Spontaneous recovery from neuromuscular block after a single dose of a muscle relaxant in pediatric patients: A systematic review using a network meta-analytic and meta-regression approach.

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.

Predicting haemoglobin deferral using machine learning models: Can we use the same prediction model across countries?

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.

A combined multilevel factor analysis and covariance regression model with mixed effects in the mean and variance structure.

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.

DMC reports in the 21st century: towards better tools for decision-making.

Data Monitoring Committees (DMCs) have the important task to protect the safety of current and future patients during the conduct of a clinical study. Unfortunately, their work is often made difficult by voluminous DMC reports that are poorly structured and difficult to digest. In this article, we suggest improved solutions. Starting from a principled approach and building upon previous proposals, we offer concrete and easily understood displays, including related computer code. While leveraging modern tools, the most important is that these displays support the DMC's workflow in answering the relevant questions of interest. We hope that the adoption of these proposals can ease the task of DMCs, and importantly, lead to better decision-making for the benefit of patients.

An international comparison of haemoglobin deferral prediction models for blood banking.

BACKGROUND AND OBJECTIVES: Blood banks use a haemoglobin (Hb) threshold before blood donation to minimize donors' risk of anaemia. Hb prediction models may guide decisions on which donors to invite, and should ideally also be generally applicable, thus in different countries and settings. In this paper, we compare the outcome of various prediction models in different settings and highlight differences and similarities. MATERIALS AND METHODS: Donation data of repeat donors from the past 5 years of Australia, Belgium, Finland, the Netherlands and South Africa were used to fit five identical prediction models: logistic regression, random forest, support vector machine, linear mixed model and dynamic linear mixed model. Only donors with five or more donation attempts were included to ensure having informative data from all donors. Analyses were performed for men and women separately and outcomes compared. RESULTS: Within countries and overall, different models perform similarly well. However, there are substantial differences in model performance between countries, and there is a positive association between the deferral rate in a country and the ability to predict donor deferral. Nonetheless, the importance of predictor variables across countries is similar and is highest for the previous Hb level. CONCLUSION: The limited impact of model architecture and country indicates that all models show similar relationships between the predictor variables and donor deferral. Donor deferral is found to be better predictable in countries with high deferral rates. Therefore, such countries may benefit more from deferral prediction models than those with low deferral rates.

Incorporating historical controls in clinical trials with longitudinal outcomes using the modified power prior.

Several dynamic borrowing methods, such as the modified power prior (MPP), the commensurate prior, have been proposed to increase statistical power and reduce the required sample size in clinical trials where comparable historical controls are available. Most methods have focused on cross-sectional endpoints, and appropriate methodology for longitudinal outcomes is lacking. In this study, we extend the MPP to the linear mixed model (LMM). An important question is whether the MPP should use the conditional version of the LMM (given the random effects) or the marginal version (averaged over the distribution of the random effects), which we refer to as the conditional MPP and the marginal MPP, respectively. We evaluated the MPP for one historical control arm via a simulation study and an analysis of the data of Alzheimer's Disease Cooperative Study (ADCS) with the commensurate prior as the comparator. The conditional MPP led to inflated type I error rate when there existed moderate or high between-study heterogeneity. The marginal MPP and the commensurate prior yielded a power gain (3.6%-10.4% vs. 0.6%-4.6%) with the type I error rates close to 5% (5.2%-6.2% vs. 3.8%-6.2%) when the between-study heterogeneity is not excessively high. For the ADCS data, all the borrowing methods improved the precision of estimates and provided the same clinical conclusions. The marginal MPP and the commensurate prior are useful for borrowing historical controls in longitudinal data analysis, while the conditional MPP is not recommended due to inflated type I error rates.

The power prior with multiple historical controls for the linear regression model.

Combining historical control data with current control data may reduce the necessary study size of a clinical trial. However, this only applies when the historical control data are similar enough to the current control data. Several Bayesian approaches for incorporating historical data in a dynamic way have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP). Here we discuss the generalization of the MPP approach for multiple historical control groups for the linear regression model. This approach is useful when the controls differ more than in a random way, but become again (approximately) exchangeable conditional on covariates. The proposed approach builds on the approach previously developed for binary outcomes by some of the current authors. Two MPP approaches have been developed with multiple controls. The first approach assumes independent powers, while in the second approach the powers have a hierarchical structure. We conducted several simulation studies to investigate the frequentist characteristics of borrowing methods and analyze a real-life data set. When there is between-study variation in the slopes of the model or in the covariate distributions, the MPP approach achieves approximately nominal type I error rates and greater power than the MAP prior, provided that the covariates are included in the model. When the intercepts vary, the MPP yields a slightly inflated type I error rate, whereas the MAP does not. We conclude that our approach is a worthy competitor to the MAP approach for the linear regression case.

