Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks.

Obesity and its impact on health is a multifaceted phenomenon encompassing many factors, including demographics, environment, lifestyle, and psychosocial functioning. A systems science approach, investigating these many influences, is needed to capture the complexity and multidimensionality of obesity prevention to improve health. Leveraging baseline data from a unique clinical cohort comprising 333 postmenopausal overweight or obese breast cancer survivors participating in a weight-loss trial, we applied Bayesian networks, a machine learning approach, to infer interrelationships between lifestyle factors (e.g., sleep, physical activity), body mass index (BMI), and health outcomes (biomarkers and self-reported quality of life metrics). We used bootstrap resampling to assess network stability and accuracy, and Bayesian information criteria (BIC) to compare networks. Our results identified important behavioral subnetworks. BMI was the primary pathway linking behavioral factors to glucose regulation and inflammatory markers; the BMI-biomarker link was reproduced in 100% of resampled networks. Sleep quality was a hub impacting mental quality of life and physical health with > 95% resampling reproducibility. Omission of the BMI or sleep links significantly degraded the fit of the networks. Our findings suggest potential mechanistic pathways and useful intervention targets for future trials. Using our models, we can make quantitative predictions about health impacts that would result from targeted, weight loss and/or sleep improvement interventions. Importantly, this work highlights the utility of Bayesian networks in health behaviors research.

Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity.

Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns. We developed statistical methods to design imputation and variance weighting algorithms to account for missing data effects when fitting regression models. Bias and precision of each method were evaluated and compared. Our results indicated that not accounting for missing data in the analysis yielded unstable estimates in the regression analysis. Incorporating variance weights and/or subject-level imputation improved precision by >50%, compared to ignoring missing data. We recommend that these simple easy-to-implement statistical tools be used to improve analysis of accelerometer data.

Cognition, quality-of-life, and symptom clusters in breast cancer: Using Bayesian networks to elucidate complex relationships.

OBJECTIVE: Breast cancer patients frequently complain of cognitive dysfunction during chemotherapy. Patients also report experiencing a cluster of sleep problems, fatigue, and depressive symptoms during chemotherapy. We aimed to understand the complex dynamic interrelationships of depression, fatigue, and sleep to ultimately elucidate their role in cognitive performance and quality of life amongst breast cancer survivors undergoing chemotherapy treatment. METHODS: Our study sample comprised 74 newly diagnosed stage I to III breast cancer patients scheduled to receive chemotherapy. An objective neuropsychological test battery and self-reported fatigue, mood, sleep quality, and quality of life were collected at 3 time points: before the start of chemotherapy (baseline: BL), at the end of cycle 4 chemotherapy (C4), and 1 year after the start of chemotherapy (Y1). We applied novel Bayesian network methods to investigate the role of sleep/fatigue/mood on cognition and quality of life prior to, during, and after chemotherapy. RESULTS: The fitted network exhibited strong direct and indirect links between symptoms, cognitive performance, and quality of life. The only symptom directly linked to cognitive performance was C4 sleep quality; at C4, fatigue was directly linked to sleep and thus indirectly influenced cognitive performance. Mood strongly influenced concurrent quality of life at C4 and Y1. Regression estimates indicated that worse sleep quality, fatigue, and mood were negatively associated with cognitive performance or quality of life. CONCLUSIONS: The Bayesian network identified local structure (eg, fatigue-mood-QoL or sleep-cognition) and possible intervention targets (eg, a sleep intervention to reduce cognitive complaints during chemotherapy).