Errors of measurement in the risk factors under study are an important source of imprecision and bias in cancer epidemiology. An example of concern to our group is the within-person variability of questionnaire data on alcohol use being collected in a case-control study of breast cancer incidence. Recently there has been a renewed interest in studying statistical methods for treating this problem. In part, this interest can be traced to the growing use of more sophisticated techniques for determining exposures and outcomes. The new research in measurement error methods has primarily focused on techniques commonly used by epidemiologists such as binary regression and case-control studies. Yet relatively few of these methods have been introduced into the practice of cancer epidemiology. The long term objective of this application is to encourage the appropriate use of recently developed measurement-error methods. This will be accomplished by developing and documenting statistical methods in the form of functions for use in the analysis of epidemiologic data. The successful application of measurement- error methods to practical research depends on the availability of reliable implementations of the suggested algorithms in a computer software format familiar to researchers working in epidemiology. One of the recurrent themes in statistical literature regarding measurement errors in binary regression is the difficulty of doing maximum likelihood estimation when the likelihood contains an integral that cannot be evaluated analytically. A number of authors, including the investigators participating in this proposal, have suggested approximate methods of inference that eliminate this computational difficulty, including efficient score tests for generalized linear models, approximations to maximum likelihood for logistic regression, and integrable models, such as probit regression with normal measurement errors. These methods will be refined and adapted for use in studies of the etiology and prevention of breast, colon and skin cancer. A major emphasis will be placed on procedures useful for determining the appropriate sample size for epidemiologic investigations with substantial errors of measurement in risk factors.
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