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Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R -squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R -squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R -squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model.

In unrestricted or exploratory factor analysis (EFA), there is a wide range of recommendations about the size samples should be to attain correct and stable solutions. In general, however, these recommendations are either rules of thumb or based on simulation results. As it is hard to establish the extent to which a particular data set suits the conditions used in a simulation study, the advice produced by simulation studies is not direct enough to be of practical use. Instead of trying to provide general and complex recommendations, in this article, we propose to estimate the sample size that is needed to analyze a data set at hand. The estimation takes into account the specified EFA model. The proposal is based on an intensive simulation process in which the sample correlation matrix is used as a basis for generating data sets from a pseudo-population in which the parent correlation holds exactly, and the criterion for determining the size required is a threshold that quantifies the closeness between the pseudo-population and the sample reproduced correlation matrices. The simulation results suggest that the proposal works well and that the determinants identified agree with those in the literature.

There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, a few limitations exist in the current DFM representatives and estimation: (1) the dynamic component contains either directed or undirected contemporaneous relations, but not both, (2) selecting the optimal model in exploratory DFM is a challenge, (3) the consequences of structural misspecifications from model selection is barely studied. Our paper serves to advance DFM with a hybrid VAR representations and the utilization of LASSO regularization to select dynamic implied instrumental variable, two-stage least squares (MIIV-2SLS) estimation. Our proposed method highlights the flexibility in modeling the directions of dynamic relations with a robust estimation. We aim to offer researchers guidance on model selection and estimation in person-centered dynamic assessments.

We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use.

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

Introduction: The ongoing conflict in Ukraine from Russian invasion presents a critical challenge to medical planning in the context of multi-domain battle against a peer adversary deploying conventional weapon systems. The potential escalation of preventable morbidity and mortality, reaching a scale unprecedented since World War II, underscores the paramount importance of effective phases of care from Point of Injury (PoI)/Point of Wounding (PoW) or Point of Exposure (PoE) to Role 1 (R1) and Role 2 (R2) echelons of care.The NATO Vigorous Warrior (VW) Live Exercise (LIVEX) serves as a strategic platform for NATO and its partners, providing an opportunity to challenge operational concepts, experiment, innovate life-saving systems, and foster best practices across the Alliance., Materials and Methods: This study delineates the strategic application of the VW LIVEX platform for the adaptation of the computational simulation software Simulation for the Assessment and Optimization of Medical Disaster Management (SIMEDIS) within the context of Large-Scale Combat Operations (LSCO). The SIMEDIS computer simulator plays a pivotal role by furnishing real-time insights into the evolving injury patterns of patients, employing an all-hazards approach. This simulator facilitates the examination of temporal shifts in medical timelines and the ramifications of resource scarcity against both morbidity and mortality outcomes. The VW LIVEX provides a unique opportunity for systematic validation to evaluate the results of the computer simulator in a realistic setting and identify gaps for future concepts of operations., Results: We report the process and methodologies to be evaluated at the VW LIVEX in far forward and retrospective medical support operations. Using the SIMEDIS simulator, we can define battlefield scenarios for varied situations including artillery, drone strikes, and Chemical, Biological, Radiological, Nuclear, and explosive (CBRNe) attacks. Casualty health progressions versus time are dependent on each threat. Mortality is computed based on the concepts found in Tactical Combat Casualty Care (TCCC) of "self-aid"/"buddy-aid" factoring in the application or absence of definitive traumatic hemorrhage control and on the distribution policy of victims to medical treatment facilities through appropriate Command and Control (C2) ("Scoop and Run" versus "Stay and Play"). The number of medical supplies available along with the number of transport resources and personnel are set and are scalable, with their effect on both morbidity and mortality quantified.Concept of Medical Operations can be optimized and interoperability enhanced when shared data are provided to C2 for prospective medical planning with retrospective data. The SIMEDIS simulator determines best practices of medical management for a myriad of injury types and tactical/operational situations relevant to policy making and battlefield medical planning for LSCO., Conclusions: The VW LIVEX provides a Concept Development and Experimentation platform for SIMEDIS refinement and conclusive insights into medical planning to reduce preventable morbidity and mortality. Recommending further iterations of similar methodologies at other NATO LIVEXs for validation is crucial, as is information sharing across the Alliance and partners to ensure best practice standards are met., (© The Association of Military Surgeons of the United States 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site–for further information please contact journals.permissions@oup.com.)

Introduction: Combat casualty care requires learning a complex set of skills to treat patients in challenging situations, including resource scarce environments, multiple casualty incidents, and care under fire. To train the skills needed to respond efficiently and appropriately to these diverse conditions, instructors employ a wide array of simulation modalities. Simulation modalities for medical training include manikins, task trainers, standardized patient actors (i.e., role players), computer or extended reality simulations (e.g., virtual reality, augmented reality), cadavers, and live tissue training. Simulation modalities differ from one another in multiple attributes (e.g., realism, availability). The purpose of this study was to compare capabilities across simulation modalities for combat casualty care from the perspective of experienced military medics., Materials and Methods: To provide a more complete understanding of the relative merits and limitations of modalities, military combat medics (N = 33) were surveyed on the capabilities of simulation modalities during a 5-day technical experimentation event where they observed medical simulations from industry developers. The survey asked them to rate each of eleven modalities on each of seven attributes. To elicit additional context for the strengths, limitations, and unique considerations of using each modality, we also collected open-ended comments to provide further insight on when and how to use specific simulation modalities., Results: Results showed differences among the simulation modalities by attribute. Cadavers, role play, moulage, and live tissue all received high ratings on two or more attributes. However, there was no modality that was rated uniformly superior to the others. Instead, modalities appear to have unique strengths and limitations depending on the training context and objectives. For example, cadavers were seen as highly realistic, but not very reusable., Conclusions: The study furthers our understanding of simulation modalities for medical training by providing insight from combat medics on the benefits, limitations, and considerations for implementing different modalities depending on the training context. These results may be helpful to instructors in selecting modalities for their programs., (© The Association of Military Surgeons of the United States 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Clinical trials powered to detect subgroup effects provide the most reliable data on heterogeneity of treatment effect among different subpopulations. However, pre-specified subgroup analysis is not always practical and post hoc analysis results should be examined cautiously. Bayesian hierarchical modelling provides grounds for defining a controlled post hoc analysis plan that is developed after seeing outcome data for the population but before unblinding the outcome by subgroup. Using simulation based on the results from a tobacco cessation clinical trial conducted among the general population, we defined an analysis plan to assess treatment effect among American Indians and Alaska Natives (AI/AN) enrolled in the study. Patients were randomized into two arms using Bayesian adaptive design. For the opt-in arm, clinicians offered a cessation treatment plan after verifying that a patient was ready to quit. For the opt-out arm, clinicians provided all participants with free cessation medications and referred them to a Quitline. The study was powered to test a hypothesis of significantly higher quit rates for the opt-out arm at one-month post randomization. Overall, one-month abstinence rates were 15.9% and 21.5% (opt-in and opt-out arm, respectively). For AI/AN, one-month abstinence rates were 10.2% and 22.0% (opt-in and opt-out arm, respectively). The posterior probability that the abstinence rate in the treatment arm is higher is 0.96, indicating that AI/AN demonstrate response to treatment at almost the same probability as the whole population.

