Annette Molinaro (Division of Biostatistics, School of Public Health, University of California, Berkeley) Mark van der Laan (Division of Biostatistics, School of Public Health, University of California, Berkeley) Dan Moore (Comprehensive Cancer Center, University of California, San Francisco) Karla Kerlikowske (Dept. of Medicine, University of California, San Francisco)
Abstract
Providing information about the risk of disease and clinical factors that may increase or decrease a patient's risk of disease is standard medical practice. Although case-control studies provide evidence of strong associations between diseases and risk factors, clinicians need to be able to communicate to patients the age-specific risk of disease for a given time period and risk factor. An estimate of absolute risk cannot be determined from case-control studies because cases are generally chosen from a population whose denominator is not known (necessary for calculation of risk) and where duration of follow-up is not known (necessary for calculation of incidence). This problem can sometimes be overcome by using a nested case-control design.A nested case-control study is "nested" within a cohort study such that both cases and controls are selected from within a defined cohort. A nested case-control design is more cost-efficient than a full cohort study since expensive predictor variables (genomic measures, sex hormone levels, mammographic breast density) are measured on all of the cases, but on only a sample of the cohort who did not develop the outcome of interest (the controls). In addition, this design avoids the potential biases of conventional case-control studies that draw cases and controls from different populations. Importantly, the presence or absence of the outcome of interest has been established for the entire cohort within the same time period.Here we introduce a novel method which provides locally efficient estimators which predict the absolute risk of a cohort from measures only taken on the nested case-control participants. The proposed method is evaluated using simulation studies and survival data from women with ductal carcinoma in situ, a non-invasive form of breast cancer.
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