Epidemiology generates a major part of the scientific knowledge needed for programs in cancer prevention and regulation of carcinogenic exposures. Case-control and cohort studies serve not only to identify carcinogenic risk factors but also to investigate dose-time-response relationships for purposes of quantitative risk assessment and the planning of intervention strategies. Statistical concepts are crucial for understanding the logical foundations of such studies and for devising appropriate methods of study design and data analysis. The broad objective of this proposal is to create new and efficient statistical methods or design and analysis of epidemiologic studies that are well suited to the quantification of human cancer risk, and to make the new tools available in a form suitable for immediate application by medical epidemiologists and others without extensive technical background. One specific goal is to generate families of statistical models for relative and absolute risk that are flexible enough to encompass competing hypotheses about the nature of the carcinogenic process. By comparing the goodness-of-fit of different models within each class, and by examining the stability of the fitted model to perturbations in the underlying data, it will be possible to evaluate the extent to which the data support one hypothesis over another. Use of general population cancer rates in the model should improve the efficiency of the analysis and the amount of information that is extracted from the data. Design modifications are suggested that should facilitate efficient estimation of relative risks with fewer subjects and lower costs. Mathematical analysis will be used to generate the new statistical models and investigate their theoretical properties. The methods will be tested immediately on several important sets of cancer epidemiology data. Their numerical characteristics will be investigated by Monte Carlo studies using parameter settings determined from these real applications. The new methodology will be implemented on microcomputers and described in revisions of established textbooks so as to facilitate its transmission to research workers.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA040644-03
Application #
3180948
Study Section
(SSS)
Project Start
1985-09-01
Project End
1988-08-31
Budget Start
1987-09-01
Budget End
1988-08-31
Support Year
3
Fiscal Year
1987
Total Cost
Indirect Cost
Name
University of Washington
Department
Type
Schools of Public Health
DUNS #
135646524
City
Seattle
State
WA
Country
United States
Zip Code
98195
Breslow, Norman E; Hu, Jie; Wellner, Jon A (2015) Z-estimation and stratified samples: application to survival models. Lifetime Data Anal 21:493-516
Breslow, Norman E; Amorim, Gustavo; Pettinger, Mary B et al. (2013) Using the Whole Cohort in the Analysis of Case-Control Data: Application to the Women's Health Initiative. Stat Biosci 5:
Breslow, Norman E; Lumley, Thomas; Ballantyne, Christie M et al. (2009) Using the whole cohort in the analysis of case-cohort data. Am J Epidemiol 169:1398-405
Breslow, Norman E; Lumley, Thomas; Ballantyne, Christie M et al. (2009) Improved Horvitz-Thompson Estimation of Model Parameters from Two-phase Stratified Samples: Applications in Epidemiology. Stat Biosci 1:32
Nelson, Kerrie P; Leroux, Brian G (2006) Statistical models for autocorrelated count data. Stat Med 25:1413-30
Breslow, Norman E (2003) Are statistical contributions to medicine undervalued? Biometrics 59:1-8
Platt, R W (2000) Saddlepoint approximations for small sample logistic regression problems. Stat Med 19:323-34
Leroux, B G (2000) Modelling spatial disease rates using maximum likelihood. Stat Med 19:2321-32
Platt, R W; Leroux, B G; Breslow, N (1999) Generalized linear mixed models for meta-analysis. Stat Med 18:643-54
McKnight, B; Tierney, C; McGorray, S P et al. (1998) Likelihood-based inference for the genetic relative risk based on affected-sibling-pair marker data. Biometrics 54:426-43

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