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Background: Age‐related differences in the pharmacokinetics and pharmacodynamics of neuromuscular blocking agents (NMBAs) and the short duration of many surgical procedures put pediatric patients at risk of postoperative residual curarization (PORC). To date, the duration of neuromuscular blocking agent effect in children has not been analyzed in a quantitative review. The current meta‐analysis aimed to compare spontaneous recovery following administration of various types and doses of neuromuscular blocking agents and to quantify the effect of prognostic variables associated with the recovery time in pediatric patients. Method: We searched for randomized controlled trials (RCTs) and controlled clinical trials (CCTs) that compared the time to 25% T1 (t25), from 25% to 75% T1 (RI25–75), and to ≥90% train‐of‐four (tTOF90) neuromuscular recovery between common neuromuscular blocking agent treatments administered as a single bolus to healthy pediatric participants. We compared spontaneous t25, RI25‐75, and tTOF90 between (1) neuromuscular blocking agent treatments and (2) age groups receiving a given neuromuscular blocking agent intervention and anesthesia technique. Bayesian random‐effects network and pairwise meta‐analyses along with meta‐regression were used to evaluate the results. Results: We used data from 71 randomized controlled trials/controlled clinical trials including 4319 participants. Network meta‐analysis allowed for the juxtaposition and ranking of spontaneous t25, RI25‐75, and tTOF90 following common neuromuscular blocking agent interventions. For all neuromuscular blocking agents a log‐linear relationship between dose and duration of action was found. With the neuromuscular blocking agent treatments studied, the average tTOF90 (mean[CrI95]) in children (>2–11 y) was 41.96 [14.35, 69.50] and 17.06 [5.99, 28.30] min shorter than in neonates (<28 d) and infants (28 d–12 M), respectively. We found a negative log‐linear correlation between age and duration of neuromuscular blocking agent effect. The difference in the tTOF90 (mean[CrI95]) between children and other age groups increased by 21.66 [8.82, 34.53] min with the use of aminosteroid neuromuscular blocking agents and by 24.73 [7.92, 41.43] min with the addition of sevoflurane/isoflurane for anesthesia maintenance. Conclusions: The times to neuromuscular recovery are highly variable. These can decrease significantly with age and are prolonged when volatile anesthetics are administered. This variability, combined with the short duration of many pediatric surgical procedures, makes quantitative neuromuscular monitoring mandatory even after a single dose of neuromuscular blocking agent. [ABSTRACT FROM AUTHOR]

Background and Objectives: Personalized donation strategies based on haemoglobin (Hb) prediction models may reduce Hb deferrals and hence costs of donation, meanwhile improving commitment of donors. We previously found that prediction models perform better in validation data with a high Hb deferral rate. We therefore investigate how Hb deferral prediction models perform when exchanged with other blood establishments. Materials and Methods: Donation data from the past 5 years from random samples of 10,000 donors from Australia, Belgium, Finland, the Netherlands and South Africa were used to fit random forest models for Hb deferral prediction. Trained models were exchanged between blood establishments. Model performance was evaluated using the area under the precision–recall curve (AUPR). Variable importance was assessed using SHapley Additive exPlanations (SHAP) values. Results: Across the validation datasets and exchanged models, the AUPR ranged from 0.05 to 0.43. Exchanged models performed similarly within validation datasets, irrespective of the origin of the training data. Apart from subtle differences, the importance of most predictor variables was similar in all trained models. Conclusion: Our results suggest that Hb deferral prediction models trained in different blood establishments perform similarly within different validation datasets, regardless of the deferral rate of their training data. Models learn similar associations in different blood establishments. [ABSTRACT FROM AUTHOR]

Bayesian methods to infer model dimensionality in factor analysis generally assume a lower triangular structure for the factor loadings matrix. Consequently, the ordering of the outcomes influences the results. Therefore, we propose a method to infer model dimensionality without imposing any prior restriction on the loadings matrix. Our approach considers a relatively large number of factors and includes auxiliary multiplicative parameters, which may render null the unnecessary columns in the loadings matrix. The underlying dimensionality is then inferred based on the number of nonnull columns in the factor loadings matrix, and the model parameters are estimated with a postprocessing scheme. The advantages of the method in selecting the correct dimensionality are illustrated via simulations and using real data sets. [ABSTRACT FROM AUTHOR]

We examine the performance of the power prior and the meta-analytic-predictive (MAP) prior for the analysis of (overdispersed) count data when multiple historical control data are incorporated into the analysis of the current data. To this end, we explore the Poisson and the negative binomial distribution. We propose a computational approach based on path sampling for the calculation of the scaling constant of the Modified Power Prior (MPP). We illustrate the methods through a motivating example of a clinical trial evaluating the effect of an experimental treatment to reduce the number of incontinence events for patients with an overactive bladder. Furthermore, we assess the performance of these methods via a simulation study in case of heterogeneity of the control arms. For similar current and historical control arms, the MPP approach offers greater statistical power than the MAP prior approach. When the means are different across the control arms, the MPP yields a slightly inflated Type I error rate, whereas the MAP prior does not. When the dispersion parameters are different across the control arms, the results are reversed. In conclusion, the MPP approach outperforms the MAP prior approach for count data. [ABSTRACT FROM AUTHOR]

There is no literature on outlier‐robust parametric mixed‐effects quantile regression models for continuous proportion data as an alternative to systematically identifying and eliminating outliers. To fill this gap, we formulate a robust method by extending the recently proposed fixed‐effects quantile regression model based on the heavy‐tailed Johnson‐t$$ t $$ distribution for continuous proportion data to the mixed‐effects modeling context, using a Bayesian approach. Our proposed method is motivated by and used to model the extreme quantiles of the vitality of cushion plants to provide insights into the ecology of the system in which the plants are dominant. We conducted a simulation study to assess the new method's performance and robustness to outliers. We show that the new model has good accuracy and confidence interval coverage properties and is remarkably robust to outliers. In contrast, our study demonstrates that the current approach in the literature for modeling hierarchically structured bounded data's quantiles is susceptible to outliers, especially when modeling the extreme quantiles. We conclude that the proposed model is an appropriate robust alternative to the current approach for modeling the quantiles of correlated continuous proportions when outliers are present in the data. [ABSTRACT FROM AUTHOR]

Li et al developed a multilevel covariance regression (MCR) model as an extension of the covariance regression model of Hoff and Niu. This model assumes a hierarchical structure for the mean and the covariance matrix. Here, we propose the combined multilevel factor analysis and covariance regression model in a Bayesian framework, simultaneously modeling the MCR model and a multilevel factor analysis (MFA) model. The proposed model replaces the responses in the MCR part with the factor scores coming from an MFA model. Via a simulation study and the analysis of real data, we show that the proposed model is quite efficient when the responses of the MCR model are not measured directly but are latent variables such as the patient experience measurements in our motivating dataset. [ABSTRACT FROM AUTHOR]