mvLognCorrEst: an R package for sampling from multivariate lognormal distributions and estimating correlations from uncomplete correlation matrix.

BACKGROUND AND OBJECTIVE: Pharmacometrics (PMX) is a quantitative discipline which supports decision-making processes in all stages of drug development. PMX leverages Modeling and Simulations (M&S), which represents a powerful tool to characterize and predict the behavior and the effect of a drug. M&S-based methods, such as Sensitivity Analysis (SA) and Global Sensitivity Analysis (GSA), are gaining interest in PMX as they allow the evaluation of model-informed inference quality. Simulations should be correctly designed to obtain reliable results. Neglecting correlations between model parameters can significantly alter the results of simulations. However, the introduction of a correlation structure between model parameters can cause some issues. Sampling from a multivariate lognormal distribution, which is the typically distribution assumed for PMX model parameters, is not straightforward when a correlation structure is introduced. Indeed, correlations need to respect some constraints which depend by the CVs (i.e., coefficients of variation) of lognormal variables. In addition, when correlation matrices have some unspecified values, they should be properly fixed preserving the positive semi-definiteness of the correlation structure. In this paper, we present mvLognCorrEst, an R package developed to address these issues. METHODS: The proposed sampling strategy was based on reconducting the extraction from the multivariate lognormal distribution of interest to the underlying Normal distribution. However, with high lognormal CVs, a positive semi-definite Normal covariance matrix cannot be obtained due to the violation of some theoretical constraints. In these cases, the Normal covariance matrix was approximated to its nearest positive definite matrix using Frobenius norm as matrix distance. For the estimation of unknown correlations terms, the graph theory was used to represent the correlation structure as weighed undirected graph. Plausible value ranges for the unspecified correlations were derived considering the paths between variables. Then, their estimation was performed by solving a constrained optimization problem. RESULTS: Package functions are presented and applied on a real case study, that is the GSA of a PMX model that has been recently developed to support preclinical oncological studies. CONCLUSIONS: mvLognCorrEst package is an R tool to support simulation-based analysis for which sampling from multivariate lognormal distributions with correlated variables and/or estimation of partially defined correlation matrix are required.

Nurse burnout before and during the COVID-19 pandemic: a systematic comparative review.

Introduction: This review aimed to compare available evidence examining burnout using the Maslach Burnout Inventory (MBI) in nurses before and during the COVID-19 pandemic. The specific objective was to compare nurse burnout scores in terms of emotional exhaustion, depersonalization, and personal accomplishment. Methods: A comprehensive search was conducted for studies on nurses' burnout using the MBI published between 1994 and 2022. In total, 19 studies conducted prior to the pandemic and 16 studies conducted during the pandemic were included and compared using the criteria from the Joanna Briggs Institute Critical Appraisal Tool. Results: Surprisingly, the results indicated that nurses' burnout scores did not differ significantly before (N = 59,111) and during (N = 18,629) the pandemic. The difference observed was qualitative rather than quantitative. Discussion: The outbreak of the COVID-19 pandemic exacerbated an already critical situation, and while COVID-19 may serve as an additional triggering factor for staff mental illness, it cannot solely explain the observed burnout levels. These findings underscore the need for long-term clinical and preventive psychological interventions, suggesting that psychological resources should not be limited to emergencies but extended to address the ongoing challenges faced by nurses. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=399628, identifier: CRD42023399628.