypothetical scenarios allows for 

the identification of key factors influencing outcomes and 

efficient cancer control strategies. The consortium’s work 

informs clinical practice and recommended guidelines by 

synthesizing existing, albeit often incomplete, information 

to model gaps in available knowledge.

CISNET provides a suite of models to meet the challenges 

of the increasing pace of scientific discovery that are poised 

to address emerging questions. Collaborative work on key 

questions promotes efficient collecting and sharing of the 

most important data resources and critical evaluation of the 

strengths and weaknesses of each data source. A systematic 

comparative modeling approach brings transparency to the 

modeling process. Providing a range of models, rather than 

a single estimate from one model, brings credibility to the 

process and reassures the users of the models’ results that the 

results are reproducible. CISNET investigators collaborate 

with groups such as the United States Preventive Services 

Task Force (USPSTF), CMS, CDC, ACS, and the American 

College of Radiology Imaging Network (ACRIN).

The Surveillance Research Program’s 

Biostatistical Methodology Portfolio

SRP has a biostatistical grant portfolio currently consisting 

of 83 active grants (Figure 5). Most of these grants are research program grants (47 R01s), but also represented are 

cooperative agreements (15 U01s), small grants (10 R03s), 

exploratory grants (2 R21s), MERIT awards (2 R37s), conference grants (5 R13s), and Pathway to Independence 

Awards (1 K99/R00); AREA grants (R15s) previously were 

represented. Most of the Principal Investigators (PIs) are 

senior statisticians in statistics or biostatistics departments. 

Many biostatistical methodology grants at NIH are cancer 

related, and the majority of these are administered in SRP. 

Some of these grants develop new statistical methodologies arising from collaborations with clinical or laboratory 

scientists, whereas others are translational. Examples of 

statistical methodology work in the SRP portfolio are:

•	 Development of methodologies for missing data in 

longitudinal models.

•	 Functional mixed models using wavelets to model images.

•	 Use of biomarkers as surrogate endpoints.

•	 Missing data in survival models.
22

SRP supports translational research, which aims to make 

the best use of already developed methodologies to benefit 

areas of health research other than its original purpose. 

One way that SRP supports translational research is by encouraging statisticians who are developing methodologies 

to create user-friendly software as well. Development of 

user-friendly software is supported through administrative supplements and Small Business Innovation Research 

(SBIR) contracts.

SRP is a strong supporter of the statistical community in 

ways other than administering statistical grants. SRP program directors regularly attend statistics meetings (such as 

the Joint Statistical Meetings and the spring meeting of 

the Eastern North American Region of the International 

Biometric Society), providing public service by educating 

new researchers about the NIH grant-funding processes. 

This is accomplished through presentations, organizing 

sessions, leading roundtables, and hosting exhibit booths. 

In addition, SRP communicates information about funding through overview articles published in bulletins such 

as Amstat News and through its Web site, www.statfund.cancer.

gov, which contains funding information particularly useful 

for statisticians. StatFund provides notices about funding 

opportunities, NIH policies, and application procedures, as 

well as listing statistical grants that are administered by SRP.

0

5

10

15

20

25

Concer control modeling* Number of grants

Complex data

Bayesian analysis

Survival analysis

spatial/GIS

Genomics/Genetics

General statistics

Conferences 

Clinical trials

Medical diagnostics

Survey

Figure 5. SRP Grant PortfolioH 

Chapter

* includes CISNET Cooperative Agreements

H Data are from March 2010
23

Cancer Surveillance Program Straticlan 

StatFund. Information about biostatistical funding opportunities is available at http://statfund.cancer.gov. StatFund 

provides notices about SRP statistical grants, funding opportunities, and application procedures. NCI’s statistical 

grant portfolio related to cancer control and population sciences encompasses many areas of research, including cancer 

treatment, epidemiology, prevention, outcomes research, 

dietary assessment, and cancer surveillance. Statistical topics range from longitudinal and spatial models to fixed, 

mixed, or random effects models, as well as survival and 

sequential analyses and Bayesian inference. Research results 

can be interpreted across the cancer control continuum and 

include prevention, detection, diagnosis, treatment, and 

survivorship. Many National Institutes of Health’s (NIH) 

statistical grant applications are cancer related. 

Figure 5 illustrates the topics covered in the SRP grant portfolio. The largest single category is cancer control modeling. 

The 44 grants classified under statistical methods represent 

a range of topic areas, such as survey analysis, analysis of 

complex datasets, survival analysis, Bayesian applications, 

and general statistical methodologies.

Reporting and Interpreting Data

In addition to collecting and analyzing data, a primary 

mission of SRP is to communicate cancer statistics to a 

variety of audiences. The form of the communication 

reflects the various needs of data users, ranging from 

evaluating progress toward meeting health objectives to 

resource allocation and planning. Cancer registry data 

provide insight into the identification of emerging trends 

through evaluation of cancer registry data. They also can 

indicate the need for additional interventions to provide 

evidence of the success or failure of previously implemented health policy. The usefulness of the data collected 

hinges on SRP’s ability to communicate the information 

to potential users, including the public.

Cancer Control P.L.A.N.E.T. The Plan, Link, Act, 

Network with Evidence-based Tools portal (http://

cancercontrolplanet.cancer.gov/) provides access to comprehensive cancer control data and resources for public health 

professionals to assist with the design, implementation, and 

evaluation of evidence-based cancer control programs. 

State Cancer Profiles. State Cancer Profiles (http://

statecancerprofiles.cancer.gov/) is a collaborative effort between 

NCI and CDC that provides graphic portraits of cancer statistics for prioritizing cancer control efforts in the Nation, 

states, and counties. Interactive maps, graphs, comparison 

tables, and support data are available for incidence rates, 

death rates, rate/trend comparisons, and prevalence projections to characterize the cancer burden in a standardized manner. Historical trends and interactive maps also 

are available. 

Annual Report to the Nation. The Annual Report to 

the Nation (http://seer.cancer.gov/report_to_nation/) describes 

cancer trends in the United States with data analyzed by 

anatomical site, population, gender, and other categories. 

The 2009 report devotes special attention to colorectal cancer and the use of microsimulation modeling as a tool for 

interpreting past and future trends that affect cancer control 

planning and policy decisions.

Cancer Trends Progress Report. The Cancer Trends 

Progress Report (http://progressreport.cancer.gov/) highlights 

progress against cancer in relation to Healthy People 2010 

targets. The report includes key measures of progress along 

the cancer control continuum and uses national trend data 

to illustrate where advances have been made.

Cancer Statistics Review. The annual Cancer Statistics 

Review (CSR) (http://seer.cancer.gov/csr/1975_2006/index.

html) is an online resource that reports cancer incidence, 

mortality, survival, prevalence, and lifetime risk statistics 

from 1975 through the most recent year for which data are 
24

Chapter

available. The current report presents statistics on 27 primary sites and subsites, organized into site-specific chapters, 

and includes delay-adjusted cancer incidence rates. Features 

recently added to the CSR include detailed histology breakdowns for lymphomas and for cancers of the oral cavity 

and pharynx, soft tissue, and pancreas; cause-specific survival by expanded race and ethnic groups; SEER 13 delayadjustment; and adjustments for Veterans’ Administration 

(VA) underreporting.

