Urinary Phytoestrogen Levels Are Associated with Female Hormonal Cancers: An Analysis of NHANES Data From 1999 to 2010.

Phytoestrogens are plant-derived compounds that are structurally similar to endogenous estrogens. Studies have shown phytoestrogens to have possible health benefits although they could also act as endocrine disruptors. This is particularly relevant for estrogen-dependent cancers since estrogens increase risk of breast, endometrial, and ovarian cancer. Using data from the National Health and Nutritional Examination Survey (NHANES), we assessed the associations between urinary phytoestrogens (daidzein, equol, o-Desmethylangolensin (O-DMA), genistein, enterodiol, enterolactone) and breast, endometrial, and ovarian cancer using multivariate logistic regression with odds ratios (ORs) and 95% confidence intervals (CIs). Cancer diagnosis and other characteristics were collected via in-person questionnaires. We found women in the highest tertile for daidzein and enterodiol had over twice the odds of having breast cancer (OR = 2.51, 95% CI 1.44-4.36 for daidzein, OR = 2.78, 95% CI 1.44-5.37 for enterodiol). In addition, women in the highest tertiles for daidzein and genistein had three to four times the odds of having endometrial cancer, respectively (OR = 3.09, 95% CI 1.01-9.49 for daidzein, OR = 4.00, 95% CI 1.38-11.59 for genistein). Overall, phytoestrogens were positively associated with breast and endometrial cancer although the associations varied by phytoestrogen type. Additional studies are needed to further inform phytoestrogens' role in disease etiology.Supplemental data for this article is available online at at https://doi.org/10.1080/01635581.2021.2020304.

Logistic LASSO Regression for Dietary Intakes and Breast Cancer.

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 (ß = 0.83) and parity (ß = -0.05) remained in the model. For dietary macro and micronutrient intakes, only vitamin B12 (ß = 0.07) was positively associated with self-reported breast cancer. Caffeine (ß = -0.01) and alcohol (ß = 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.

Nonparametric Bayesian inference for mean residual life functions in survival analysis.

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.

Bayesian nonparametric analysis of neuronal intensity rates.

We propose a flexible hierarchical Bayesian nonparametric modeling approach to compare the spiking patterns of neurons recorded under multiple experimental conditions. In particular, we showcase the application of our statistical methodology using neurons recorded from the supplementary eye field region of the brains of two macaque monkeys trained to make delayed eye movements to three different types of targets. The proposed Bayesian methodology can be used to perform either a global analysis, allowing for the construction of posterior comparative intervals over the entire experimental time window, or a pointwise analysis for comparing the spiking patterns locally, in a predetermined portion of the experimental time window. By developing our nonparametric Bayesian model we are able to analyze neuronal data from three or more conditions while avoiding the computational expenses typically associated with more traditional analysis of physiological data.