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.

Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method "updated pseudo class draws (uPCD)" to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome ("one-step," "bias-adjusted three-step," "modal class assignment," "non-inclusive pseudo class draws," and "inclusive pseudo class draws") and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach.

Calculating confidence intervals and p -values of edges in networks is useful to decide their presence or absence and it is a natural way to quantify uncertainty. Since lasso estimation is often used to obtain edges in a network, and the underlying distribution of lasso estimates is discontinuous and has probability one at zero when the estimate is zero, obtaining p -values and confidence intervals is problematic. It is also not always desirable to use the lasso to select the edges because there are assumptions required for correct identification of network edges that may not be warranted for the data at hand. Here, we review three methods that either use a modified lasso estimate (desparsified or debiased lasso) or a method that uses the lasso for selection and then determines p -values without the lasso. We compare these three methods with popular methods to estimate Gaussian Graphical Models in simulations and conclude that the desparsified lasso and its bootstrapped version appear to be the best choices for selection and quantifying uncertainty with confidence intervals and p -values.

Objective: In-person healthcare delivery is rapidly changing with a shifting employment landscape and technological advances. Opportunities to care for patients in more efficient ways include leveraging technology and focusing on caring for patients in the right place at the right time. We aim to use computer modelling to understand the impact of interventions, such as virtual consultation, on hospital census for referring and referral centres if non-procedural patients are cared for locally rather than transferred., Patients and Methods: We created computer modelling based on 25 138 hospital transfers between June 2019 and June 2022 with patients originating at one of 17 community-based hospitals and a regional or academic referral centre receiving them. We identified patients that likely could have been cared for at a community facility, with attention to hospital internal medicine and cardiology patients. The model was run for 33 500 days., Results: Approximately 121 beds/day were occupied by transferred patients at the academic centre, and on average, approximately 17 beds/day were used for hospital internal medicine and nine beds/day for non-procedural cardiology patients. Typical census for all internal medicine beds is approximately 175 and for cardiology is approximately 70., Conclusion: Deferring transfers for patients in favour of local hospitalisation would increase the availability of beds for complex care at the referral centre. Potential downstream effects also include increased patient satisfaction due to proximity to home and viability of the local hospital system/economy, and decreased resource utilisation for transfer systems., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Propensity score (PS) analyses are increasingly popular in behavioral sciences. Two issues often add complexities to PS analyses, including missing data in observed covariates and clustered data structure. In previous research, methods for conducting PS analyses with considering either issue alone were examined. In practice, the two issues often co-occur; but the performance of methods for PS analyses in the presence of both issues has not been evaluated previously. In this study, we consider PS weighting analysis when data are clustered and observed covariates have missing values. A simulation study is conducted to evaluate the performance of different missing data handling methods (complete-case, single-level imputation, or multilevel imputation) combined with different multilevel PS weighting methods (fixed- or random-effects PS models, inverse-propensity-weighting or the clustered weighting, weighted single-level or multilevel outcome models). The results suggest that the bias in average treatment effect estimation can be reduced, by better accounting for clustering in both the missing data handling stage (such as with the multilevel imputation) and the PS analysis stage (such as with the fixed-effects PS model, clustered weighting, and weighted multilevel outcome model). A real-data example is provided for illustration.

In psychology and education, tests (e.g., reading tests) and self-reports (e.g., clinical questionnaires) generate counts, but corresponding Item Response Theory (IRT) methods are underdeveloped compared to binary data. Recent advances include the Two-Parameter Conway-Maxwell-Poisson model (2PCMPM), generalizing Rasch's Poisson Counts Model, with item-specific difficulty, discrimination, and dispersion parameters. Explaining differences in model parameters informs item construction and selection but has received little attention. We introduce two 2PCMPM-based explanatory count IRT models: The Distributional Regression Test Model for item covariates, and the Count Latent Regression Model for (categorical) person covariates. Estimation methods are provided and satisfactory statistical properties are observed in simulations. Two examples illustrate how the models help understand tests and underlying constructs.

Student evaluation of teaching (SET) questionnaires are ubiquitously applied in higher education institutions in North America for both formative and summative purposes. Data collected from SET questionnaires are usually item-level data with cross-classified structure, which are characterized by multivariate categorical outcomes (i.e., multiple Likert-type items in the questionnaires) and cross-classified structure (i.e., non-nested students and instructors). Recently, a new approach, namely the cross-classified IRT model, was proposed for appropriately handling SET data. To inform researchers in higher education, in this article, the cross-classified IRT model, along with three existing approaches applied in SET studies, including the cross-classified random effects model (CCREM), the multilevel item response theory (MLIRT) model, and a two-step integrated strategy, was reviewed. The strengths and weaknesses of each of the four approaches were also discussed. Additionally, the new and existing approaches were compared through an empirical data analysis and a preliminary simulation study. This article concluded by providing general suggestions to researchers for analyzing SET data and discussing limitations and future research directions.

Accelerated longitudinal designs allow researchers to efficiently collect longitudinal data covering a time span much longer than the study duration. One important assumption of these designs is that each cohort (a group defined by their age of entry into the study) shares the same longitudinal trajectory. Although previous research has examined the impact of violating this assumption when each cohort is defined by a single age of entry, it is possible that each cohort is instead defined by a range of ages, such as groups that experience a particular historical event. In this paper we examined how including cohort membership in linear and quadratic multilevel models performed in detecting and controlling for cohort effects in this scenario. Using a Monte Carlo simulation study, we assessed the performance of this approach under conditions related to the number of cohorts, the overlap between cohorts, the strength of the cohort effect, the number of affected parameters, and the sample size. Our results indicate that models including a proxy variable for cohort membership based on age at study entry performed comparably to using true cohort membership in detecting cohort effects accurately and returning unbiased parameter estimates. This indicates that researchers can control for cohort effects even when true cohort membership is unknown.

