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Previous studies indicated that self-compassion can alleviate posttraumatic stress disorder (PTSD) and promote posttraumatic growth (PTG). Rumination is a cognitive process in which individuals repeatedly think about traumatic events and their consequences, which includes invasive rumination (IR) and deliberate rumination (DR). IR is a process of thoughts over traumatic event that invades the cognitive world in an undesired state, whereas DR is an individual's conscious and repeated thinking on events. IR and DR have also been shown to have effects on PTSD and PTG. Studies have examined the relationship between self-compassion, IR, deliberate rumination DR, PTSD, and PTG, respectively, but only a few studies have examined their combined roles in PTSD and PTG among adolescents who have experienced an earthquake. In addition, recent studies have shown that the positive and negative components of self-compassion have different effects on post-traumatic stress responses, but few studies have explored the mechanisms of positive and negative self-compassion on PTSD and PTG in adolescents who experience earthquakes. This study examines the mediating roles of rumination in the relationship between self-compassion, PTSD and PTG among Chinese adolescents after the Jiuzhaigou earthquake. Ten months after the Jiuzhaigou earthquake, a questionnaire survey was conducted among 621 middle school students in Jiuzhaigou county. The participants were assessed by using the following systems: Trauma Exposure Questionnaire, Self-Compassion Scale, Event-Related Rumination Inventory, PTSD Checklist for DSM-5, and Posttraumatic Growth Inventory. When we controlled for the gender, age, and trauma exposure in the direct effect model, positive self-compassion (PSC) had a negative effect on PTSD and a positive effect on PTG. Moreover, negative self-compassion (NSC) had a positive effect on PTSD, but the effect on PTG was nonsignificant. In the indirect model, PSC had an indirect and positive effect on PTSD and PTG via deliberate rumination (DR). NSC had an indirect and positive effect on PTSD and PTG via invasive rumination (IR), whereas NSC also had an indirect and negative effect on PTSD and PTG, as well as via an indirect path from IR to DR on PTSD and PTG. Findings indicated that increased PSC may reduce PTSD and be beneficial for PTG. NSC may exacerbate PTSD. Rumination plays a significant mediating role between self-compassion, PTSD, and PTG., Author(s): Aiyi Liu [sup.1] , Wenchao Wang [sup.1] , Xinchun Wu [sup.1] Author Affiliations: (1) grid.20513.35, 0000 0004 1789 9964, Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center [...]

We consider the problem of combining multiple biomarkers to improve the diagnostic accuracy of detecting a disease when only group‐tested data on the disease status are available. There are several challenges in addressing this problem, including unavailable individual disease statuses, differential misclassification depending on group size and number of diseased individuals in the group, and extensive computation due to a large number of possible combinations of multiple biomarkers. To tackle these issues, we propose a pairwise model fitting approach to estimating the distribution of the optimal linear combination of biomarkers and its diagnostic accuracy under the assumption of a multivariate normal distribution. The approach is evaluated in simulation studies and applied to data on chlamydia detection and COVID‐19 diagnosis. [ABSTRACT FROM AUTHOR]

This study aims to investigate the relationships between work and family conflicts, specifically work‐to‐family and family‐to‐work conflicts, parental depressive symptoms and coparenting conflict behaviours during the COVID‐19 pandemic. This national survey study involved the participation of 830 families in mainland China, including fathers, mothers and adolescents. Fathers and mothers provided self‐reports on their experiences of work‐to‐family conflicts, family‐to‐work conflicts and depressive symptoms. Meanwhile, adolescents reported their perceptions of coparenting conflict behaviours exhibited by both fathers and mothers. The Actor‐Partner Interdependence Mediation Model was employed to analyse these relationships. In the Actor‐Partner Interdependence Mediation Model, paternal family‐to‐work conflicts were found to be positively associated with both paternal and maternal coparenting conflict behaviours, mediated through paternal depressive symptoms. Similarly, maternal family‐to‐work conflicts were positively linked to both paternal and maternal coparenting conflict behaviours, mediated through maternal depressive symptoms. Additionally, paternal family‐to‐work conflicts exhibited a direct positive relationship with both paternal and maternal coparenting conflict behaviours. However, paternal work‐to‐family conflicts demonstrated a direct negative association with paternal coparenting conflict behaviours. This study sheds light on the complex interconnectedness between work and family conflicts, parental depressive symptoms and coparenting conflict behaviours during the COVID‐19 pandemic. The findings highlight the importance of addressing work and family conflicts in understanding and managing coparenting dynamics, particularly during challenging times such as a pandemic. Such insights can inform interventions and support systems to promote healthier coparenting relationships and family well‐being. [ABSTRACT FROM AUTHOR]

