A multitude of dietary factors from dietary fat to macro and micronutrients intakes have been associated with breast cancer, yet data are still equivocal. Therefore, utilizing data from the large, multi-year, cross-sectional National Health and Nutrition Examination Survey (NHANES), we applied a novel, modern statistical shrinkage technique, logistic least absolute shrinkage and selection operator (LASSO) regression, to examine the association between dietary intakes in women, ≥50 years, with self-reported breast cancer (n = 286) compared with women without self-reported breast cancer (1144) from the 1999–2010 NHANES cycle. Logistic LASSO regression was used to examine the relationship between twenty-nine variables, including dietary variables from food, as well as well-established/known breast cancer risk factors, and to subsequently identify the most relevant variables associated with self-reported breast cancer. We observed that as the penalty factor (λ) increased in the logistic LASSO regression, well-established breast cancer risk factors, including age (β = 0.83) and parity (β = −0.05) remained in the model. For dietary macro and micronutrient intakes, only vitamin B12 (β = 0.07) was positively associated with self-reported breast cancer. Caffeine (β = −0.01) and alcohol (β = 0.03) use also continued to remain in the model. These data suggest that a diet high in vitamin B12, as well as alcohol use may be associated with self-reported breast cancer. Nonetheless, additional prospective studies should apply more recent statistical techniques to dietary data and cancer outcomes to replicate and confirm the present findings.

No abstract

No abstract

SUMMARY Modeling and inference for survival analysis problems typically revolves around different functions related to the survival distribution. Here, we focus on the mean residual life (MRL) function, which provides the expected remaining lifetime given that a subject has survived (i.e. is event-free) up to a particular time. This function is of direct interest in reliability, medical, and actuarial fields. In addition to its practical interpretation, the MRL function characterizes the survival distribution. We develop general Bayesian nonparametric inference for MRL functions built from a Dirichlet process mixture model for the associated survival distribution. The resulting model for the MRL function admits a representation as a mixture of the kernel MRL functions with time-dependent mixture weights. This model structure allows for a wide range of shapes for the MRL function. Particular emphasis is placed on the selection of the mixture kernel, taken to be a gamma distribution, to obtain desirable properties for the MRL function arising from the mixture model. The inference method is illustrated with a data set of two experimental groups and a data set involving right censoring. The supplementary material available at Biostatistics online provides further results on empirical performance of the model, using simulated data examples.