Network analysis has gained popularity as an approach to investigate psychological constructs. However, there are currently no guidelines for applied researchers when encountering missing values. In this simulation study, we compared the performance of a two-step EM algorithm with separated steps for missing handling and regularization, a combined direct EM algorithm, and pairwise deletion. We investigated conditions with varying network sizes, numbers of observations, missing data mechanisms, and percentages of missing values. These approaches are evaluated with regard to recovering population networks in terms of loss in the precision matrix, edge set identification and network statistics. The simulation showed adequate performance only in conditions with large samples ( n ≥ 500 ) or small networks ( p  = 10). Comparing the missing data approaches, the direct EM appears to be more sensitive and superior in nearly all chosen conditions. The two-step EM yields better results when the ratio of n/p is very large - being less sensitive but more specific. Pairwise deletion failed to converge across numerous conditions and yielded inferior results overall. Overall, direct EM is recommended in most cases, as it is able to mitigate the impact of missing data quite well, while modifications to two-step EM could improve its performance.

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.

Importance: Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies., Objective: To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques., Design, Setting, and Participants: Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack., Exposures: Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms., Main Outcomes and Measures: Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups., Results: The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied., Conclusions and Relevance: In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

Mass-forming ductal carcinoma in situ (DCIS) detected on core needle biopsy (CNB) is often a radiology-pathology discordance and thought to represent missed invasive carcinoma. This brief report applied supervised machine learning (ML) for image segmentation to investigate a series of 44 mass-forming DCIS cases, with the primary focus being stromal computational signatures. The area under the curve (AUC) for receiver operator curves (ROC) in relation to upgrade to invasive carcinoma from DCIS were as follows: high myxoid stromal ratio (MSR): 0.923, P = <0.001; low collagenous stromal percentage (CSP): 0.875, P = <0.001; and low proportionated stromal area (PSA): 0.682, P = 0.039. The use of ML in mass-forming DCIS could predict upgraded to invasive carcinoma with high sensitivity and specificity. The findings from this brief report are clinically useful and should be further validated by future studies., (Copyright © 2022 Elsevier GmbH. All rights reserved.)

Background: Simulation-based training is a clinical skill learning method that can replicate real-life situations in an interactive manner. In our study, we compared a novel hybrid learning method with conventional simulation learning in the teaching of endotracheal intubation., Methods: One hundred medical students and residents were randomly divided into two groups and were taught endotracheal intubation. The first group of subjects (control group) studied in the conventional way via lectures and classic simulation-based training sessions. The second group (experimental group) used the hybrid learning method where the teaching process consisted of distance learning and small group peer-to-peer simulation training sessions with remote supervision by the instructors. After the teaching process, endotracheal intubation (ETI) procedures were performed on real patients under the supervision of an anesthesiologist in an operating theater. Each step of the procedure was evaluated by a standardized assessment form (checklist) for both groups., Results: Thirty-four subjects constituted the control group and 43 were in the experimental group. The hybrid group (88%) showed significantly better ETI performance in the operating theater compared with the control group (52%). Further, all hybrid group subjects (100%) followed the correct sequence of actions, while in the control group only 32% followed proper sequencing., Conclusions: We conclude that our novel algorithm-driven hybrid simulation learning method improves acquisition of endotracheal intubation with a high degree of acceptability and satisfaction by the learners' as compared with classic simulation-based training., (© 2022. The Author(s).)