Diagnostic biomarkers play a critical role in biomedical research, particularly for the diagnosis and prediction of diseases, etc. To enhance diagnostic accuracy, extensive research about combining multiple biomarkers has been developed based on the multivariate normality, which is often not true in practice, as most biomarkers follow distributions that deviate from normality. While the likelihood ratio combination is recognized to be the optimal approach, it is complicated to calculate. To achieve a more accurate and effective combination of biomarkers, especially when these biomarkers deviate from normality, we propose using a receiver operating characteristic (ROC) curve methodology based on the optimal combination of elliptically distributed biomarkers. In this paper, we derive the ROC curve function for the elliptical likelihood ratio combination. Further, proceeding from the derived best combinations of biomarkers, we propose an efficient technique via nonparametric maximum likelihood estimate (NPMLE) to build empirical estimation. Simulation results show that the proposed elliptical combination method consistently provided better performance, demonstrating its robustness in handling various distribution types of biomarkers. We apply the proposed method to two real datasets: Autism/autism spectrum disorder (ASD) and neural tube defects (NTD). In both applications, the elliptical likelihood ratio combination improves the AUC value compared to the multivariate normal likelihood ratio combination and the best linear combination. [ABSTRACT FROM AUTHOR]

To examine cross-sectional and longitudinal associations of general parenting style and diabetes-specific parenting behaviors with depression in youth with type 1 diabetes. Participants (n = 390) completed self-report measures of depression at baseline and 2-year follow-up, general parenting style at baseline, and diabetes-specific parenting (conflict, task involvement, and collaborative involvement) at baseline and every 6 months. Logistic regression examined associations of parenting with depression at baseline and 2-year follow-up. A less authoritative parenting style, lower parent collaborative involvement, and greater diabetes-related conflict were associated with baseline depression in the model simultaneously including all parenting variables and covariates. Lower parent collaborative involvement and higher diabetes-related conflict were associated with depression at 2-year follow-up, adjusting for baseline depression and covariates. Parent task involvement was not associated with depression at either time. Findings suggest a protective role of parenting in reducing the risk of depression in youth with type 1 diabetes., Author(s): Katherine W. Dempster [sup.1] , Aiyi Liu [sup.1] , Tonja R. Nansel [sup.1] Author Affiliations: (Aff1) 0000 0000 9635 8082, grid.420089.7, Division of Intramural Population Health Research, Social and [...]

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non‐parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non‐diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real‐world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research. [ABSTRACT FROM AUTHOR]

We consider the problem of optimizing treatment allocation for statistical efficiency in randomized clinical trials. Optimal allocation has been studied previously for simple treatment effect estimators such as the sample mean difference, which are not fully efficient in the presence of baseline covariates. More efficient estimators can be obtained by incorporating covariate information, and modern machine learning methods make it increasingly feasible to approach full efficiency. Accordingly, we derive the optimal allocation ratio by maximizing the design efficiency of a randomized trial, assuming that an efficient estimator will be used for analysis. We then expand the scope of optimization by considering covariate‐dependent randomization (CDR), which has some flavor of an observational study but provides the same level of scientific rigor as a standard randomized trial. We describe treatment effect estimators that are consistent, asymptotically normal, and (nearly) efficient under CDR, and derive the optimal propensity score by maximizing the design efficiency of a CDR trial (under the assumption that an efficient estimator will be used for analysis). Our optimality results translate into optimal designs that improve upon standard practice. Real‐world examples and simulation results demonstrate that the proposed designs can produce substantial efficiency improvements in realistic settings. [ABSTRACT FROM AUTHOR]