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.

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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.

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Gaussian process models have been used in applications ranging from machine learning to fisheries management. In the Bayesian framework, the Gaussian process is used as a prior for unknown functions, allowing the data to drive the relationship between inputs and outputs. In our research, we consider a scenario in which response and input data are available from several similar, but not necessarily identical, sources. When little information is known about one or more of the populations it may be advantageous to model all populations together. We present a hierarchical Gaussian process model with a structure that allows distinct features for each source as well as shared underlying characteristics. Key features and properties of the model are discussed and demonstrated in a number of simulation examples. The model is then applied to a data set consisting of three populations of Rotifer Brachionus calyciflorus Pallas. Specifically, we model the log growth rate of the populations using a combination of lagged population sizes. The various lag combinations are formally compared to obtain the best model inputs. We then formally compare the leading hierarchical Gaussian process model with the inferential results obtained under the independent Gaussian process model.

Background: There were an estimated 16.9 million cancer survivors in the United States (U.S.) in 2019. Since this number is projected to increase to over 22 million by 2030 and cancer survivors often face long-term challenges and late effects of treatment, it has become increasingly important to evaluate patients’ health-related quality of life in order to better understand their needs, identify disparities, and develop strategies to improve their overall well-being. Racial/ethnic differences in cancer survivorship have been previously reported, but few have evaluated quality of life among a nationally representative, population-based sample of U.S. patients. Methods: We used self-reported data from the Medical Expenditure Panel Survey (MEPS) as well as its Experience with Cancer Survivorship Supplement questionnaire from 2016-17, which collected information on patient experiences with cancer including quality of life based on the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of physical and mental health. A Global Physical Health (GPH) score using questions related to physical health, physical function, fatigue, and pain and a Global Mental Health (GMH) score using questions related to quality of life, mental health, social support, and emotional problems were calculated where the lower the score indicated the poorer the health. In addition, questions related to whether cancer had a positive impact on patients were considered as well. Multiple logistic regression models with odds ratios (ORs) and 95% confidence intervals (CIs) were used to examine the impact of race on these various quality of life/cancer experience outcomes after considering relevant confounders. Results: A total of 1608 cancer survivors (1225 non-Hispanic Whites, 165 Hispanic Whites, 176 Blacks, 42 Asians) were included. When compared to non-Hispanic Whites, only Blacks were statistically significantly more likely to have a low GPH score (OR=1.95, 95% CI 1.15-3.27) and a low GMH score (OR=1.89, 95% CI 1.24-2.89). However, Blacks and Hispanic Whites were statistically significantly more likely to report their cancer experience leading to positive things in their lives; for example, both racial groups were three times as likely to report that their cancer helped them cope better with life’s challenges relative to non-Hispanic Whites (OR=3.66, 95% CI 2.34-5.73 for Blacks, OR=2.91, 95% CI 1.68-5.03 for Hispanic Whites). Conclusions: There are important racial disparities when it comes to health-related quality of life among cancer survivors. Although Blacks were more likely to see the positive aspects of their cancer diagnosis, they still experienced poorer physical and mental health overall. Future studies should explore the factors that may be contributing to these racial disparities as they could greatly inform targeted strategies to improve the overall survivorship experience of cancer patients. Citation Format: Alice W. Lee, Valerie Poynor, JinKyu Choi. Racial disparities in health-related quality of life among cancer survivors in the United States [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A038.

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