Background & Aims: Several models have recently been developed to predict risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Our aims were to develop and validate an artificial intelligence-assisted prediction model of HCC risk., Methods: Using a gradient-boosting machine (GBM) algorithm, a model was developed using 6,051 patients with CHB who received entecavir or tenofovir therapy from 4 hospitals in Korea. Two external validation cohorts were independently established: Korean (5,817 patients from 14 Korean centers) and Caucasian (1,640 from 11 Western centers) PAGE-B cohorts. The primary outcome was HCC development., Results: In the derivation cohort and the 2 validation cohorts, cirrhosis was present in 26.9%-50.2% of patients at baseline. A model using 10 parameters at baseline was derived and showed good predictive performance (c-index 0.79). This model showed significantly better discrimination than previous models (PAGE-B, modified PAGE-B, REACH-B, and CU-HCC) in both the Korean (c-index 0.79 vs. 0.64-0.74; all p <0.001) and Caucasian validation cohorts (c-index 0.81 vs. 0.57-0.79; all p <0.05 except modified PAGE-B, p = 0.42). A calibration plot showed a satisfactory calibration function. When the patients were grouped into 4 risk groups, the minimal-risk group (11.2% of the Korean cohort and 8.8% of the Caucasian cohort) had a less than 0.5% risk of HCC during 8 years of follow-up., Conclusions: This GBM-based model provides the best predictive power for HCC risk in Korean and Caucasian patients with CHB treated with entecavir or tenofovir., Lay Summary: Risk scores have been developed to predict the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B. We developed and validated a new risk prediction model using machine learning algorithms in 13,508 antiviral-treated patients with chronic hepatitis B. Our new model, based on 10 common baseline characteristics, demonstrated superior performance in risk stratification compared with previous risk scores. This model also identified a group of patients at minimal risk of developing HCC, who could be indicated for less intensive HCC surveillance., Competing Interests: Conflict of interest Hwi Young Kim: Nothing to declare; Pietro Lampertico: Speaking bureau/advisor for AbbVie, Eiger, Bristol-Myers Squibb, Gilead, GlaxoSmithKline, Merck/Merck Sharp & Dohme, MYR Pharma, Roche; Joon Yeul Nam: Nothing to declare; Hyung-Chul Lee: Nothing to declare; Seung Up Kim: Receiving grants from Yuhan Pharmaceuticals, and lecture fees from Bristol-Myers Squibb, Gilead Science and Yuhan Pharmaceuticals; Dong Hyun Sinn: Nothing to declare; Yeon Seok Seo: Nothing to declare; Han Ah Lee: Nothing to declare; Soo Young Park: Nothing to declare; Young-Suk Lim: Advisory board member of and receives research funding from Gilead Sciences; Eun Sun Jang: Nothing to declare; Eileen L. Yoon: Nothing to declare; Hyoung Su Kim: Nothing to declare; Sung Eun Kim: Nothing to declare; Sang Bong Ahn: Nothing to declare; Jae-Jun Shim: Nothing to declare; Soung Won Jeong: Nothing to declare; Yong Jin Jung: Nothing to declare; Joo Hyun Sohn: Nothing to declare; Yong Kyun Cho: Nothing to declare; Dae Won Jun: Nothing to declare; George N. Dalekos: Advisor/lecturer for AbbVie, Bayer, Bristol-Myers Squibb, Gilead, Janssen, Novartis, Roche; grant support from Bristol-Myers Squibb, Gilead, Roche; Ramazan Idilman: Nothing to declare; Vana Sypsa: Advisor/lecturer for AbbVie, Gilead, Janssen; research grants from AbbVie, Gilead; Thomas Berg: Advisor/consultant/lecturer for AbbVie, Alexion, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Gilead, GlaxoSmithKline, Janssen, Merck Sharp & Dohme/Merck, Novartis, Roche, and Vertex; Research support from AbbVie, Bristol-Myers Squibb, Gilead, Janssen, Merck Sharp & Dohme/Merck, Novartis and Roche; Maria Buti: Advisor/lecturer for AbbVie, Arbutus, Bristol-Myers Squibb, Gilead, Glaxo Smith-Kleine, Merck, Roche, Spring Bank; Jose Luis Calleja: Advisor/lecturer for AbbVie, Bristol-Myers Squibb, Gilead, Janssen, Merck; John Goulis: advisor/lecturer for Bristol-Myers Squibb, Gilead, GlaxoSmithKline, Merck Sharp & Dohme, Roche; research grant from Bristol-Myers Squibb; Spilios Manolakopoulos: Advisor/lecturer for AbbVie, Bristol-Myers Squibb, Gilead, GlaxoSmithKline, Merck Sharp & Dohme, Novartis, Roche; grants from Bristol-Myers Squibb, Gilead; Harry LA Janssen: Consultant for and grants from AbbVie, Arbutus, Bristol Myers Squibb, Enyo, Gilead Sciences, Janssen, Medimmune, Merck, Roche, Vir Biotechnology Inc., Viroclinics; Myoung-jin Jang: Nothing to declare; Yun Bin Lee: Research grant from Samjin Pharmaceuticals and Yuhan Pharmaceuticals; Yoon Jun Kim: Research grants from Bristol-Myers Squibb, Roche, JW Creagene, Bukwang Pharmaceuticals, Handok Pharmaceuticals, Hanmi Pharmaceuticals, Yuhan Pharmaceuticals and Pharmaking, and lecture fees from Bayer HealthCare Pharmaceuticals, Gilead Science, MSD Korea, Yuhan Pharmaceuticals, Samil Pharmaceuticals, CJ Pharmaceuticals, Bukwang Pharmaceuticals and Handok Pharmaceuticals; Jung-eHwan Yoon: Research grant from Bayer HealthCare Pharmaceuticals, Bukwang Pharmaceuticals and Daewoong Pharmaceuticals; Jeong-Hoon Lee: Lecture fees from GreenCross Cell, Daewoong Pharmaceuticals and Gilead Korea; George V. Papatheodoridis: Advisor/lecturer for AbbVie, Dicerna, Gilead, GlaxoSmithKline, Ipsen, Janssen, Merck Sharp & Dohme, Roche, Spring Bank; research grants AbbVie, Gilead. Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2021 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

In Canadian boreal forests, bryophytes represent an essential component of biodiversity and play a significant role in ecosystem functioning. Despite their ecological importance and sensitivity to disturbances, bryophytes are overlooked in conservation strategies due to knowledge gaps on their distribution, which is known as the Wallacean shortfall. Rare species deserve priority attention in conservation as they are at a high risk of extinction. This study aims to elaborate predictive models of rare bryophyte species in Canadian boreal forests using remote sensing-derived predictors in an Ensemble of Small Models (ESMs) framework. We hypothesize that high ESMs-based prediction accuracy can be achieved for rare bryophyte species despite their low number of occurrences. We also assess if there is a spatial correspondence between rare and overall bryophyte richness patterns. The study area is located in western Quebec and covers 72,292 km2. We selected 52 bryophyte species with <30 occurrences from a presence-only database (214 species, 389 plots in total). ESMs were built from Random Forest and Maxent techniques using remote sensing-derived predictors related to topography and vegetation. Lee's L statistic was used to assess and map the spatial relationship between rare and overall bryophyte richness patterns. ESMs yielded poor to excellent prediction accuracy (AUC > 0.5) for 73% of the modeled species, with AUC values > 0.8 for 19 species, which confirmed our hypothesis. In fact, ESMs provided better predictions for the rarest bryophytes. Likewise, our study revealed a spatial concordance between rare and overall bryophyte richness patterns in different regions of the study area, which have important implications for conservation planning. This study demonstrates the potential of remote sensing for assessing and making predictions on inconspicuous and rare species across the landscape and lays the basis for the eventual inclusion of bryophytes into sustainable development planning., Competing Interests: The authors have declared that no competing interests exist.

Clinical positron emission tomography (PET) research is costly and entails exposing participants to radioactivity. Researchers should therefore aim to include just the number of subjects needed to fulfill the purpose of the study. In this tutorial we show how to apply sequential Bayes Factor testing in order to stop the recruitment of subjects in a clinical PET study as soon as enough data have been collected to make a conclusion. By using simulations, we demonstrate that it is possible to stop a study early, while keeping the number of erroneous conclusions low. We then apply sequential Bayes Factor testing to a real PET data set and show that it is possible to obtain support in favor of an effect while simultaneously reducing the sample size with 30%. Using this procedure allows researchers to reduce expense and radioactivity exposure for a range of effect sizes relevant for PET research.

