A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types

Eastern versus Western Control Beliefs at Work: An Investigation of Secondary Control, Socioinstrumental Control, and Work Locus of Control in China and the US

Research and theory concerning beliefs (locus of control) and perceptions of control suggest that Asians tend to be lower and more passive than Americans, but this work has been conducted mainly with US‐developed constructs and scales that assess primary control (i.e. changing the environment to adapt to the self). An international research team expanded the notion of control beliefs by developing scales to assess secondary control beliefs (i.e. adapting the self to the environment) and the new construct of socioinstrumental control beliefs (i.e. control via interpersonal relationships), both of which were thought to better fit the control beliefs of collectivist cultures than Western‐developed control scales. We expected that, when culturally appropriate scales were employed, Americans would not show higher control beliefs than Asians. Hypotheses were partially confirmed that Americans would be lower than Chinese (Hong Kong and PR China) on these new scales. It is suggested that views of Asians as passive avoiders of control at work may be incorrect and due to the overlooking of socioinstrumental control