Fast Stats. Fast Stats (http://seer.cancer.gov/faststats/) is an 

interactive data query Web system that provides easy, rapid 

access to key SEER and U.S. cancer statistics at various 

sites. Data may be selected by age group, sex, race, and 

year of diagnosis. Incidence is the default statistic displayed; 

survival, prevalence, and lifetime risk also may be available, 

depending on the data site. 
25

Chapter 3

The Strateg

Where Do We Go Fro

SRP leads cancer surveillance research science with strong 

programs in individual- and group-level data collection, 

statistical methods, and modeling tools that enhance the 

understanding of risk, prognosis, and population differences (Figure 6). SRP works with surveillance partners to 

set standards for data collection, use, and communication. 

The October 2009 workshop participants identified major 

scientific and societal trends that affect the burden of cancer in the United States:

•	 Cancer survivors are living longer. This success brings 

a consequent surge in long-term health effects and cancer recurrences.

•	 There is a lessening of health disparities in some health 

areas, but constant or growing health disparities in 

other areas.

•	 The research community recognizes greater clinical 

and tumor heterogeneity within major cancer sites. 

The movement to develop therapies specific to tumor 

molecular and proteomic characteristics is anticipated 

to continue to expand. 

•	 Broad societal factors at multiple levels of analysis increasingly are being shown to have substantial impacts 

on the burden of cancer in the United States.

•	 Consumers’ and patient advocates’ interest in cancer 

statistics is burgeoning. Scientists have a better understanding of how to disseminate information and how 

people understand numbers and statistics.

•	 The growth of electronic health records (EHRs) 

has enabled more efficient and more comprehensive collection of data about the burden of cancer. 

Interoperability among data systems is facilitating opportunities for data linkages to provide more comprehensive data on factors not currently obtained in cancer 

registration systems.

The priorities and action plans for cancer surveillance research for the next 3 to 7 years aim to enhance cancer data 

collection efforts to include detailed patient characteristics 

using electronic and other tools that help refine the understanding of population differences and health disparities, 

and communicate these cancer statistics to researchers and 

the public. This chapter provides a brief rationale of each 

of the priority areas and action plans to meet urgent needs 

of public health and biomedical science research.

The informatics infrastructure needed to expand data collection in a timely and efficient manner currently is not in 

place. Major advances in the development of EHRs and 

electronic capture of patient information are ongoing; 
however, work remains to define how cancer surveillance 

systems will tie into the nationwide efforts to build up EHRs. 

As the electronic exchange of information and linkages of 

data from various sources expands, concerns regarding data 

privacy and confidentiality become more prominent and 

need to be addressed. Utilizing cancer surveillance data requires knowledge of informatics, demographics, statistical 

concepts, and disease characteristics. Increasing the number 

of researchers trained to use surveillance systems is critical 

in moving cancer surveillance forward.

Researchers who use SEER data and analytic tools developed for registry data require training across all areas of the 

cancer surveillance research spectrum, including gathering, 

compiling, analyzing, and disseminating data. For data to be 

used effectively and widely, NCI needs to make information 

more readily available and explain how to access and use 

it. A comprehensive approach to training for surveillance 

researchers is needed.

• Understanding risk, prognosis, 

 and populations differences

• Describing the relationships 

 between key factors influencing 

 health outcomes 

• Data visualization and interpretation 

• Enhanced measures of health 

 disparities

• Electronic capture

• Data Linkages

• Inclusion of secondary data 

 sources

• Identify gaps in existing data 

 sources

• Targeted data collected

Statistical Methods 

Development and Modeling

Individual and group level data

Detailed patient characteristics

Enhance Cancer Data Collection Describe Burden of Cancer

Standardized communication 

materials on cancer measures for 

specific audiences

Available resources to the 

researcher community

Methods and software for the 

presentation of national cancer data

Press, Public, Policymakers

Develop and Disseminate Information to a Range of Audiences

Science community Users of cancer data

Leading the Science of Cancer Surveillance

Figure 6. SRP’s Approach to Cancer Surveillance

Chapter

26
27

Table 3. Cancer Surveillance Priority Areas and Action Plan, 2010

Cancer Surveillance Program Straticlan 

Priority Areas and Action Plan 2

Priority Area1 Provide a more complete depiction of the burden of cancer in the United 

past, present, and future.

Action Plan

1. Link with other data sourc

2. Develop processes to obtain and use other types of variables and d

3. Collect more detailed individual data from a limited number of registr

Priority Area 2 Collect better quality data on the burden of cancer in the United Stat

efficiently through effective utilization of electronic tools.

Action Plan

4. Identify and evaluate relevant data sourc

5. Automate the capture of informat

6.    Expand interoperability and standardizati

surveillance data.

7. Use data-capture techniques and statistical modeling to improve the timelines

cancer statistics reporting.

Priority Area 3 Improve understanding of the differences and disparities in the bu

cancer in the United States.

Action Plan

8.   Enhance measurements of health disparit

9.   Expand data visualization and interpretat

Priority Area 4 Better understand the continuum in the burden of cancer in the United

from risk to prognosis.

Action Plan

10.   Evaluate the potential impact of prevention, early detection, and treatment on ca

statistics to inform cancer research and policy development.

11. Identify and track a cohort of cancer patients to obtain data and monitor impacts at t

population level.

Priority Area 5 Communicate cancer statistics more effectively to researchers and us

make the data more accessible and understandable to all.

Action Plan

12. Conduct needs assessment to identify data needs and audiences and develop a plan 

communicate cancer statistics and interpret data for audiences.

13. Produce standardized communication materials.

14. Disseminate information on existing resources to researchers and evaluate their effectivenes

15. Develop and disseminate methods and software for the analysis and presentation of Nation

cancer statistics.
28

Priorit

Provide a more complete depiction of the 

burden of cancer in the United States.

As epidemiologic and genetic research facilitates more 

personalized medical treatment, greater attention is being 

devoted to the progression of cancer in patients, from the 

onset of the disease through survivorship, as well as the 

identification of risk factors and biomarkers for cancer. 

Cancer surveillance must capture these trends and assess 

how they affect the burden of cancer by addressing data 

needs along the length of the cancer continuum, from 

screening and early detection through diagnosis, treatment, 

and outcome (survival, disease recurrence, or death). 