Checklists can improve adherence to standardized procedures and minimize human error. We aimed to test if implementation of a checklist was feasible and effective in enhancing patient care in an emergency department handling internal medicine cases. We developed four critical event checklists and confronted volunteer teams with a series of four simulated emergency scenarios. In two scenarios, the teams were provided access to the crisis checklists in a randomized cross-over design. Simulated patient outcome plus statement of the underlying diagnosis defined the primary endpoint and adherence to key processes such as time to commence CPR represented the secondary endpoints. A questionnaire was used to capture participants' perception of clinical relevance and manageability of the checklists. Six teams of four volunteers completed a total of 24 crisis sequences. The primary endpoint was reached in 8 out of 12 sequences with and in 2 out of 12 sequences without a checklist (Odds ratio, 10; CI 1.11, 123.43; p = 0.03607, Fisher's exact test). Adherence to critical steps was significantly higher in all scenarios for which a checklist was available (performance score of 56.3% without checklist, 81.9% with checklist, p = 0.00284, linear regression model). All participants rated the checklist as useful and 22 of 24 participants would use the checklist in real life. Checklist use had no influence on CPR quality. The use of context-specific checklists showed a statistically significant influence on team performance and simulated patient outcome and contributed to adherence to standard clinical practices in emergency situations., (© 2021. The Author(s).)

Context: Simulation-based education (SBE) has demonstrated its acceptability and effectiveness in improving ultrasound training. Because of the high cost of its implementation (investment in equipment and supervision), a pragmatic assessment of the transfer of skills learned in SBE to clinical practice and the identification of its optimal scheduling conditions have been requested to optimize its input., Objectives: To quantify the long-term impact of simulation-based education (SBE) on the adequate performance of ultrasound fetal biometry measurements (I). The secondary objective was to identify the temporal patterns that enhanced SBE input in learning (II)., Methods: Trainees were arbitrarily assigned to a 6-month course in obstetric ultrasound with or without an SBE workshop. In the SBE group, the workshop was implemented 'before' or at an 'early' or a 'late-stage' of the course. Those who did not receive SBE were the control group. The ultrasound skills of all trainees were prospectively collected, evaluated by calculating the delta between OSAUS (Objective Structured Assessment of Ultrasound Skills) scores before and after the course (I). Concomitantly, the accuracy of trainees' measurements was assessed throughout the course by verifying their correlation with the corresponding measurements by their supervisors. The percentage of trainees able to perform five consecutive sets of correct measurements in the control group and in each SBE subgroup were compared (II)., Results: The study included 61 trainees (39 SBE and 22 controls). Comparisons between groups showed no significant difference in the quantitative assessment of skill enhancement (difference in the pre- and post-internship OSAUS score: 1.09 ± 0.87 in the SBE group and 0.72 ± 0.98 in the control group) (I). Conversely, the predefined acceptable skill level was reached by a significantly higher proportion of trainees in the 'early' SBE subgroup (74%, compared with 30% in the control group, P<0.01)(II)., Conclusions: The quantitative assessment does not support the existence of long-term benefits from SBE training, although the qualitative assessment confirmed SBE helped to raise the minimal level within a group when embedded in an 'early' stage of a practical course., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

Importance: The possibility of widespread use of a novel effective therapy for Alzheimer disease (AD) will present important clinical, policy, and financial challenges., Objective: To describe how including different patient, caregiver, and societal treatment-related factors affects estimates of the cost-effectiveness of a hypothetical disease-modifying AD treatment., Design, Setting, and Participants: In this economic evaluation, the Alzheimer Disease Archimedes Condition Event Simulator was used to simulate the prognosis of a hypothetical cohort of patients selected from the Alzheimer Disease Neuroimaging Initiative database who received the diagnosis of mild cognitive impairment (MCI). Scenario analyses that varied costs and quality of life inputs relevant to patients and caregivers were conducted. The analysis was designed and conducted from June 15, 2019, to September 30, 2020., Exposures: A hypothetical drug that would delay progression to dementia in individuals with MCI compared with usual care., Main Outcomes and Measures: Incremental cost-effectiveness ratio (ICER), measured by cost per quality-adjusted life-year (QALY) gained., Results: The model included a simulated cohort of patients who scored between 24 and 30 on the Mini-Mental State Examination and had a global Clinical Dementia Rating scale of 0.5, with a required memory box score of 0.5 or higher, at baseline. Using a health care sector perspective, which included only individual patient health care costs, the ICER for the hypothetical treatment was $192 000 per QALY gained. The result decreased to $183 000 per QALY gained in a traditional societal perspective analysis with the inclusion of patient non-health care costs. The inclusion of estimated caregiver health care costs produced almost no change in the ICER, but the inclusion of QALYs gained by caregivers led to a substantial reduction in the ICER for the hypothetical treatment, to $107 000 per QALY gained in the health sector perspective. In the societal perspective scenario, with the broadest inclusion of patient and caregiver factors, the ICER decreased to $74 000 per added QALY., Conclusions and Relevance: The findings of this economic evaluation suggest that policy makers should be aware that efforts to estimate and include the effects of AD treatments outside those on patients themselves can affect the results of the cost-effectiveness analyses that often underpin assessments of the value of new treatments. Further research and debate on including these factors in assessments that will inform discussions on fair pricing for new treatments are needed.

Objectives: There is limited knowledge about students' experiences with virtual simulation when using a video conferencing system. Therefore, the aim of this study was to explore how second-year undergraduate nursing students experienced learning through virtual simulations during the COVID-19 pandemic., Methods: The study had an exploratory design with both quantitative and qualitative approaches. In total, 69 nursing students participated in two sessions of virtual simulation during spring 2020, and 33 students answered online questionnaires at session 1. To further explore students' experiences, one focus group interview and one individual interview were conducted using a video conferencing system after session 2. In addition, system information on use during both sessions was collected., Results: Changes in the students' ratings of their experiences of virtual simulation with the Body Interact ™ system were statistically significant. The virtual simulation helped them to bridge gaps in both the teaching and learning processes. Four important aspects of learning were identified: 1) learning by self-training, 2) learning from the software ( Body Interact ™), 3) learning from peers, and 4) learning from faculty., Conclusions: We conclude that virtual simulation through a video conferencing system can be useful for student learning and feedback from both peers and faculty is important., (© 2021 Walter de Gruyter GmbH, Berlin/Boston.)

Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning., (Copyright © 2021. Published by Elsevier Masson SAS.)