Invariance of the WHO violence against women instrument among Kenyan adolescent girls and young women: Bayesian psychometric modeling.

INTRODUCTION: To make valid comparisons across groups, a measurement instrument needs to be measurement invariant across those groups. The present study evaluates measurement invariance for experience of violence among adolescent girls and young women (AGYW) in two informal settlements in Nairobi, Kenya. METHODS: We used survey data collected from 1,081 AGYW aged 15-22 years from two Nairobi's informal settlements of Korogocho (n = 617) and Viwandani (n = 464) in 2017 through DREAMS (an initiative aimed at reducing HIV incidence among AGYW with a core package of evidence-based interventions) impact evaluation project. Experience of violence was measured using the 15-item WHO's violence against women instrument, and factorial (non)invariance assessed within exploratory structural equation modeling (ESEM) framework. Cross-group measurement invariance was assessed using Bayesian Multiple Indicator Multiple Causes (MIMIC) model across site, age groups, self-reported invitation to participate in DREAMS, marital status, currently in school, education level, religion, ethnic groups, ever had sex, slept hungry at night past 4 weeks, and wealth index. RESULTS: The mean and median ages of the AGYW were 17.9 years and 17 years, respectively. About 59% reported having had sex and 58% of AGYW were in school. The percentage reporting each act of violence varied from 1.6% ("attacked you with a weapon") to 26.5% ("insult you or make you feel bad about yourself"). About 44% (n = 474) of participants experienced ≥1 acts of violence, and 2.7% (n = 29) experienced at least half of the 15 acts. The structure underlying the 15 items was configurally similar to that proposed by WHO, with three factors reflecting either psychological, physical, or sexual violence. Noninvariance was detected for five items-spread across the three domains. Three of five items showed noninvariance only for sleeping hungry at night in the past 4 weeks. As the majority of items did not show evidence of noninvariance, differences in latent mean scores likely reflect actual differences and may not be attributable to measurement artifacts. CONCLUSIONS: Using state-of-the-art statistical techniques on a widely used instrument for measuring exposure to violence among women, this study provides support for the subscales of psychological, physical and sexual violence in a Kenyan AGYW population. The instrument supports comparisons across groups within this population. This is crucial when comparing violence against girls/women prevalence rates and to understand challenges and exchange strategies to reduce abuse or violence experienced by AGYW, or women in general.

Nonlinear mixed-effects modeling of longitudinal count data: Bayesian inference about median counts based on the marginal zero-inflated discrete Weibull distribution.

This article proposes a Bayesian regression model for nonlinear zero-inflated longitudinal count data that models the median count as an alternative to the mean count. The nonlinear model generalizes a recently introduced linear mixed-effects model based on the zero-inflated discrete Weibull (ZIDW) distribution. The ZIDW distribution is more robust to severe skewness in the data than conventional zero-inflated count distributions such as the zero-inflated negative binomial (ZINB) distribution. Moreover, the ZIDW distribution is attractive because of its convenience to model the median counts given its closed-form quantile function. The median is a more robust measure of central tendency than the mean when the data, for instance, zero-inflated counts, are right-skewed. In an application of the model we consider a biphasic mixed-effects model consisting of an intercept term and two slope terms. Conventionally, the ZIDW model separately specifies the predictors for the zero-inflation probability and the counting process's median count. In our application, the two latent class interpretations are not clinically plausible. Therefore, we propose a marginal ZIDW model that directly models the biphasic median counts marginally. We also consider the marginal ZINB model to make inferences about the nonlinear mean counts over time. Our simulation study shows that the models have good properties in terms of accuracy and confidence interval coverage.

Identifying influential observations in a Bayesian multi-level mediation model.