Concerted efforts during the past decade to collect information on care patterns, quality of life, and morbidity 

in cancer patients include the annual SEER Patterns of 

Care and RRSS studies, the Health, Eating, Activity, and 

Lifestyle (HEAL) study, Cancer Care Outcomes Research 

and Surveillance Consortium (CanCORS), and the NLMSSEER and SEER-Medicare datasets. Because of these activities, data currently collected by SEER cover incidence 

and survival information, with detailed characteristics of 

the cancer at diagnosis, some information on patient characteristics, and initial treatment data. No single initiative, 

however, provides a comprehensive look at a patient from 

diagnosis to death or long-term survival. With advances in 

technologies, broadening focus on the EHR, and the surge 

in biological and molecular information, a significant opportunity exists to expand data collection and management 

to include complete treatment data; recurrence, disease status, and trajectory data; comorbidities; and late effects. Calls 

have been made for the creation of a patient treatment 

summary and survivorship plan to support the move toward personalized medicine for cancer patients. Data on the 

existence of such summaries and plans should be captured 

by future surveillance systems.

Monitoring treatment on a population level allows for a basis for understanding the usage and impact of treatment on 

outcomes such as survival, mortality, and treatment-related 

comorbidities. It is an essential step in assessing if all cancer 

patients have access to the latest treatments and benefit 

from medical advances.

Data collection should be increased along the cancer continuum through coordination with other programs to identify common goals, and the provision of data from other 

sources to SEER to complete the information or expand 

the variables collected, both on an episodic and a routine 

basis. Statistical methodologies that can assist with data expansion efforts are described in Box 3.1. Additional opportunities to broaden data collection include identifying gaps 

in population coverage, developing approaches to collect 

information from individuals not covered by existing sources, and automating linkages between SEER and other data 

sources. NCI should supply the infrastructure and expertise 

to develop cancer survivor cohorts that can provide missing 

data for specific geographic areas, and also should support 

research to better understand regional differences, which 

will help generalize information to the U.S. population.

Current SRP surveillance systems rely on population data 

from the census and other sources, cancer registry data, 

and mortality data systems to estimate risk of cancer, 

prevalence of cancer, and survival. A systematic approach 

is used to obtain statistics about recurrences; use of longterm cancer-directed therapies; and physical, social, and 

emotional sequelae. It depends on systematic registry efforts to capture and relate these events to population data, 

and thence to enable the computation of rates; in some 

instances, observational cohorts can provide this information. Extensive research is needed to develop standardized 

definitions for recurrences that are sufficiently practical to 

be applied in large populations that can be generalized to 

U.S. cancer patients. In addition, it may be useful to establish cohorts that provide this information. For example, 

information obtained via a population-based sample from 

a cohort of patients from the registry system could be 

Chapter
29

Cancer Surveillance Program Straticlan 

used to assess or compare morbidity or survivorship for 

a wide range of subpopulations. 

Three specific research opportunities to advance data collection along the patient continuum are:

•	 Link with other data sources.

•	 Develop processes to obtain and use other types of 

variables and data.

•	 Collect more detailed individual data within a limited 

number of registries.

Action Plan 1: Link with other data sources.

The cancer surveillance system should be improved to better handle data on the growing number of cancer survivors 

and in preparation for the expected increase in the aging 

population in the United States. A key ingredient for success in this effort is to enhance the system without incurring additional costs. Newer technologies and more data 

linkages may allow expansion of data with cost savings. 

Important operational processes to consider in developing 

such a system include utility of data linkages to allow collection of additional data needed in a cost-effective manner; 

EHRs and structured, standardized formats for collecting 

clinical data; and collaborations across governments and 

with clinical data networks.

Specific research opportunities in this area include: 

Coordinate with existing collaborators and initiatives or 

other data sources to gather missing data; expand the 

variables collected; or promote a standardized, cancerfocused EHR that incorporates data into the SEER database routinely, particularly through automated collection 

processes and linkages. Such organizations and initiatives 

could include: CMS’ Medicare data, larger insurance systems (HMO Cancer Research Network [CRN]), National 

Comprehensive Cancer Network (NCCN), the American 

Society of Clinical Oncology (ASCO), NCI’s caHUBtm, 

AJCC’s National Cancer Data Base (NCDB), and pharmacosurveillance groups. 

Box 3.1.   Statistical Methds:  Promoting Data 

Statistical methodologies and applications can be used to facilitate the collection of data from a wider range

of sources. Research opportunities include:

• Development of imputation methods for missing data. As we expand the information collected it will become increasingly important to account for missing information and adjust for related biases in populati

estimates.

• Development of competing risk survival methods that incorporate comorbidities. Survival models th

predict outcomes for cancer patients will need to include information on the whole individual not just

the cancer diagnosed. Competing risk models that adjust for existing health condition when estimating

prognosis will customize results for individuals.

• Data mining techniques to identify adverse outcomes associated with treatments. Identifying rare outcom

associated with increased risk or cancer treatments through population data will require the advanced

data mining techniques and procedures to distinguish false alarms from signals that would warrant further

investigation.
30

•	 Explore the relationship between reporting sources 

and other sources of information and the contents of 

the registries’ summary records to maximize the benefits achieved from collected data. The existing twoway data flow involves, for example, making data from 

RRSS available through the SEER system, and bringing 

data from hospital special studies into the SEER system from local registries into the central registry. Such 

efforts require that legal requirements in this area be 

identified and satisfied. 

•	 Develop a plan to address Institutional Review Boards’ 

(IRB) concerns about sharing data and samples. 

•	 Establish a “biospecimen library” to house blood or 

tissue samples to help address anticipated changes 

driven by continued discoveries in basic and clinical 

research and their eventual incorporation into care 

delivery. Analyze the types of data that are collected 

and determine how registries might serve as a bridge 

between discovery research and clinical practice. RTR’s 

experiences with maintaining biospecimens from several SEER registries could assist in the development 

of a library.

Action Plan 2: Develop processes to obtain and use 

other types of variables and data.

SRP surveillance systems rely on population data from a variety of primary and secondary sources, including the census, cancer registry data, and mortality data systems. Each 

of these sources has a unique informatics infrastructure 

and modes of operating and collaborating. A systematic approach to obtaining and using other types of variables and 

data would facilitate the capture of cancer data and expedite 

the computation of population-based cancer statistics. 

Potential models for data access include SEER-Medicare 

and the California Community Health System. These systems have created public files while providing security layers 

and additional precautions regarding degrees of access to 

individual private data; they use a firewall to protect sensitive information. The Tools for Electronic Data initiative 

and the Cancer Research Network (CRN)-SEER data transfer pilot project also serve as data expansion models for 

developing processes to obtain and use other information.

Data needs include: socioeconomic status and education; 

height/weight; address, including past addresses; race/ethnicity (better quality data); family history; tumor subtypes 

based on molecular signature; environmental risk factors 

(e.g., satellite imagery); treatment and interventions; and tobacco use, comorbidities, insurance information, and mode 

of detection. Other variables include geographic/built environment, census tract, education, BMI, family history, and 

environmental or other risk factors. Genetic/biomarkers 

for prognosis are a statistical priority.