Estimating parameters accurately in groundwater models for aquifers is challenging because the models are non-explicit solutions of complex partial differential equations. Modern research methods, such as Monte Carlo methods and metaheuristic algorithms, for searching an efficient design to estimate model parameters require hundreds, if not thousands of model calls, making the computational cost prohibitive. One method to circumvent the problem and gain valuable insight on the behavior of groundwater is to first apply a Galerkin method and convert the system of partial differential equations governing the flow to a discrete problem and then use a Proper Orthogonal Decomposition to project the high-dimensional model space of the original groundwater model to create a reduced groundwater model with much lower dimensions. The reduced model can be solved several orders of magnitude faster than the full model and able to provide an accurate estimate of the full model. The task is still challenging because the optimization problem is non-convex, non-differentiable and there are continuous variables and integer-valued variables to optimize. Following convention, heuristic algorithms and a combination is used search to find efficient designs for the reduced groundwater model using various optimality criteria. The main goals are to introduce new design criteria and the concept of design efficiency for experimental design research in hydrology. The two criteria have good utility but interestingly, do not seem to have been implemented in hydrology. In addition, design efficiency is introduced. Design efficiency is a method to assess how robust a design is under a change of criteria. The latter is an important issue because the design criterion may be subjectively selected and it is well known that an optimal design can perform poorly under another criterion. It is thus desirable that the implemented design has relatively high efficiencies under a few criteria. As applications, two heuristic algorithms are used to find optimal designs for a small synthetic aquifer design problem and a design problem for a large-scale groundwater model and assess their robustness properties to other optimality criteria. The results show the proof of concept is workable for finding a more informed and efficient model-based design for a water resource study., Competing Interests: The authors have declared that no competing interests exist.

As the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate [Formula: see text] is not significantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, [Formula: see text]) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, [Formula: see text]) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with [Formula: see text] hovering around its threshold value.

Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pK a were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pK a values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method., (© 2021. The Author(s).)

Introduction: Sample size "rules-of-thumb" for external validation of clinical prediction models suggest at least 100 events and 100 non-events. Such blanket guidance is imprecise, and not specific to the model or validation setting. We investigate factors affecting precision of model performance estimates upon external validation, and propose a more tailored sample size approach., Methods: Simulation of logistic regression prediction models to investigate factors associated with precision of performance estimates. Then, explanation and illustration of a simulation-based approach to calculate the minimum sample size required to precisely estimate a model's calibration, discrimination and clinical utility., Results: Precision is affected by the model's linear predictor (LP) distribution, in addition to number of events and total sample size. Sample sizes of 100 (or even 200) events and non-events can give imprecise estimates, especially for calibration. The simulation-based calculation accounts for the LP distribution and (mis)calibration in the validation sample. Application identifies 2430 required participants (531 events) for external validation of a deep vein thrombosis diagnostic model., Conclusion: Where researchers can anticipate the distribution of the model's LP (eg, based on development sample, or a pilot study), a simulation-based approach for calculating sample size for external validation offers more flexibility and reliability than rules-of-thumb., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pK a for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pK a challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pK a challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pK a values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pK a prediction methods., (© 2021. The Author(s).)

The beneficial effects of coffee on human diseases are well documented, but the molecular mechanisms of its bioactive compounds on cancer are not completely elucidated. This is likely due to the large heterogeneity of coffee preparations and different coffee-based beverages, but also to the choice of experimental models where proliferation, differentiation and immune responses are differently affected. The aim of the present study was to investigate the effects of one of the most interesting bioactive compounds in coffee, i.e., caffeine, using a cellular model of melanoma at a defined differentiation level. A preliminary in silico analysis carried out on public gene-expression databases identified genes potentially involved in caffeine's effects and suggested some specific molecular targets, including tyrosinase. Proliferation was investigated in vitro on human melanoma initiating cells (MICs) and cytokine expression was measured in conditioned media. Tyrosinase was revealed as a key player in caffeine's mechanisms of action, suggesting a crucial role in immunomodulation through the reduction in IL-1β, IP-10, MIP-1α, MIP-1β and RANTES secretion onto MICs conditioned media. The potent antiproliferative effects of caffeine on MICs are likely to occur by promoting melanin production and reducing inflammatory signals' secretion. These data suggest tyrosinase as a key player mediating the effects of caffeine on melanoma.

Background: Simulation training has become a key part of the surgical curriculum over recent years. Current trainees face significantly reduced operating time as a result of the coronavirus disease 2019 pandemic, alongside increased costs to surgical training, thus creating a need for low-cost simulation models., Methods: A systematic review of the literature was performed using multiple databases. Each model included was assessed for the ease and expense of its construction, as well as its validity and educational value., Results: A total of 18 low-cost simulation models were identified, relating to otology, head and neck surgery, laryngeal surgery, rhinology, and tonsil surgery. In only four of these models (22.2 per cent) was an attempt made to demonstrate the educational impact of the model. Validation was rarely formally assessed., Conclusion: More efforts are required to standardise validation methods and demonstrate the educational value of the available low-cost simulation models in otorhinolaryngology.

Researchers often convert prediction tools built on statistical regression models into integer scores and risk classification systems in the name of simplicity. However, this workflow discards useful information and reduces prediction accuracy. We, therefore, investigated the impact on prediction accuracy when researchers simplify a regression model into an integer score using a simulation study and an example clinical data set. Simulated independent training and test sets (n = 1000) were randomly generated such that a logistic regression model would perform at a specified target area under the receiver operating characteristic curve (AUC) of 0.7, 0.8, or 0.9. After fitting a logistic regression with continuous covariates to each data set, continuous variables were dichotomized using data-dependent cut points. A logistic regression was refit, and the coefficients were scaled and rounded to create an integer score. A risk classification system was built by stratifying integer scores into low-, intermediate-, and high-risk tertiles. Discrimination and calibration were assessed by calculating the AUC and index of prediction accuracy (IPA) for each model. The optimism in performance between the training set and test set was calculated for both AUC and IPA. The logistic regression model using the continuous form of covariates outperformed all other models. In the simulation study, converting the logistic regression model to an integer score and subsequent risk classification system incurred an average decrease of 0.057-0.094 in AUC, and an absolute 6.2%-17.5% in IPA. The largest decrease in both AUC and IPA occurred in the dichotomization step. The dichotomization and risk stratification steps also increased the optimism of the resulting models, such that they appeared to be able to predict better than they actually would on new data. In the clinical data set, converting the logistic regression with continuous covariates to an integer score incurred a decrease in externally validated AUC of 0.06 and a decrease in externally validated IPA of 13%. Converting a regression model to an integer score decreases model performance considerably. Therefore, we recommend developing a regression model that incorporates all available information to make the most accurate predictions possible, and using the unaltered regression model when making predictions for individual patients. In all cases, researchers should be mindful that they correctly validate the specific model that is intended for clinical use., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 International Anesthesia Research Society.)

