The long-term objective of this research is the development of statistical tools to improve scientific inference in cancer research, with a principal focus on the elucidation of the long-term effects of prevention and treatment interventions for breast cancer. A primary objective is to provide flexible semiparametric models to estimate and predict the population impact of prevention and adjuvant therapy on breast cancer using data from cancer screening trials and large prevalent cohort studies. Research will include a focus on methods that adjust for different biases encountered in such studies. A continuing effort is to provide a quantitative framework to determine optimal cancer screening schedules by balancing benefit and cost.
The specific aims of this competing renewal include: (1) to develop and evaluate a class of semiparametric density ratio models to test a treatment effect in right-censored length-biased data;(2) to develop a unified estimation and prediction tool for semiparametric transformation models applied to right- censored length-biased data;(3) to develop robust and efficient estimation and prediction procedures for semiparametric accelerated failure time models on right-censored length-biased data;(4) to develop estimation and prediction methods for data of uncertain time of disease initiation with or without a cure probability;(5) to optimize screening programs using decision theoretic approaches by explicitly incorporating different risk profiles and natural history distributions into the general model structure;and (6) to develop user-friendly computer codes linked to existing software for the medical and statistical communities.

Public Health Relevance

New statistical models and methods are proposed to study some longstanding problems as well as newly emerging issues for data observed in cancer research, which are subject to biased sampling. With the proposed analytic methods, the improved estimations of screening benefit and treatment intervention will better inform health policy and clinical practice in breast cancer prevention and treatment.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA079466-14
Application #
8657806
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Patriotis, Christos F
Project Start
1999-08-01
Project End
2015-04-30
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
14
Fiscal Year
2014
Total Cost
$127,283
Indirect Cost
$46,724
Name
University of Texas MD Anderson Cancer Center
Department
Biostatistics & Other Math Sci
Type
Other Domestic Higher Education
DUNS #
800772139
City
Houston
State
TX
Country
United States
Zip Code
77030
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Shen, Yu; Ning, Jing; Qin, Jing (2017) Nonparametric and semiparametric regression estimation for length-biased survival data. Lifetime Data Anal 23:3-24
Liu, Hao; Shen, Yu; Ning, Jing et al. (2017) Sample size calculations for prevalent cohort designs. Stat Methods Med Res 26:280-291
Shaitelman, Simona F; Lin, Heather Y; Smith, Benjamin D et al. (2016) Practical Implications of the Publication of Consensus Guidelines by the American Society for Radiation Oncology: Accelerated Partial Breast Irradiation and the National Cancer Data Base. Int J Radiat Oncol Biol Phys 94:338-48
Ning, J; Peng, S; Ueno, N et al. (2015) Has racial difference in cause-specific death improved in older patients with late-stage breast cancer? Ann Oncol 26:2161-8
Fouad, Tamer M; Kogawa, Takahiro; Liu, Diane D et al. (2015) Overall survival differences between patients with inflammatory and noninflammatory breast cancer presenting with distant metastasis at diagnosis. Breast Cancer Res Treat 152:407-16
Fujii, Takeo; Le Du, Fanny; Xiao, Lianchun et al. (2015) Effectiveness of an Adjuvant Chemotherapy Regimen for Early-Stage Breast Cancer: A Systematic Review and Network Meta-analysis. JAMA Oncol 1:1311-8
Shih, Ya-Chen Tina; Xu, Ying; Dong, Wenli et al. (2014) First do no harm: population-based study shows non-evidence-based trastuzumab prescription may harm elderly women with breast cancer. Breast Cancer Res Treat 144:417-25
Ning, Jing; Qin, Jing; Shen, Yu (2014) Score Estimating Equations from Embedded Likelihood Functions under Accelerated Failure Time Model. J Am Stat Assoc 109:1625-1635
Hoffman, Karen E; Niu, Jiangong; Shen, Yu et al. (2014) Physician variation in management of low-risk prostate cancer: a population-based cohort study. JAMA Intern Med 174:1450-9

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