Specific research opportunities in this area include:

•	 Develop processes to capture more individual data 

through both primary data collection and increasing 

linkages with secondary sources. Processes should be 

sensitive to issues of privacy, statutory regulations, and 

patient consent, and should incorporate flexibility and 

adaptability to accommodate changes in scientific advances and data collection as methods of quality care 

or assessments of risk change in the future. 

•	 Examine methodological issues to determine the best 

way to: support the expanded collection of patients’ 

treatment history, disease status, and followup; prepare 

for the collection, access, and use of genomic data 

(germline and somatic DNA); and facilitate collection 

of and access to multilevel data from multiple sources. 

Missing denominator data also should be imputed from 

residual tissue repositories. 

•	 Explore linkages with the SEER dataset in understanding health disparities, social determinants of health, 

and genetic and geographic population subgroups, in 

tandem with Priority Area 3. Develop pilot studies to 

determine the efficacy of using SEER data at the census level and to investigate additional linkages at this 

level. Expand SEER data to provide a resource for 

researchers through custom population data requests 

and targeted studies. 

Action Plan 3: Collect more detailed individual 

data from a limited number of registries.

The factors influencing the burden of cancer are extremely 

complex, and more detailed individual data could strengthen the understanding and prognosis of cancer. Despite the 

Chapter
Cancer Surveillance Program Straticlan 

31

increasing availability of EHRs and genomic data, there 

are serious obstacles to collecting all of the information 

needed. For example, it is frequently difficult to capture data 

when patients suffer from cancer recurrences.

Specific research opportunities in this area include:

•	 Develop a surveillance system to capture systematically 

the extent of: use of long-term cancer-directed therapies and recurrences; as well as to enhance the physical, social, and emotional sequelae available through 

SEER-MHOS. An intensive, rigorous data-collection 

process could be used to extrapolate regionally specific data to a broader application across U.S. populations. An alternative approach is the “sentinel registry” 

model, in which a few cancer registries concentrate on 

collecting more detailed individual data and specimens 

on a limited basis from a specific area(s). A “sentinel 

registry” approach is appropriate for times when widespread adoption is not feasible because of the costs associated with collecting additional variables. The model 

could encompass one disease site covered by one registry for a 2- to 3-year followup as a demonstration, 

either population-based or sampling. 

•	 Consider cost-effective alternatives to the “sentinel 

registry” model, such as establishing partnerships with 

other data-collection efforts that collect samples (e.g., 

or linking with large clinical or observational trials 

(e.g., Prostate, Lung, Colorectal and Ovarian Cancer 

Screening Trial [PLCO], which has collected specimens 

from individuals for the past 15 years). 

Priorit

Collect better quality data on the burden of 

cancer in the United States more efficiently 

through effective utilization of electronic tools.

Integrated health informatics is a central component in 

the contemporary health care system setting, particularly 

in cancer surveillance. 

NCI has developed and refined several premier tools 

to capture electronic data, including: SEER*DMS and 

caBIG®, as well as SEER Abstracting Software and 

NAACCR Interoperability Working Groups. SEER*DMS 

has played a significant role in transforming populationbased research by helping registries access, process, and 

submit high-quality data to NCI. E‐Path screens pathology reports for cancer-related reports in 239 facilities at 13

SEER central cancer registries, and ongoing refinements 

include auto-coding and use of synoptic guideline formats. 

caBIG® provides a conduit between institutional infrastructures to allow information sharing while respecting both 

patient and organizational confidentiality. Cancer surveillance informatics also involves linkages between datasets, 

such as SEER-Medicare and SEER-NLMS, as well as the 

development of software and dissemination of results. 

Advances in technological and computing fields, the development of more sophisticated electronic platforms and 

tools, and the concerted movement toward widespread 

adoption of EHRs present a prime opportunity to develop 

a strategy to use electronic tools to expand the collection of 

surveillance data. As new data sources are found and mined, 

however, patient privacy and confidentiality must be safeguarded as tools are used to access electronic data and autopopulate SEER data fields. NCI will continue to participate 

actively in the national Health Information Technology effort and will help to develop standards on electronic reporting. Through an RFP on Tools for Electronic Data, 

NCI will expand its capabilities to collect population-based 

cancer surveillance data from electronic pathology reports 

beyond the current SEER dataset. Statistical methods 

that can help to capture electronic data are suggested in

Box 3.2. Additional challenges include obtaining future research consent from patients, developing new models for 

e-data acquisition, creating real-time data feeds in the datacapture process, and reducing the timeframe for generating 

and reporting cancer incidence rates.
32

Statistical Metho

 Capturing Electro

Statistical methodologies and applications can

be used to facilitate capture of electronic data.

Research opportunities include:

	 • Development of matching algorithms a

learning algorithms to efficiently merge

datasets.

	 • Use of Natural Language Processing (NL

to help obtain defined data elements from

free text in medical records.

	 • Development of approaches for linkage 

other databases.

Specific research opportunities to help capture existing data 

and add to the portfolio of surveillance information include:

•	 Identify and evaluate relevant data sources.

•	 Automate the capture of information.

•	 Expand the interoperability and standardization of 

surveillance data.

•	 Use data-capture techniques and statistical modeling to 

improve the timeliness of reporting cancer statistics.

Action Plan 4: Identify and evaluate relevant 

data sources.

SEER’s surveillance data currently covers a range of geographic areas and select populations, from which extrapolations are made and applied across a broader population. 

Collaborators provide further data, but data sources beyond SEER and its collaborators are needed. These sources 

should provide information that both expands the current 

boundaries of the SEER dataset and supports the emphasis 

on population-based cancer surveillance.

Specific research opportunities in this area include:

•	 Evaluate ongoing collaborations for their potential to 

obtain further data, including further synergy between 

SRP and CMS to collect more treatment data using the 

SEER-Medicare dataset. 

•	 Develop new algorithms or tools to feed treatment data 

back to the registries from SEER-Medicare. 

•	 Address treatment definitions and coding rules that 

limit SEER’s ability to link to treatment data from other 

sources. HL7 extraction processes may be helpful in 

providing links to other clinical data sources.

•	 Develop an approach that leverages the caBIG® network to link cancer surveillance data and data from 

other sources.

Action Plan 5: Automate the capture of information.

An informatics approach is needed on a broad scale to automate the capture of information. SEER*DMS currently 

provides a good model for the automation of processes that 

make data submission to NCI as seamless as possible. The 

widespread adoption of EHRs slated for 2015, however, 

is a prime opportunity to expand automation processes to 

capture more cancer-related information. A key element in 

this automation is NLP, a nexus of computer science and 

linguistics focused on the interactions between computers 

and human languages. The NLP field is rapidly improving 

the capacity to identify and extract discrete data items from 

a wide range of free text documents that may contain grammatical and other errors. 

Specific research opportunities in this area include:

•	 Explore NLP’s capability to extract information from 

reports produced by hospitals and physicians’ offices.

