Safety and Efficacy of Insulins in Critically Ill Patients Receiving Continuous Enteral Nutrition.

OBJECTIVE: There is a relative lack of consensus regarding the optimal management of hyperglycemia in patients receiving continuous enteral nutrition (EN), with or without a diagnosis of diabetes. METHODS: This retrospective study examined 475 patients (303 with known diabetes) hospitalized in critical care setting units in 2019 in a single center who received continuous EN. Rates of hypoglycemia, hyperglycemia, and glucose levels within the target range (70-180 mg/dL) were compared between patients with and without diabetes, and among patients treated with intermediate-acting (IA) biphasic neutral protamine Hagedorn 70/30, long-acting (LA) insulin, or rapid-acting insulin only. RESULTS: Among those with type 2 diabetes mellitus, IA and LA insulin regimens were associated with a significantly higher proportion of patient-days in the target glucose range and fewer hyperglycemic days. Level 1 (<70 mg/dL) and level 2 (<54 mg/dL) hypoglycemia occurred rarely, and there were no significant differences in level 2 hypoglycemia frequency across the different insulin regimens. CONCLUSION: Administration of IA and LA insulin can be safe and effective for those receiving insulin doses for EN-related hyperglycemia.

Statistical Mediation Analysis for Models with a Binary Mediator and a Binary Outcome: the Differences Between Causal and Traditional Mediation Analysis.

Mediation analysis is an important statistical method in prevention research, as it can be used to determine effective intervention components. Traditional mediation analysis defines direct and indirect effects in terms of linear regression coefficients. It is unclear how these traditional effects are estimated in settings with binary variables. An important recent methodological advancement in the mediation analysis literature is the development of the causal mediation analysis framework. Causal mediation analysis defines causal effects as the difference between two potential outcomes. These definitions can be applied to any mediation model to estimate natural direct and indirect effects, including models with binary variables and an exposure-mediator interaction. This paper aims to clarify the similarities and differences between the causal and traditional effect estimates for mediation models with a binary mediator and a binary outcome. Causal and traditional mediation analyses were applied to an empirical example to demonstrate these similarities and differences. Causal and traditional mediation analysis provided similar controlled direct effect estimates, but different estimates of the natural direct effects, natural indirect effects, and total effect. Traditional mediation analysis methods do not generalize well to mediation models with binary variables, while the natural effect definitions can be applied to any mediation model. Causal mediation analysis is therefore the preferred method for the analysis of mediation models with binary variables.

A Randomization Permutation Test for Single Subject Mediation.

In response to the importance of individual-level effects, the purpose of this paper is to describe the new randomization permutation (RP) test for a mediation mechanism for a single subject. We extend seminal work on permutation tests for individual-level data by proposing a test for mediation for one person. The method requires random assignment to the levels of the treatment variable at each measurement occasion, and repeated measures of the mediator and outcome from one subject. If several assumptions are met, the process by which a treatment changes an outcome can be statistically evaluated for a single subject, using the permutation mediation test method and the permutation confidence interval method for residuals. A simulation study evaluated the statistical properties of the new method suggesting that at least eight repeated measures are needed to control Type I error rates and larger sample sizes are needed for power approaching .8 even for large effects. The RP mediation test is a promising method for elucidating intraindividual processes of change that may inform personalized medicine and tailoring of process-based treatments for one subject.

Assessing Potential Outcomes Mediation in HIV Interventions.

Knowledge of causal processes through mediation analysis can help improve the effectiveness and reduce costs of public health programs, like HIV prevention and treatment interventions. Advancements in mediation using the potential outcomes framework provide a method for estimating the causal effect of interventions on outcomes via a mediating variable. The purpose of this paper is to provide practical information about mediation and the potential outcomes framework that can enhance data analysis and causal inference for intervention studies. Causal mediation effects are defined and then estimated using data from an HIV intervention randomized trial among people who inject drugs (PWID) in Ukraine. Results from a potential outcomes mediation analysis show that the intervention had a total causal effect on incident HIV infection such that participants in the experimental group were 36% less likely to become infected during the 12-month study than those in the control arm, but that neither self-efficacy nor network communication mediated this effect. Because neither putative mediator was significant, measurement and confounding issues should be investigated to rule out these mediators. Other putative mediators, such as injection frequency, route of administration, or HIV knowledge can be considered. Future research is underway to examine additional, multiple mediators explaining efficacy of the current intervention and sensitivity to confounding effects.

Statistical Evaluation of Person-Oriented Mediation Using Configural Frequency Analysis.

Mediation analysis helps explain how and why two variables are related, providing information for investigating causal processes useful for theoretical and applied research (MacKinnon 2008). Inference from mediation analysis typically applies to the population, but researchers and clinicians are often interested in making inference to individual clients or small sub-populations of people. Person-oriented approaches focus on the differences between people, or latent groups of people, to ask how individuals differ across variables. A recently proposed method allows for the analysis of person differences as part of mediation. The method from configural frequency analysis, which we call configural frequency mediation, is based on log-linear modeling of contingency tables. The complexity of configural frequency mediation and its use of a causal steps mediation method, may contribute to the lack of application and study of this promising method since its introduction in the literature a decade ago (von Eye et al. 2009, 2010) In this paper we clarify the steps used for configural frequency mediation and report the results of a large statistical simulation study evaluating the method and comparing it to the variable-oriented traditional method using logistic regression analysis. Overall, configural frequency mediation analysis tended to have excessive type I error rates but we describe an alternative approach to configural mediation analysis based on a joint significance test that had adequate performance. We also clarify the decision rules that define configural mediation analysis and develop a test for configural frequency mediation using a joint significance mediation method.

Correlation Database of 60 Cross-Disciplinary Surveys and Cognitive Tasks Assessing Self-Regulation.

Self-regulation is studied across various disciplines, including personality, social, cognitive, health, developmental, and clinical psychology; psychiatry; neuroscience; medicine; pharmacology; and economics. Widespread interest in self-regulation has led to confusion regarding both the constructs within the nomological network of self-regulation and the measures used to assess these constructs. To facilitate the integration of cross-disciplinary measures of self-regulation, we estimated product-moment and distance correlations among 60 cross-disciplinary measures of self-regulation (23 self-report surveys, 37 cognitive tasks) and measures of health and substance use based on 522 participants. The correlations showed substantial variability, though the surveys demonstrated greater convergent validity than did the cognitive tasks. Variables derived from the surveys only weakly correlated with variables derived from the cognitive tasks (M = .049, range = .000 to .271 for the absolute value of the product-moment correlation; M = .085, range = .028 to .241 for the distance correlation), thus challenging the notion that these surveys and cognitive tasks measure the same construct. We conclude by outlining several potential uses for this publicly available database of correlations.

Causal Mediation Programs in R, Mplus, SAS, SPSS, and Stata.

Mediation analysis is a methodology used to understand how and why an independent variable (X) transmits its effect to an outcome (Y) through a mediator (M). New causal mediation methods based on the potential outcomes framework and counterfactual framework are a seminal advancement for mediation analysis, because they focus on the causal basis of mediation analysis. There are several programs available to estimate causal mediation effects, but these programs differ substantially in data set up, estimation, output, and software platform. To compare these programs, an empirical example is presented, and a single mediator model with XM interaction was estimated with a continuous mediator and a continuous outcome in each program. Even though the software packages employ different estimation methods, they do provide similar causal effect estimates for mediation models with a continuous mediator and outcome. A detailed explanation of program similarities, unique features, and recommendations are discussed.