New Epidemiologic Methods for Reducing Measurement Error and Misclassification Bias in Cancer Epidemiology
New Epidemiologic Methods for Reducing Measurement Error and Misclassification Bias in Cancer Epidemiology
批准号:
10801058
负责人:
DONNA L SPIEGELMAN
金额:
$75.0万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-21 至 2027-08-31
关键词:
AccelerationAddressAdultAlcohol consumptionAmerican Cancer SocietyBehaviorBig DataBiological MarkersBirthCancer EtiologyCancer Prevention Study IIChildClinical TreatmentCohort StudiesColorectal CancerCommunitiesComplexComputer softwareDataDevelopmentDiabetes MellitusDietDiseaseElectronic Health RecordEnergy IntakeEpidemiologic MethodsEpidemiologistEpidemiologyEtiologyExposure toFacultyHandHealthHealth behaviorHeterogeneityHospitalsIncidenceIndividualInfantInternationalInvestmentsLife StyleMalignant NeoplasmsManualsMeasurementMeasuresMedicare claimMethodologyMethodsNational Cancer InstituteNegative FindingNon-Insulin-Dependent Diabetes MellitusNurses&apos Health StudyNutritionalOutcomePaperParticipantPatient Self-ReportPatternPharmaceutical PreparationsPhysical activityPoliciesPopulation ResearchPrevalenceProceduresProcessPublic HealthQuestionnairesRecoveryRelative RisksResearchRisk EstimateRisk FactorsRoleSeminalSleepSourceStructureTalentsTimeTranslationsUncertaintyUnited States National Institutes of HealthValidationVariantWomanWorkanticancer researchattenuationcancer epidemiologycancer preventioncancer riskclinical practicecohortcolorectal cancer riskcomorbiditycourse developmentdesigndietaryenergy balanceepidemiology studyexperiencefollow-upimprovedinnovationinterestlecturesmalignant breast neoplasmmassive open online coursesmenmethod developmentmortalitynovelnutritionprospectiverecruitresearch to practicesedentary lifestylesoftware developmentstatisticssymposiumtheoriesuser friendly softwareuser-friendlyvalidation studiesweb sitewebinar
中文摘要
项目总结/文摘
英文摘要
Project Summary/Abstract
Uncertainty in exposure and outcome measurements poses substantial challenges to the identification and
quantification of the causes of cancer. For example, although difficult to measure well, physical activity patterns
form the basis of many etiologic hypotheses concerning cancer risk. Cancer cases identified in electronic
health records (EHR) and other administrative ‘big data’ sources, such as Medicare claims data, are also
subject to misclassification. This exposure and outcome uncertainty leads to considerable bias in estimated
health effects, masking our ability to detect true associations, which are likely underestimated if detected at all.
It is the role of measurement error and misclassification correction methods to validly and efficiently estimate
the relationship between exposures and cancer outcomes. To accomplish this, a validation study is required for
estimating key features of the error process. Although much has been accomplished in this domain over the
years, the current aims address unsolved problems of high scientific significance that would otherwise remain
unanswered without this additional work. We will drill down into the multi-faceted themes that arise in cancer
research, tackling several seminal new directions of critical importance for the translation of the results of
population-based research to practice and policy. These methods will include estimation of the effects of
within-individual change in lifestyle behaviors on cancer risk corrected for measurement error in the change
variables, utilizing complex, currently under-accessed validation studies of diet and physical activity comprised
of repeated paper and online questionnaire self-reports and repeated concentration and recovery biomarkers
to obtain relative risk estimates unbiased by general measurement error structures which may include
correlated and biased errors, and estimating effects of exposures, including medications, other clinical
treatments, and health behaviors, on cancer incidence in EHR data. The new methods will be applied to
studies of the impact of within-participant change in alcohol intake on breast cancer incidence in the American
Cancer Society’s CPS-II cohort and in Harvard’s Nurses’ Health Study, and to a study disentangling the
impacts of diabetes and diabetes medications on colorectal cancer risk in Yale New Haven’s Epic EHRs.
Dissemination is a central feature of this research. User-friendly publicly available software will accompany all
new methods to be developed. The new methods will be disseminated through short courses and lectures at
national and international epidemiologic and statistical conferences, and through the development of a massive
online open course (MOOC). We have assembled an outstanding team of experts in measurement error
methods and statistical theory, along with an exceptional team of cancer epidemiologists with much prior
collaborative experience with the methods team, to guide the developments and their applications to the
scientific problems at hand. With the talented junior faculty and trainees to be recruited for this project, we will
solve the challenging problems that have been identified.
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