•	 Determine NLP’s capability to use auto-coding and 

transformation of data to synoptic formats (Synoptix) 

to increase recognition sensitivity and accuracy. 

Box 3.2. 

Chapter
33

Cancer Surveillance Program Straticlan 

Action Plan 6: Expand the interoperability and 

standardization of surveillance data.

Cancer surveillance organizations and their partners often 

struggle with sharing data because of the myriad of software 

and hardware systems used. In addition, the data elements 

collected often vary widely. As the quantity of data burgeons 

and caBIG® provides a framework within which researchers 

can share data, the capability to exchange information within 

SEER as well as with hospitals and other data providers likewise should be expanded. Mutually beneficial collaborations 

should involve a structure for data collected at the point of 

care. A key element in the success of these interactions will 

be collegiality among organizations and widespread adoption 

of bioinformatic and surveillance data standards. 

Specific research opportunities in this area include:

•	 Establish standards for the collection and coding of 

health data to ensure the consistent quality and interoperability of data from a variety of sources and 

to facilitate the integration of information for use by 

registries, researchers, and other entities. Cancer surveillance organizations and their partners should work 

toward agreement on standards. 

Action Plan 7: Use data-capture techniques and 

statistical modeling to improve the timeliness of 

reporting cancer statistics.

Cancer statistics currently are reported several years following the year of diagnosis. With the public accustomed to 

receiving information quickly, the current 3-year time lag 

presents challenges in meeting this expectation. Decreasing 

the time between diagnosis, notification to the registry 

about a case, and reporting from the registry would assist 

in computation, help ensure more accurate estimation, and 

improve the timeliness in reporting cancer statistics. 

At the present time, Medicare is an important source of 

treatment data, but EHRs eventually may provide more 

current and complete treatment data for cases of all ages. 

Efforts should begin now to understand and refine approaches to capture and analyze this information.

Specific research opportunities in this area include:

•	 Develop data-capture techniques, including E-Path, to 

expedite the reporting process. 

•	 Design a delay adjustment model to collect incidence 

data in a short timeframe. Cancer surveillance programs should determine: (1) What is needed for all 

cancer registries to have the capability of capturing 

a high percentage of incident case information electronically? (2) How can technology similar to E-Path 

be extended to all diagnostic resources? (3) What data 

components are precoded, and what components need 

to be transformed? A system should be designed to 

alert registrars when more information becomes available in a case’s chart, or to extract new information 

automatically. 

•	 Accelerate reporting of data to CMS and improve access to more current Medicare treatment data.

Priorit

Improve understanding of the differences 

and disparities in the burden of cancer in the 

United States.

A major goal of the public health system is to improve 

understanding of the difference and disparities in the U.S. 

cancer burden. Broad societal factors at multiple levels of 

analysis increasingly are being shown to have substantial 

impacts on the burden of cancer in the United States. 

Measuring and monitoring the effectiveness of treatments 

across populations and subpopulations provides indications 

of where planning and programmatic shifts may be helpful 

in reducing the cancer burden. As significant changes occur 

in the use of tumor markers, a strategy should be developed 

to improve the characterizing of subgroups of cancer patients, particularly in terms of treatment, recurrence, and 

comorbidities. Statistical methods and tools also could help 

to improve understanding of the differences and disparities 

in the U.S. cancer burden (Box 3.3).
34

Specific research opportunities to help better understand 

differences and disparities in the burden of cancer in the 

United States include:

•	 Enhance measurements of health disparities.

•	 Expand data visualization and interpretation.

Action Plan 8: Enhance measurements of health 

disparities.

SRP plays a leading role in the development of health disparities measures that promote the cancer-related objectives 

of Healthy People 2020, including the elimination of health 

disparities across multiple categories. There is a lessening 

of health disparities in some health areas, but constant or 

increasing health disparities in other areas. Methods to measure cancer disparities should continue to be developed, 

with the recognition that (1) a single measure generally 

will not provide adequate information to inform policy 

decisions, (2) the choice of measures (e.g., absolute versus relative) makes a substantive difference in the results 

and interpretation of data, and (3) the size of population 

groups and magnitude of disparities should be considered 

when determining the impact on the public health system. 

SRP has encouraged the use of measures through HD*Calc 

(http://seer.cancer.gov/hdcalc/) and should continue to lead in 

this area of research by developing geographic and areasocioeconomic status measures that capture disparities, and 

by promoting HD*Calc as a resource for researchers.

Specific research opportunities in this area include:

•	 Consider ways to link SEER registry data to geographic 

variables such as county of residence and census tract 

of residence at the time of diagnosis. 

•	 Evaluate cancer cases linked to area-socioeconomic 

status/census tract variables, with the ultimate goal of 

making this data available to the research community. 

•	 Evaluate linkages between SEER data and other available data through geographic variables such as census 

tract of primary residence to expand the set of measures that may describe disparities.

Action Plan 9: Expand data visualization and 

interpretation.

Geographic methods, such as data visualization and interpretation, provide a unique perspective on the identification and quantification of cancer risk and prognosis. The 

value of geography goes beyond cancer etiology and can 

influence prevention, detection, diagnosis, treatment, and 

survivorship. Geography often serves as a surrogate for 

other factors, including environmental risk, behavioral risk, 

demographics, race/ethnicity and ancestry, and socioeconomic factors. However, more complete spatial and spatialtemporal data are needed about geographic subgroups of 

cancer patients. 

Although access to spatial data is improving through clearinghouses, Web-based access, and improved technologies, 

challenges remain concerning the availability of data, pace 

 Statistical Method:  Improving the Understanding of the Di

and Disparities in the U.S. Cance

Statistical methodologies and applications can be applied to further the understanding of differences and

disparities in cancer patients. Research opportunities include:

• Multilevel modeling––Modeling to capture the complex interrelated factors contributing to

health disparities.

Chapter

Box 3.3. 
35

Cancer Surveillance Program Straticlan 

of change, and spatial data linkage through geocoding. 

Geocoding techniques are improving in terms of match 

rates, accuracy, and measures of uncertainty, but spatialtemporal analysis is time consuming and involves daily 

travel and access to care as well as residential history and 

lifetime exposure.

Specific research opportunities in this area include:

•	 Develop new geocoding techniques to impute 

missing data. 

•	 Develop methods with communications researchers 

to display the communication of uncertainty in spatial 

analysis and visualization of data.

Priorit

Better understand the continuum in the 

burden of cancer in the United States from 

risk to prognosis.

Cancer risk and prognosis address both sides of a cancer 

diagnosis: prevention and detection (before diagnosis) and 

treatment and survivorship (after diagnosis). Surveillance 

research within SRP currently approaches risk and prognosis in populations by understanding health disparities and 

social determinants of health, as well as genetic and geographic population subgroups. Researchers and clinicians 

are studying and using tumor markers along the disease 

continuum, including in terms of treatment, recurrence, 

and comorbidities. The cancer surveillance research community can help with these efforts by devising better ways 

to characterize subgroups of cancer patients. 

