Cancer-related Behavioral Research through Integrating Existing Data (R21)
Department of Health and Human Services
National Institutes of Health
NCI investment in IDA-related research would yield efficient and productive research that reduces costs, bridges behavioral research with other disciplines, and provides the ability to test hypotheses in ways that cannot be accomplished without data integration.
A. Enhanced Longitudinal Analyses
Prospective, longitudinal studies offer many advantages when studying processes or outcomes that develop or change over time; yet these types of studies are expensive and time-consuming. Retrospective use of any one data set is often limited in scope. However, merging several similar data sets using IDA provides an opportunity to study a broader swath of behaviors and experiences to better understand developmental processes without having to collect new data. The basic idea is to merge multiple existing datasets that have common data elements but different cohorts. These methods incorporate an enhanced longitudinal component by extending the timeframe of the study without the added time needed to collect the data. These more efficient types of IDA studies, however, require collaboration among researchers to share data, and the data must meet certain conditions before merging is possible. These conditions include having common data elements that assess process or outcome measures across studies and respondents with at least one common age (or any common variable that assesses a time-varying component) that serves to ‘link’ studies together. For instance, previous studies using these methods have examined changes in intellectual abilities over the lifespan and development of substance use and abuse in children, adolescents, and young adults. Application in the cancer arena would be particularly useful given that cancer-related behaviors, such as smoking and obesity, are initiated and maintained over a lifetime.
B. Assessment of Small Populations
Small populations are defined as populations for which the size, dispersion, or accessibility of the population of interest makes it difficult to obtain adequate sample sizes in order to test specific research questions. Examples of small populations include racial/ethnic sub-groups (e.g., Honduran Latin Americans), those with relatively rare characteristics (e.g., transgender persons), rare cancers, low base-rate behaviors, low income and rural populations, or people living in small geographic units such as census blocks or particular zip codes. The concern is that these groups may not be studied or may be aggregated inappropriately (e.g., combining all Latin American subgroups together) when there are important or unique characteristics of these groups that result in cancer-related health disparities or differences in specific cancer-related outcomes such as incidence or mortality. These types of studies have clear utility for understanding health disparities.
Benefits can be derived from linking methods to assess small geographic units. For example, methods such as small-area estimation would also be encouraged as a model-based approach to link information from population-based surveys. It takes advantage of the strengths of different surveys, with the goal of creating more accurate and precise outcomes at smaller geographic units.
C. Multi-level Analyses
Multi-level analyses can be achieved through data linkages. This refers to data collected at many levels of abstraction, that is, biological, behavioral, and societal. An example of this type of analysis would be a study that examines the relationship between individual smoking behavior measured through cotinine levels (as a biomarker) and self-reported smoking behavior; environmental factors such as number of stores selling cigarettes; and, finally, policy-level data such as cigarette taxes and indoor smoke-free laws. These data could be linked by a geographic unit– such as county where the individual resides –and then analyzed as a whole. This approach would incorporate the effects of multiple levels of influence to understand their effects on behavior or test for the effects of interventions on changing behavior.
Research questions of interest include, but are not limited to, the following:
- What are the long-term effects of chemotherapy on fatigue, cognition, and other treatment-related outcomes, taking into account individual characteristics (e.g., coping, multiple morbidities), type of cancer, type of therapy, health care access and use practices; and what are the characteristics of the different clinics in which chemotherapy is performed, and how do these contribute?
- How do individual risk perceptions, knowledge, and attitudes towards tobacco use interact with biological factors (e.g., ability to metabolize cotinine), environmental factors (i.e., built environment), and policy factors (e.g., laws banning smoking in restaurants and bars, cigarette taxes) to explain why current smokers continue to smoke or have trouble quitting?
- How do personal attitudes towards vaccination and sexual behavior (as measured within parents, adolescents, and young adults) together with physician recommendations and accessibility to health care (as measured within the built environment) interact to influence HPV vaccination uptake?
- What are the long-term trends in cancer incidence/mortality inequities? Have they changed over time, and what are biological, self-report, environmental, and policy factors that explain these differences? What can be learned to inform behavioral interventions based on these data?
- What are the most valid and precise estimates of cancer-related predictors, mediators/moderators, and outcomes for small populations that can be obtained by merging across population-level surveys? For example, do Mexican Americans exercise more or less than Cuban Americans, and are there different between-group predictors?
|Posted Date:||May 11, 2016|
|Last Updated Date:||May 11, 2016|
|Original Closing Date for Applications:||Jun 14, 2019|
|Current Closing Date for Applications:||Jun 14, 2019|
|Archive Date:||Jul 15, 2019|
|Estimated Total Program Funding:|
Native American tribal governments (Federally recognized)
Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education
City or township governments
Others (see text field entitled “Additional Information on Eligibility” for clarification)
Public and State controlled institutions of higher education
Independent school districts
Native American tribal organizations (other than Federally recognized tribal governments)
Public housing authorities/Indian housing authorities
Private institutions of higher education
Special district governments
For profit organizations other than small businesses
Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education
|Additional Information on Eligibility:||Other Eligible Applicants include the following: Alaska Native and Native Hawaiian Serving Institutions; Asian American Native American Pacific Islander Serving Institutions (AANAPISISs); Eligible Agencies of the Federal Government; Faith-based or Community-based Organizations; Hispanic-serving Institutions; Historically Black Colleges and Universities (HBCUs); Indian/Native American Tribal Governments (Other than Federally Recognized); Non-domestic (non-U.S.) Entities (Foreign Organizations); Regional Organizations; Tribally Controlled Colleges and Universities (TCCUs) ; U.S. Territory or Possession.|
|Agency Name:||National Institutes of Health|
|Description:||This Funding Opportunity Announcement (FOA) invites applications that seek to integrate two or more independent data sets to answer novel cancer control and prevention questions. The goal is to encourage applications that incorporate Integrative Data Analysis (IDA) methods to study behavioral risk factors for cancer, including tobacco use, sedentary behavior, poor weight management, and lack of medical adherence to screening and vaccine uptake. It is important that the data being integrated are from different sources and types (including both quantitative and qualitative; data may span different levels such as genetic and environmental) and should include at least one source of behavioral data. Importantly, applicants should use existing data sources rather than collect new data. In addition, creating harmonized measures, developing culturally sensitive measures, replicating results and cross-study comparisons will be encouraged.|
|Link to Additional Information:||http://grants.nih.gov/grants/guide/pa-files/PAR-16-255.html|
|Contact Information:||If you have difficulty accessing the full announcement electronically, please contact:
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