Increasingly complex models are being fit to data these days. This is especially the case for Bayesian modelling making use of Markov chain Monte Carlo methods. Tailored model diagnostics are usually lacking behind. This is also the case for Bayesian mediation models. In this paper, we developed a method for the detection of influential observations for a popular mediation model and its extensions in a Bayesian context. Detection of influential observations is based on the case-deletion principle. Importance sampling with weights which take advantage of the dependence structure in hierarchical models is utilized in order to identify the part of the model which is influenced most. We make use of the variance of log importance sampling weights as the measure of influence. It is demonstrated that this approach is useful when interest lies in the impact of individual observations on a subset of model parameters. The method is illustrated on a three-level data set from the field of nursing research, which was previously used to fit a mediation model of patient satisfaction with care. We focused on influential cases on both the second and the third level of the data.

Modeling local dependence in latent vector autoregressive models.

We propose a Bayesian latent vector autoregressive (LVAR) model to analyze multivariate longitudinal data of binary and ordinal variables (items) as a function of a small number of continuous latent variables. We focus on the evolution of the latent variables while taking into account the correlation structure of the responses. Often local independence is assumed in this context. Local independence implies that, given the latent variables, the responses are assumed mutually independent cross-sectionally and longitudinally. However, in practice conditioning on the latent variables may not remove the dependence of the responses. We address local dependence by further conditioning on item-specific random effects. A simulation study shows that wrongly assuming local independence may give biased estimates for the regression coefficients of the LVAR process as well as the item-specific parameters. Novel features of our proposal include (i) correcting biased estimates of the model parameters, especially the regression coefficients of the LVAR process, obtained when local dependence is ignored and (ii) measuring the magnitude of local dependence. We applied our model on data obtained from a registry on the elderly population in Belgium. The purpose was to examine the values of oral health information on top of general health information.

Serial correlation structures in latent linear mixed models for analysis of multivariate longitudinal ordinal responses.

We propose a latent linear mixed model to analyze multivariate longitudinal data of multiple ordinal variables, which are manifestations of fewer continuous latent variables. We focus on the latent level where the effects of observed covariates on the latent variables are of interest. We incorporate serial correlation into the variance component rather than assuming independent residuals. We show that misleading inference may be drawn when misspecifying the variance component. Furthermore, we provide a graphical tool depicting latent empirical semi-variograms to detect serial correlation for latent stationary linear mixed models. We apply our proposed model to examine the treatment effect on patients having the amyotrophic lateral sclerosis disease. The result shows that the treatment can slow down progression of latent cervical and lumbar functions.

Latent Ornstein-Uhlenbeck models for Bayesian analysis of multivariate longitudinal categorical responses.

We propose a Bayesian latent Ornstein-Uhlenbeck (OU) model to analyze unbalanced longitudinal data of binary and ordinal variables, which are manifestations of fewer continuous latent variables. We focus on the evolution of such latent variables when they continuously change over time. Existing approaches are limited to data collected at regular time intervals. Our proposal makes use of an OU process for the latent variables to overcome this limitation. We show that assuming real eigenvalues for the drift matrix of the OU process, as is frequently done in practice, can lead to biased estimates and/or misleading inference when the true process is oscillating. In contrast, our proposal allows for both real and complex eigenvalues. We illustrate our proposed model with a motivating dataset, containing patients with amyotrophic lateral sclerosis disease. We were interested in how bulbar, cervical, and lumbar functions evolve over time.

Assessment of oral health in older adults by non-dental professional caregivers-development and validation of a photograph-supported oral health-related section for the interRAI suite of instruments.

OBJECTIVES: An optimized oral health-related section and a video training were developed and validated for the interRAI suite of instruments. The latter is completed by professional non-dental caregivers and used in more than 40 countries to assess care needs of older adults. METHODS: The optimized oral health-related section (ohr-interRAI) consists of nine items and a video training that were developed in consecutive phases. To evaluate psychometric properties, a study was conducted in 260 long-term care residents. Each resident was assessed by a dentist and by four caregivers (two who received the video training, two who did not). RESULTS: Mean kappa values and percent agreement between caregivers and dentist ranged between κ = 0.60 (80.2%) for dry mouth and κ = 0.13 (54.0%) for oral hygiene. The highest inter-caregiver agreement was found for dry mouth with κ = 0.63 [95% CI: 0.56-0.70] (81.6%), while for the item palate/lips/cheeks only κ = 0.27 [95% CI: 0.18-0.36] (76.7%) was achieved. Intra-caregiver agreement ranged between κ = 0.93 [95% CI: 0.79-1.00] (96.4%) for dry mouth and κ = 0.45 [95% CI: 0.06-0.84] (82.8%) for gums. Logistic regression analysis showed only small differences between caregivers who watched the video training and those who did not. CONCLUSIONS: Psychometric properties of the optimized ohr-interRAI section were improved compared to previous versions. Nevertheless, particularly the items based on inspection of the mouth require further refinement and caregiver training needs to be improved. CLINICAL RELEVANCE: Valid assessment of oral health by professional caregivers is essential due to the impaired accessibility of regular dental care for care-dependent older adults.