During the past decade, NCI has built a multi-faceted portfolio to understand population differences that includes 

the RTR, monographs on health disparities, HD*Calc, 

CISNET, and grants on data display/presentation. 

Challenges remain, however, in identifying and obtaining 

relevant denominator data for race/ethnicity, socioeconomic data and social networking, the pace of change in 

genomic discoveries about genetic subgroups, and spatial 

and temporal data about cancer patients. To advance population sciences research about risk and prognosis, pilot 

studies should be developed to determine the usefulness of 

analyzing SEER data at the census level, promote the use 

of HD*Calc to researchers, help CISNET expand cancer 

sites, and interact with other research and policy groups. 

Other opportunities to apply statistical methods to better 

understand risk, prognosis, and population differences are 

provided in Box 3.4. Alternative formats also should be 

developed to display annual cancer statistics. Additional opportunities include multilevel modeling to link population 

and molecular models and use of expanded data collection 

and cancer cohorts to gain a more comprehensive understanding of survival in populations and subpopulations.

Specific research opportunities to advance understanding 

of risk, prognosis, and populations differences are:

•	 Evaluate the potential impact of prevention, early 

detection, and treatment on cancer statistics to inform 

cancer research and policy development. Develop a plan 

to better understand characterizations of cancer populations and subpopulations, in unison with efforts described in Priority Area 3 above.

•	 Identify and monitor a cohort of cancer patients by 

sampling a cohort from the registry system, in which patients represent the entire population and provide the 

ability to examine health disparities across many levels.

Action Plan 10: Evaluate the potential impact of 

prevention, early detection, and treatment on cancer statistics to inform cancer research and policy 

development. 

The ultimate goal of prevention, early detection, and treatment is to reduce the rate of cancer incidence and mortality. 

As new information, technologies, and treatment advances 
are disseminated, the surveillance program should assess 

the effect on the cancer burden in the population. Through 

the understanding of risk factor trends, use of early detection and treatment, and information on risks and efficacy 

from clinical trials, models can predict the impact on cancer 

incidence and mortality trends. Modeling also can provide 

estimates of potential impact if interventions were more 

widely implemented in the population. This type of analysis provides both the feedback needed to evaluate the success of particular advances in early detection and treatment 

and the input needed by policymakers and health planners 

when deciding where to focus their efforts. Surveillance can 

evaluate differences between results observed in a tightly 

controlled clinical setting and what occurs in the population 

setting. CISNET has provided an example of the contribution that population modeling can make in assessing the 

benefits of early detection and treatment advances, comparing the benefits associated with different technologies and 

treatments, and informing policy decisions. 

Specific research opportunities in this area include:

•	 Expand surveillance modeling to cover a wider range 

of applications and cancer sites. 

•	 Incorporate genomic and family history risk profiles.

•	 Upstream modeling––determinants of trends in risk 

factors, screening, and treatment.

•	 Issues in comparative effectiveness research (CER).

•	 Optimizing biomarker development strategies.

•	 Multiscale modeling, ranging from the molecular to 

population level.

•	 Evaluation of diagnostic tests.

•	 Optimal routes to reducing health disparities.

•	 Translation of trial results into clinical guidelines and 

public health policy.

•	 Develop interactive policy-level decision-making tools.

Action Plan 11: Identify and track a cohort of 

cancer patients to obtain data and monitor impacts 

at the population level.

As cancer patients enjoy increased longevity, health providers and biomedical researchers remain concerned about 

cancer recurrence and linking the effects of comorbidities on recurrence. The definition of recurrence is not well 

established or standardized for most cancers, and it relies 

on a battery of tests that are not applied consistently to all 

patients. In addition to recurrence, concerns exist about 

Statistical Method:  Advancing the Understanding of

Prognosis, and Population Differe

Statistical methodologies and applications can be applied to further the understanding of risk, prognosis, and

population differences. Research opportunities include:

•	 Understanding risk of disclosure to ensure patient confidentiality. Methods need to be put in place to ensure

patient privacy when combining data sources.

•	 Small-area estimation methods and measures of uncertainty. Interest in producing estimates for small

geographic areas or small subpopulations continues to grow, leading to results with large levels of uncertainty. Understanding the limitations and communicating the uncertainty are essential to avoiding the

misinterpretation of results.

•	 Classification methods to identify cases with similar prognosis. As biological markers for risk and prognosis

continue to be discovered, grouping patients with similar prognoses will become more complex.This is the

first step to estimating survival and other outcomes that are patient relevant.

Box 3.4. 

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Chapter
37

Cancer Surveillance Program Straticlan 

patient-reported outcomes and biospecimens; data about 

these currently are not captured by SEER datasets, although 

other NCI program efforts to link patient-reported outcomes to SEER data, such as SEER-MHOS and SEERCAHPS, are beginning to provide national surveillance data 

on patient-reported outcomes. Other areas in which data 

are limited include: complete treatment data, trajectory 

data, clinically relevant and evolving data, comorbidities, 

late effects, patient-reported outcomes, multilevel analyses, 

health behaviors, and patient treatment summary and survivorship plans.

Newer cancer-directed therapies may be having a substantial impact on the cancer burden in the United States. In 

addition, therapies can be prolonged, even over many years, 

with long-term or indefinite physical, social, and emotional 

consequences. The effect of these newer cancer-directed 

therapies and of prolonged treatment regimens has not 

been established at the population level. Knowledge of this 

impact may help guide medical practice and health policies. 

A holistic approach to population studies involving investigators collaborating across registries would build a base of 

knowledge more effectively than a selective, single-aspect 

focus brought by an investigator working within one cancer 

registry. The registry system also would provide nationally 

representative samples to supplement data from more localized groups, allowing the opportunity to examine socioeconomic factors and other health disparities variables.

Specific research opportunities in this area include:

•	 Identify and follow a cohort of cancer survivors who 

are identified through the registry system to supplement data from more localized groups and examine 

health disparities factors, such as socioeconomic status. 

Develop guidance for groups that wish to develop such 

a cohort, including data consolidation and data collection aimed at filling gaps for geographically defined 

areas. Design a plan to provide access to the data by 

investigators who are not participating in the cohort.

Priorit

Communicate cancer statistics more effectively 

to researchers and users and make the data 

more accessible and understandable to all.

The availability to use cancer statistics by a broad range of 

audiences has expanded significantly during the past decade. 

Consumers and patient advocates are increasingly interested 

in cancer statistics; scientists have a better understanding 

of how to disseminate information and how people understand numbers and statistics. Each type of audience—from 

researchers, registry staff, tumor boards, and NCI staff, to 

journalists, patients and patient advocates, policymakers, 

and the public—seeks data for different purposes, and 

each time data are used provides an opportunity for data 

misinterpretation. 

