This is a proposal to develop, refine, and implement statistical methods for comparative effectiveness research in cancer. Using these methods, we will emulate complex randomized trials of personalized and dynamic strategies for treatment and diagnosis of cancer. We will focus our efforts on three areas. First, the comparative effectiveness of personalized strategies for the diagnostic work-up of cancer patients. Because questions about the allocation of health care for diagnostic work-up involves the comparison of strategies that are clearly assigned in the data, conventional statistical methods cannot be used and alternative methods, like artificial censoring plus inverse probability weighting, are required.
In Specific Aim 1, we will develop methods for the comparison of personalized health care delivery strategies in cancer diagnosis. As a motivating example, we will use SEER-Medicare to compare personalized strategies for the attendance to multiple medical centers by patients'and providers'characteristics.
In Specific Aim 2, we will develop methods for the comparison of personalized strategies in the presence of unmeasured confounding. Subgroup analyses are important in identifying subpopulations for which treatment is most effective, and thus to personalize treatment. Because confounding may change the interpretation of subgroup analyses and lead to bias in effect modification and interaction parameters, we will develop methodology to assess and correct for the impact of unmeasured confounding in the design, analysis, and interpretation of subgroup analyses in comparative effectiveness research.
In Specific Aim 3, we will develop methods for the comparison of dynamic health care delivery strategies in cancer treatment. Because many questions in cancer involve clinical decisions that depend on the evolving responses of patients or the characteristics of the local health care system, i.e., they are dynamic decisions, conventional statistical methods cannot be used and alternative methods, like dynamic marginal structural models and the parametric g-formula, are required. As a motivating example, we will compare dynamic strategies regarding androgen deprivation therapy for prostate cancer in the CaPSURE cohort.
In Specific Aim 4, we will develop user-friendly, high quality and open access software to be distributed to cancer researchers. This project complements the descriptive and inferential aims in Projects 2 and 3, and relies heavily on the Statistical Computing Core, and the organizational infrastructure, team building strategies, provided through the Administrative Core.

Public Health Relevance

This project will provide innovative and practical statistical methods to study the effectiveness of clinical strategies for diagnosis and treatment of cancer patients using data from large and complex observational studies. These methods will lead to a better development and use of observational data for cancer research, and a better care of cancer patients, and will assist clinicians and patients in their decision making process, and will inform clinical guidelines.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA134294-07
Application #
8754132
Study Section
Special Emphasis Panel (ZCA1-RPRB-2)
Project Start
Project End
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
7
Fiscal Year
2014
Total Cost
$220,832
Indirect Cost
$81,998
Name
Harvard University
Department
Type
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Bobb, Jennifer F; Claus Henn, Birgit; Valeri, Linda et al. (2018) Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. Environ Health 17:67
Chen, Han; Cade, Brian E; Gleason, Kevin J et al. (2018) Multiethnic Meta-Analysis Identifies RAI1 as a Possible Obstructive Sleep Apnea-related Quantitative Trait Locus in Men. Am J Respir Cell Mol Biol 58:391-401
Pierce, Brandon L; Kraft, Peter; Zhang, Chenan (2018) Mendelian randomization studies of cancer risk: a literature review. Curr Epidemiol Rep 5:184-196
Barfield, Richard; Feng, Helian; Gusev, Alexander et al. (2018) Transcriptome-wide association studies accounting for colocalization using Egger regression. Genet Epidemiol 42:418-433
Liu, Zhonghua; Lin, Xihong (2018) Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 74:165-175
Emilsson, Louise; García-Albéniz, Xabier; Logan, Roger W et al. (2018) Examining Bias in Studies of Statin Treatment and Survival in Patients With Cancer. JAMA Oncol 4:63-70
Sun, Ryan; Carroll, Raymond J; Christiani, David C et al. (2018) Testing for gene-environment interaction under exposure misspecification. Biometrics 74:653-662
Antonelli, Joseph; Cefalu, Matthew; Palmer, Nathan et al. (2018) Doubly robust matching estimators for high dimensional confounding adjustment. Biometrics :
Wilson, Ander; Zigler, Corwin M; Patel, Chirag J et al. (2018) Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression. Biometrics 74:1034-1044
Ritchie, Marylyn D; Davis, Joe R; Aschard, Hugues et al. (2017) Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 186:771-777

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