Summarising salient information on historical controls: A structured assessment of validity and comparability across studies.

BACKGROUND: While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions. METHODS: We discuss the original 'Pocock' criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer. RESULTS: A number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be. CONCLUSION: Historical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.

Assessment of oral health conditions presented in photographs - is there a difference between dentists and non-dental professional caregivers?

BACKGROUND: Photographs can help non-dental professional caregivers to identify problems when inspecting the mouth of care-dependent older individuals. This study evaluated whether the assessment of oral health-related conditions presented in photographs differed between dentists and non-dental professional caregivers. MATERIALS AND METHODS: One-hundred-and-seventy-nine photographs were taken from long-term care facility residents and from patients at the Department of Dentistry of a University Hospital. The following oral health aspects were depicted: denture hygiene, oral hygiene, teeth, gums, tongue and palate/lips/cheeks. Collection continued until for each oral health aspect a pool of photographs was available that showed conditions from perfect health and hygiene to severe problems. A segmented Visual Analogue Scale was applied to assess the conditions presented in the photographs. Each photograph was assessed by each participant of this study. The benchmark was established by three dentists with academic-clinical expertise in gerodontology, special needs dentistry and periodontology. For each photograph, they provided a collective score after reaching consensus. Photographs were assessed individually by 32 general dentists and by 164 non-dental professional caregivers. Linear mixed effects models and generalized linear mixed effects models were fitted and mean squared errors were computed to quantify differences between both groups. RESULTS: For the different oral health aspects, absolute distances from the benchmark scores were 1.13 (95%CI:1.03-1.23) to 1.51 (95%CI:1.39-1.65) times higher for the caregivers than for the dentists. The odds to overestimate the condition were higher for the caregivers than the dentists for oral hygiene (OR = 0.72, 95%CI = 0.62-0.84) and teeth (OR = 0.74; 95%CI = 0.61-0.88). The odds to underestimate the condition were higher for the caregivers than the dentists for gums (OR = 1.39; 95%CI:1.22-1.59) and palate/lips/cheeks (OR = 1.22; 95%CI = 1.07-1.40). Over all assessments, the variance in caregiver scores was 1.9 (95%CI:1.62-2.23) times higher than that for the dentists. CONCLUSION: Small but significant differences were found between dentists and non-dental professional caregivers assessing oral health-related conditions presented in photographs. When photographs are used to aid non-dental professional caregivers with the oral health assessment, these visualizations should be complemented with comments to facilitate accurate interpretation.

Correction: Cumulative viral load as a predictor of CD4+ T-cell response to antiretroviral therapy using Bayesian statistical models.

[This corrects the article DOI: 10.1371/journal.pone.0224723.].

Bayesian model selection in linear mixed models for longitudinal data.

Linear mixed models (LMMs) are popular to analyze repeated measurements with a Gaussian response. For longitudinal studies, the LMMs consist of a fixed part expressing the effect of covariates on the mean evolution in time and a random part expressing the variation of the individual curves around the mean curve. Selecting the appropriate fixed and random effect parts is an important modeling exercise. In a Bayesian framework, there is little agreement on the appropriate selection criteria. This paper compares the performance of the deviance information criterion (DIC), the pseudo-Bayes factor and the widely applicable information criterion (WAIC) in LMMs, with an extension to LMMs with skew-normal distributions. We focus on the comparison between the conditional criteria (given random effects) versus the marginal criteria (averaged over random effects). In spite of theoretical arguments, there is not much enthusiasm among applied statisticians to make use of the marginal criteria. We show in an extensive simulation study that the three marginal criteria are superior in choosing the appropriate longitudinal model. In addition, the marginal criteria selected most appropriate model for growth curves of Nigerian chicken. A self-written R function can be combined with standard Bayesian software packages to obtain the marginal selection criteria.