NCI has built a portfolio to address communication needs, 

including an ongoing study of SEER branding, CSR, cancer fact sheets, the Annual Report to the Nation, state cancer 

profiles, P.L.A.N.E.T., short courses on surveillance survival 

methods, and web sites. 

The need for a more comprehensive, unified approach to 

communications about cancer statistics data to these audiences is growing. A needs assessment to better help audiences, the promotion and revision of existing software and 

products, and a cancer survival query system all would help 

fine-tune NCI’s communications. Statistical methods and 

tools also could help improve the communication of cancer 

data (Box 3.5). A National Cancer Index could provide a 

centralized resource for cancer incidence information. Other 

opportunities include the introduction of alternative formats 

to display cancer statistics, assistance to registries in their 

communications with NCI and between registries, expanded 

communication regarding surveillance statistical methods, 

and periodic evaluations of communications materials.
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Chapter

Research opportunities to communicate cancer statistics 

and resources available to the research community include:

•	 Conduct a needs assessment to identify data needs and 

audiences and develop a plan to communicate cancer 

statistics and interpret data for audiences.

•	 Produce standardized communication materials that 

are informed by research advances in the communication and interpretation of health data. 

•	 Disseminate information on existing resources to researchers and evaluate their effectiveness.

•	 Develop and disseminate methods and software for the 

analysis and presentation of national cancer statistics.

Action Plan 12: Conduct a needs assessment to 

identify data needs and audiences and develop a 

plan to communicate cancer statistics and interpret 

data for audiences.

As the broad swath of data about cancer incidence, mortality, morbidity, and survival continues to be gathered 

and used by many groups, there is a need to communicate 

cancer statistics and help researchers and other audiences 

interpret and report data with accuracy. Information about 

cancer sought by scientists, however, differs greatly from 

that requested by lay audiences. Different communications 

media are needed to reach a plethora of audiences, such 

as researchers, registry staff, tumor boards, journalists, patients and their families, patient advocates, policymakers, 

journalists, and the public. A plan to communicate cancer 

statistics should be developed that distinguishes approaches 

for these audiences. 

Specific research opportunities in this area include:

•	 Conduct a needs assessment to determine the type of 

information sought by investigators. Identify those 

questions currently asked of national and local SEER 

staff and examine how SEER data are used. Conduct 

a bibliometric analysis of publications that use or otherwise reference SEER data to obtain additional information. Content that should be examined includes 

statements of data limitations, materials targeting local populations, and cross-links. Review the Health 

Information National Trends Survey (HINTS) and 

other needs assessments to extract useful approaches 

and suggestions in the process of conducting the assessment. Also identify existing products and determine whether they should be expanded or new ones 

developed. 

•	 Delineate ways to promote existing products and 

resources, such as Fast*Stats and SEER*Stat, to the 

scientific community (e.g., expanded training and dissemination, short courses, conferences, and Webinars). 

Develop recommendations regarding how to translate 

data for journalists and the public, including information about data limitations, as well as how locally 

relevant cancer data could be provided to researchers. Accentuate the need to evaluate communications 

materials periodically, including the effectiveness of 

formats used.

•	 Survey users of State Cancer Profiles and other data 

on Cancer Control P.L.A.N.E.T. to help refine the resource to promote more efficient use of the data by 

researchers preparing grant applications and those developing presentations to Congress. 

•	 Consider the public’s need for cancer surveillance data 

and whether to incorporate the lay audience in the assessment and develop a separate plan to meet this need. 

Identify preferred formats for communicating data and 

perform usability testing for each type of audience—

including via the Web, print, and Webinars; specific formats that should be tested include accessible language, 

various formats that facilitate rapid access to desired 

information, and segmented Web pages. Developing 

templates for presenting cancer surveillance data to 

different audiences would expedite the process. Revise 

standardized fact sheets to be more descriptive, present 

risk in the context of other cancers and other major 

diseases, and show risks by age or other approaches 

that have been found to enhance comprehension of 

such data.

Action Plan 13: Produce standardized communication materials.

A number of national efforts currently disseminate cancer 

surveillance data to interested parties. Collaboration among 

leading organizations, including NCI, CDC, and ACS, 

has facilitated and guided some of this communication. 
39

Cancer Surveillance Program Straticlan 

However, much of the information available about surveillance data is fragmented or in some cases inconsistent in 

presentation. Miscommunication and misinterpretation of 

prognosis information is a particular problem. The adoption 

of standardized messages and templates for communicating cancer surveillance data will present a cogent, coherent 

voice about cancer trends to the health community at large, 

dissipate confusion, and help reduce data misinterpretation. 

The development of standardized templates that present 

data to different audiences could help advance this effort 

significantly. In particular, fact sheets that contain the same 

type of information for existing data (e.g., caveats/descriptors, or good news/bad news) could be prepared with existing resources in a relatively short timeframe.

Specific research opportunities in this area include:

•	 In a collaborative setting that includes NCI, CDC, ACS, 

and their registries, define data communication and develop strategies and standards for data communication 

by reaching consensus about common data elements, 

while remaining sensitive to issues related to a potential 

overload of cancer surveillance information. 

•	 Consider ways to communicate data limitations to researchers to avoid misinterpretation, such as the inclusion of caveats that acknowledge data limitations.

•	 Identify possible cross-links between the SEER 

Web site and other credible Web resources (e.g., risk 

calculators, screening recommendations), particularly 

to help users answer questions that SEER data cannot answer. Review other federal Web sites (e.g., NIH, 

Department of Health and Human Services [HHS]). 

Use a structured approach to weigh the benefits and 

challenges of linking SEER to nonfederal Web sites, 

recognizing the lack of consensus on standards for 

health information Web sites, as well as the difficulty 

in obtaining government approval for such linkage.

•	 Develop standardized, accessible communication materials for lay audiences, as directed by the needs assessment and communication plan described in Research 

Opportunity 12. 

Action Plan 14: Disseminate information on 

existing resources to researchers and evaluate their 

effectiveness. 

The SEER cancer registry system serves as a platform for 

research studies in the field of cancer. A wide variety of 

resources are available currently, and more are being developed. As available resources are developed, SRP needs 

a structured approach to promote available resources and 

a system to make these resources available to the wider 

research community to maximize their potential. SEERMedicare has been used widely by the research community 

and provides a model of how research resources can be 

used to address a range of research questions. The RTR 

and the linked SEER-NLMS databases are examples of resources that have potential to be more widely disseminated 

to the larger research community. SRP should develop a 

 Statistical Method:  Enhancing the Communication 

to the Research Comm

Statistical methodologies and applications can be used to facilitate the communication of data to the cancer

research community. Research opportunities include:

	 • Development of absolute measures of risk (in the presence of competing risks). Cancer survival typically

is reported in the absence of competing risks.This measure currently provides the best progress measure

in the fight against cancer because it is not influenced by changes in other diseases. Better measures are

needed, however, to help the individual patient who considers their overall heath when making decisions.