The receiver operating characteristic (ROC) curve is commonly used to evaluate the accuracy of a diagnostic test for classifying observations into two groups. We propose two novel tuning parameters for estimating the ROC curve via Bernstein polynomial smoothing of the empirical ROC curve. The new estimator is very easy to implement with the naturally selected tuning parameter, as illustrated by analyzing both real and simulated data sets. Empirical performance is investigated through extensive simulation studies with a variety of scenarios where the two groups are both from a single family of distributions (symmetric or right skewed) or one from a symmetric and the other from a right skewed distribution. The new estimator is uniformly more efficient than the empirical ROC estimator, and very competitive to eleven other existing smooth ROC estimators in terms of mean integrated square errors., Competing Interests: The authors have declared that no competing interests exist.

Background: COVID-19 outbreaks have occurred in homeless shelters across the US, highlighting an urgent need to identify the most effective infection control strategy to prevent future outbreaks., Methods: We developed a microsimulation model of SARS-CoV-2 transmission in a homeless shelter and calibrated it to data from cross-sectional polymerase chain reaction (PCR) surveys conducted during COVID-19 outbreaks in five homeless shelters in three US cities from March 28 to April 10, 2020. We estimated the probability of averting a COVID-19 outbreak when an exposed individual is introduced into a representative homeless shelter of 250 residents and 50 staff over 30 days under different infection control strategies, including daily symptom-based screening, twice-weekly PCR testing, and universal mask wearing., Results: The proportion of PCR-positive residents and staff at the shelters with observed outbreaks ranged from 2.6 to 51.6%, which translated to the basic reproduction number (R 0 ) estimates of 2.9-6.2. With moderate community incidence (~ 30 confirmed cases/1,000,000 people/day), the estimated probabilities of averting an outbreak in a low-risk (R 0 = 1.5), moderate-risk (R 0 = 2.9), and high-risk (R 0 = 6.2) shelter were respectively 0.35, 0.13, and 0.04 for daily symptom-based screening; 0.53, 0.20, and 0.09 for twice-weekly PCR testing; 0.62, 0.27, and 0.08 for universal masking; and 0.74, 0.42, and 0.19 for these strategies in combination. The probability of averting an outbreak diminished with higher transmissibility (R 0 ) within the simulated shelter and increasing incidence in the local community., Conclusions: In high-risk homeless shelter environments and locations with high community incidence of COVID-19, even intensive infection control strategies (incorporating daily symptom screening, frequent PCR testing, and universal mask wearing) are unlikely to prevent outbreaks, suggesting a need for non-congregate housing arrangements for people experiencing homelessness. In lower-risk environments, combined interventions should be employed to reduce outbreak risk.

Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate's arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7 ± 5.45%) than non-inducible models (11.07 ± 3.61%; p<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (p=0.90), meaning that the intrinsic pro-arrhythmic substrate properties of fibrosis in ESUS and AFib are indistinguishable. This suggests that some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers., Competing Interests: SB, GS, SS, ZB, CR, SN, CM, PK, MM, DT, WL, NA, PB No competing interests declared, (© 2021, Bifulco et al.)

Background: Cattle fever ticks (CFT), Rhipicephalus (Boophilus) annulatus and R. (B.) microplus, are vectors of microbes causing bovine babesiosis and pose a threat to the economic viability of the US livestock industry. Efforts by the Cattle Fever Tick Eradication Program (CFTEP) along the US-Mexico border in south Texas are complicated by the involvement of alternate hosts, including white-tailed deer (Odocoileus virginianus) and nilgai (Boselaphus tragocamelus)., Methods: In the present study, we use a spatially explicit, individual-based model to explore the potential effects of host species composition and host habitat use patterns on southern cattle fever ticks (SCFT, R. (B.) microplus) infestation dynamics and efficacy of eradication schemes., Results: In simulations without eradication efforts, mean off-host larval densities were much higher when cattle were present than when only white-tailed deer and nilgai were present. Densities in mesquite and meadows were slightly higher, and densities in mixed brush were much lower, than landscape-level densities in each of these scenarios. In eradication simulations, reductions in mean off-host larval densities at the landscape level were much smaller when acaricide was applied to cattle only, or to cattle and white-tailed deer, than when applied to cattle and nilgai. Relative density reductions in mesquite, mixed brush, and meadows depended on host habitat use preferences. Shifting nilgai habitat use preferences increasingly toward mixed brush and away from mesquite did not change mean off-host larval tick densities noticeably at the landscape level. However, mean densities were increased markedly in mesquite and decreased markedly in mixed brush, while no noticeable change in density was observed in meadows., Conclusions: Our results suggest that continued integration of field data into spatially explicit, individual-based models will facilitate the development of novel eradication strategies and will allow near-real-time infestation forecasts as an aid in anticipating and preventing wildlife-mediated impacts on SCFT eradication efforts.

