Implementing machine learning methods with complex survey data: Lessons learned on the impacts of accounting sampling weights in gradient boosting

Despite the prominent use of complex survey data and the growing popularity of machine learning methods in epidemiologic research, few machine learning software implementations offer options for handling complex samples. A major challenge impeding the broader incorporation of machine learning into epidemiologic research is incomplete guidance for analyzing complex survey data, including the importance of sampling weights for valid prediction in target populations. Using data from 15, 820 participants in the 1988-1994 National Health and Nutrition Examination Survey cohort, we determined whether ignoring weights in gradient boosting models of all-cause mortality affected prediction, as measured by the F1 score and corresponding 95% confidence intervals. In simulations, we additionally assessed the impact of sample size, weight variability, predictor strength, and model dimensionality. In the National Health and Nutrition Examination Survey data, unweighted model performance was inflated compared to the weighted model (F1 score 81.9% [95% confidence interval: 81.2%, 82.7%] vs 77.4% [95% confidence interval: 76.1%, 78.6%]). However, the error was mitigated if the F1 score was subsequently recalculated with observed outcomes from the weighted dataset (F1: 77.0%; 95% confidence interval: 75.7%, 78.4%). In simulations, this finding held in the largest sample size (N = 10,000) under all analytic conditions assessed. For sample sizes <5,000, sampling weights had little impact in simulations that more closely resembled a simple random sample (low weight variability) or in models with strong predictors, but findings were inconsistent under other analytic scenarios. Failing to account for sampling weights in gradient boosting models may limit generalizability for data from complex surveys, dependent on sample size and other analytic properties. In the absence of software for configuring weighted algorithms, post-hoc re-calculations of unweighted model performance using weighted observed outcomes may more accurately reflect model prediction in target populations than ignoring weights entirely

The role of negative emotions in the social processes of entrepreneurship: Power rituals and shame-related appeasement behaviors

This paper examines the role of negative emotions in the social processes of entrepreneurship. Drawing on a study of Russian entrepreneurs, we develop a model of the emotional effects of social interactions between entrepreneurs and state officials. We found that negative emotions were elicited by these interactions and, in turn, fueled three forms of shame-related corrective appeasement behavior (reactive, anticipatory, and sporadic), which served to corrode entrepreneurial motivation and direct attention and energy away from business growth and development

An Official American Thoracic Society Workshop Report: Obesity and Metabolism. An Emerging Frontier in Lung Health and Disease.

The world is in the midst of an unprecedented epidemic of obesity. This epidemic has changed the presentation and etiology of common diseases. For example, steatohepatitis, directly attributable to obesity, is now the most common cause of cirrhosis in the United States. Type 2 diabetes is increasingly being diagnosed in children. Pulmonary researchers and clinicians are just beginning to appreciate the impact of obesity and altered metabolism on common pulmonary diseases. Obesity has recently been identified as a major risk factor for the development of asthma and for acute respiratory distress syndrome. Obesity is associated with profound changes in pulmonary physiology, the development of pulmonary hypertension, sleep-disordered breathing, and altered susceptibility to pulmonary infection. In short, obesity is leading to dramatic changes in lung health and disease. Simultaneously, the rapidly developing field of metabolism, including mitochondrial function, is shifting the paradigms by which the pathophysiology of many pulmonary diseases is understood. Altered metabolism can lead to profound changes in both innate and adaptive immunity, as well as the function of structural cells. To address this emerging field, a 3-day meeting on obesity, metabolism, and lung disease was convened in October 2015 to discuss recent findings, foster research initiatives, and ultimately guide clinical care. The major findings arising from this meeting are reported in this document