Box 3.5. 
40

Chapter

structured approach to communicating available resources 

and facilitating the acquisition and use of these resources. 

Specific research opportunities in this area include:

•	 Determine the needs of local registries in disseminating local-level cancer surveillance data. Define the steps 

required to access cancer surveillance data that is not 

publically available and facilitate the process. 

•	 Conduct a systematic, heuristic review of existing resources and how they are promoted and accessed to 

better understand the needs of the extramural research 

community and determine the strengths, weaknesses, 

and user satisfaction with existing resources available 

in SRP. 

•	 Investigate methods of making data from SEER and 

other registries more readily available to researchers for 

a variety of purposes

Action Plan 15: Develop and disseminate methods 

and software for the analysis and presentation of 

national cancer statistics. 

NCI’s surveillance program has developed statistical methods and software for the analysis of surveillance data in 

the past and continues to develop and refine analysis tools. 

These tools allow researchers with diverse backgrounds to 

easily access surveillance data and use the data for particular purposes. Although SEER*STAT and related software 

programs are widely used, NCI should further promote the 

use of available analysis methods and tools and provide 

training on the use of available software. Dissemination 

and targeted training are expected to help invigorate the 

field and inform research conducted on the cancer control 

continuum to enhance the impact on cancer mortality in 

the United States.

Specific research opportunities in this area include:

•	 Determine better ways to communicate new and existing surveillance statistical methods. Communications 

should be expanded through through the use of monographs, special journal issues, a textbook on surveillance methods, or other materials as the quantity of 

cancer data increases. 

•	 Expand training efforts on the use of statistical methods and software to facilitate wider use of the tools 

developed at NCI.
41

Cancer Surveillance Program Straticlan 

Appendix A: 

Acknowledgements

This Strategic Plan would not have been possible without the collaboration and efforts of 

many individuals and organizations. Each made significant contributions throughout the 

process of preparing this document. We thank Brenda K. Edwards and Stacey Vandor for 

their invaluable support and comments throughout the process of developing this Plan.

2009 Cancer Surveillance Research Workshop Planning Committee

Kathleen A. Cronin, Chair

Rachel M. Ballard-Barbash

Martin Brown

Brenda K. Edwar

Eric (Rocky) J. Feue

Sally Glaser

Bradford W. Hesse

Muin Khoury

Marsha Reichman

Julia Rowland

David G. Stinchcomb

Stacey Vandor

Deborah M. Winn

Contract Support

The Scientific Consulting Group, Inc.

Marcia Feinleib, Project Director

Darlene Summers, Principal Writer

Charles Wallace, Graphic Designer

Appendices
42

Appen

Appendix B: 

NCI Resources and Tools for Cancer Surveillance Research

Annual Report to the Nation

http://seer.cancer.gov/report_to_nation/

Cancer Statistics Review

http://seer.cancer.gov/csr/1975_2006/index.html

Cancer Trends Progress Report

http://progressreport.cancer.gov/

Cancer Intervention and Surveillance Modeling Network (CISNET)

http://cisnet.cancer.gov/ 

DevCan

http://srab.cancer.gov/devcan/ 

Electronic Pathology (E-Path) software

http://www.aim.on.ca/products/ePath.jsp

Fast Stats 

http://seer.cancer.gov/faststats/

Health Disparities Calculator (HD*Calc)

http://seer.cancer.gov/hdcalc/index.html

Joinpoint

http://srab.cancer.gov/joinpoint/

Plan, Link, Act, Network with Evidence-based Tools (P.L.A.N.E.T.)

http://cancercontrolplanet.cancer.gov/

Rapid Response Surveillance Studies (RRSS)

http://seer.cancer.gov/rapidresponse

Residual Tissue Repository (RTR) Program

http://seer.cancer.gov/biospecimen

SEER

http://seer.cancer.gov/

SEER Data Management System (SEER*DMS)

http://seer.cancer.gov/seerdms/

SEER-Medicare data

http://healthservices.cancer.gov/seermedicare

SEER-Medical Health Outcomes Survey (SEER-MHOS)

http://outcomes.cancer.gov/surveys/seer3-mhos)

SEER-National Longitudinal Mortality Study (SEER-NLMS) database

http://surveillance.cancer.gov/disparities/nlms/

SEER*Prep

http://seer.cancer.gov/seerprep/index.html

SEER*Stat

http://seer.cancer.gov/seerstat/ 

StatFund

http://statfund.cancer.gov

State Cancer Profiles

http://statecancerprofiles.cancer.gov/
43

Cancer Surveillance Program Straticlan 

Appendix C:

List of Acronyms

ACoS	 American College of Surgeons

ACS	 American Cancer Society

AJCC	 American Joint Committee on Cancer

ARP	 Applied Research Program

ASCO American Society of Clinical Oncol

caBIG ® cancer Biomedical Informatics Grid®

CAHPS	 Consumer Assessment in Healthcare Providers and Systems

caHUBtm Cancer Human Bioban

CanCORS	 Cancer Care Outcomes Research and Surveillance Consort

CDC	 Centers for Disease Control and Prevention

CISNET	 Cancer Intervention and Surveillance Modeling Networ

CMS	 Centers for Medicare and Medicaid Services

COC	 Commission on Cancer (ACoS

CRN	 Cancer Research Network

CSR	 Cancer Statistics Review

CSRP	 Cancer Surveillance Research Program

DCCPS	 Division of Cancer Control and Population Sciences

EHR	 electronic health recor

E-Path	 Electronic Pathology softwa

GIS	 geographic information systems

HD*Calc 	 health disparities calculator

HEAL	 Health, Eating, Activity, and Lifestyle St

HHS	 Department of Health and Human Services

HINTS	 Health Information NationalTrends Survey

IRB	 Institutional Review Board

IACR	 International Association of Cancer Registries

MHOS	 Medical Health Outcomes Sur

NAACCR	 North American Association of Central Cancer Registries, Inc.

NCCCS	 National Coordinating Council for Cancer Surveillance

NCCN	 National Comprehensive Cancer Network

NCDB	 National Cancer Data Base

NCHS	 National Center for Health Statistics (CDC)

NCI	 National Cancer Institute

NCRA	 National Cancer Registrars Association

NHANES	 National Health and Nutrition Examination Sur

NLMS	 National Longitudinal Mortality Study

NLP	 natural language processing

NPCR	 National Program of Cancer Registries (CDC)

P.L.A.N.E.T.	 Plan, Link, Act, Network with Evidence-basedTo

PLCO Prostate, Lung, Colorectal and Ovarian Cancer ScreeningTr

RRSS	 Rapid Response Surveillance Studies

RTR	 ResidualTissue Repository Program

SEER	 Surveillance, Epidemiology, and End Results P

SEER*DMS	 SEER Data Management Sy

SIG	 Surveillance Implementation Group

SRP	 Surveillance Research Program

USCS	 U.S. Cancer Statistics
March 2011