Missing or erroneous information is a common problem in the analysis of pharmacokinetic (PK) data. This may present as missing or inaccurate dose level or dose time, drug concentrations below the analytical limit of quantification, missing sample times, or missing or incorrect covariate information. Several methods to handle problematic data have been evaluated, although no single, broad set of recommendations for commonly occurring errors has been published. In this tutorial, we review the existing literature and present the results of our simulation studies that evaluated common methods to handle known data errors to bridge the remaining gaps and expand on the existing knowledge. This tutorial is intended for any scientist analyzing a PK data set with missing or apparently erroneous data. The approaches described herein may also be useful for the analysis of nonclinical PK data., (© 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Clinical trial simulation (CTS) is a valuable tool in drug development. To obtain realistic scenarios, the subjects included in the CTS must be representative of the target population. Common ways of generating virtual subjects are based upon bootstrap (BS) procedures or multivariate normal distributions (MVNDs). Here, we investigated the performance of an alternative method based on conditional distributions (CDs). Covariate data from a hypertension drug development program were used. The methods were evaluated based on the original data set (internal evaluation) and on their ability to reproduce an older, unobserved population (extrapolation). Similar results were obtained in the internal evaluation for summary statistics, yet BS was able to preserve the correlation structure of the empirical distribution, which was not adequately reproduced by MVND; CD was in between BS and MVND. BS does not allow to extrapolate to an unobserved population. When the data set used to inform the extrapolation was well approximated by an MVND, the results from CD and MVND were comparable. However, improved extrapolation performance was observed for CD when deviations from normality assumptions occurred. If CTS is used to simulate within the observed distribution, BS is the preferred method. When extrapolating to new populations, a parametric method like CD/MVND is needed. In case the empirical multivariate distribution is characterized by linearly related covariates and unimodal marginal distributions, MVND can be used because of the simpler statistical framework and well-established use; however, if uncertainty about the MVND assumptions exists, CD will increase the confidence in the simulations compared to MVND., (© 2021 Pharmethues AB. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Objectives: This study outlines the development of a new method (split component synthesis; SCS) for meta-analysis of diagnostic accuracy studies and assesses its performance against the commonly used bivariate random effects model., Methods: The SCS method summarizes the study-specific diagnostic odds ratio (on the ln(DOR) scale), which mainly reflects test discrimination rather than threshold effects, and then splits the summary ln(DOR) into its component parts, logit sensitivity (Se) and logit specificity (Sp). Performance of SCS estimator was assessed through simulation and compared against the bivariate random effects model estimator in terms of bias, mean squared error (MSE), and coverage probability across varying degrees of between-studies heterogeneity., Results: The SCS estimator for the DOR, Se, and Sp was less biased and had smaller MSE than the bivariate model estimator. Despite the wider width of the 95% confidence intervals under the bivariate model, the latter had a poorer coverage probability than that under the SCS method., Conclusion: The SCS estimator outperforms the bivariate model estimator and thus represents an improvement in the approach to diagnostic meta-analyses. The SCS method is available to researchers through the diagma module in Stata and the SCSmeta function in R., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Abstract: The Gas Man simulation software provides an opportunity to teach, understand and examine the pharmacokinetics of volatile anesthetics. The primary aim of this study was to investigate the accuracy of a cardiac output and alveolar ventilation matched Gas Man model and to compare its predictive performance with the standard pharmacokinetic model using patient data.Therefore, patient data from volatile anesthesia were successively compared to simulated administration of desflurane and sevoflurane for the standard and a parameter-matched simulation model with modified alveolar ventilation and cardiac output. We calculated the root-mean-square deviation (RMSD) between measured and calculated induction, maintenance and elimination and the expiratory decrement times during emergence and recovery for the standard and the parameter-matched model.During induction, RMSDs for the standard Gas Man simulation model were higher than for the parameter-matched Gas Man simulation model [induction (desflurane), standard: 1.8 (0.4) % Atm, parameter-matched: 0.9 (0.5) % Atm., P = .001; induction (sevoflurane), standard: 1.2 (0.9) % Atm, parameter-matched: 0.4 (0.4) % Atm, P = .029]. During elimination, RMSDs for the standard Gas Man simulation model were higher than for the parameter-matched Gas Man simulation model [elimination (desflurane), standard: 0.7 (0.6) % Atm, parameter-matched: 0.2 (0.2) % Atm, P = .001; elimination (sevoflurane), standard: 0.7 (0.5) % Atm, parameter-matched: 0.2 (0.2) % Atm, P = .008]. The RMSDs during the maintenance of anesthesia and the expiratory decrement times during emergence and recovery showed no significant differences between the patient and simulated data for both simulation models.Gas Man simulation software predicts expiratory concentrations of desflurane and sevoflurane in humans with good accuracy, especially when compared to models for intravenous anesthetics. Enhancing the standard model by ventilation and hemodynamic input variables increases the predictive performance of the simulation model. In most patients and clinical scenarios, the predictive performance of the standard Gas Man simulation model will be high enough to estimate pharmacokinetics of desflurane and sevoflurane with appropriate accuracy., Competing Interests: Stefan Schumann has a consulting contract with Gründler GmbH Freudenstadt, Germany (no relationship to this study). James H. Philip received a speaking honorarium and travel expenses from AbbVie in 2016. None of the other authors has any conflict of interest., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Temporal dynamics in attention bias (AB) have gained increasing attention in recent years. It has been proposed that AB is variable over trials within a single test session of the dot-probe task, and that the variability in AB is more predictive of psychopathology than the traditional mean AB score. More important, one of the dynamics indices has shown better reliability than the traditional mean AB score. However, it has been also suggested that the dynamics indices are unable to uncouple random measurement error from true variability in AB, which questions the estimation precision of the dynamics indices. To clarify and overcome this issue, the current article introduces a state-space modeling (SSM) approach to estimate trial-level AB more accurately by filtering random measurement error. The estimation error of the extant dynamics indices versus SSM were evaluated by computer simulations with different parameter settings for the temporal variability and between-person variance in AB. Throughout the simulations, SSM showed robustly lower estimation error than the extant dynamics indices. We also applied these indices to real data sets, which revealed that the dynamics indices overestimate within-person variability relative to SSM. Here SSM indicated less temporal dynamics in AB than previously proposed. These findings suggest that SSM might be a better alternative to estimate trial level AB than the extant dynamics indices. However, it is still unclear whether AB has meaningful in-session variability that is predictive of psychopathology. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Background : The U.S. opioid epidemic has caused substantial harm for over 20 years. Policy interventions have had limited impact and sometimes backfired. Experts recommend a systems modeling approach to address the complexities of opioid policymaking. Objectives : Develop a system dynamics simulation model that reflects the complexities and can anticipate intended and unintended intervention effects. Methods : The model was developed from literature review and data gathering. Its outputs, starting in 1990, were compared against 12 historical time series. Four illustrative interventions were simulated for 2020-2030: reducing prescription dosage by 20%, cutting diversion by 30%, increasing addiction treatment from 45% to 65%, and increasing lay naloxone use from 4% to 20%. Sensitivity testing was performed to determine effects of uncertainties. No human subjects were studied. Results : The model fits historical data well with error percentage averaging 9% across 201 data points. Interventions to reduce dosage and diversion reduce the number of persons with opioid use disorder (PWOUD) by 11% and 16%, respectively, but each of these interventions reduces overdoses by only 1%. Boosting treatment reduces overdoses by 3% but increases PWOUD by 1%. Expanding naloxone reduces overdose deaths by 12% but increases PWOUD by 2% and overdoses by 3%. Combining all four interventions reduces PWOUD by 24%, overdoses by 4%, and deaths by 18%. Uncertainties may affect these numerical results, but policy findings are unchanged. Conclusion : No single intervention significantly reduces both PWOUD and overdose deaths, but a combination strategy can do so. Entering the 2020s, only protective measures like naloxone expansion could significantly reduce overdose deaths.