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Antimicrobial-resistance genes (ARGs) are spread among bacteria by horizontal gene transfer, however, the effect of environmental factors on the dynamics of the ARG in water environments has not been very well understood. In this systematic review, we employed the regression tree algorithm to identify the environmental factors that facilitate/inhibit the transfer of ARGs via conjugation in planktonic/biofilm-formed bacterial cells based on the results of past relevant research. Escherichia coli strains were the most studied genus for conjugation experiments as donor/recipient in the intra-genera category. Conversely, Pseudomonas spp. Acinetobacter spp. and Salmonella spp. were studied primarily as recipients across inter-genera bacteria. The conjugation efficiency (ce) was found to be highly dependent on the incubation period. Some antibiotics, such as nitrofurantoin (at ≥0.2 µg ml−1) and kanamycin (at ≥9.5 mg l−1) as well as metallic compounds like mercury (II) chloride (HgCl2, ≥3 µmol l−1), and vanadium (III) chloride (VCl3, ≥50 µmol l−1) had enhancing effect on conjugation. The highest ce value (−0.90 log10) was achieved at 15°C–19°C, with linoleic acid concentrations <8 mg l−1, a recognized conjugation inhibitor. Identifying critical environmental factors affecting ARG dissemination in aquatic environments will accelerate strategies to control their proliferation and combat antibiotic resistance. [ABSTRACT FROM AUTHOR]

Background and Aims Mediterranean ecosystems have a high vascular plant species richness (SR) relative to their surface area. This SR, representing the balance between speciation and extinction, has been attributed to multiple mechanisms that result in both high rates of speciation and/or low rates of extinction. An abiding question is, however, what is special about Mediterranean ecosystems that enables this high SR? Apart from the long-term climatic stability of the region, SR has also been related to resource availability, the many individuals hypothesis, resource spatial heterogeneity, temporal heterogeneity and biotic feedbacks. Methods Spatial patterns of species richness were related to climatic, edaphic and biotic variables and to spatial variability within the Greater Cape Floristic Region (GCFR) of South Africa. Boosted regression tree models were used to explore the strength of relationships between SR and environmental predictors related to each hypothesized mechanism. Key Results Water availability (i.e. precipitation) was a stronger predictor of SR than potential evapotranspiration or temperature. Scarcity of nutrients was also related to SR. There was no indication that SR was related to the density of individuals and only temporal heterogeneity induced by fire was related to SR. Spatial heterogeneities of climatic, edaphic and biotic variables were strongly associated with SR. Biotic interactions remain difficult to assess, although we have some evidence for a putative role in regulating SR. Conclusions While the lack of ecosystem-resetting disturbances (e.g. glaciation) is undoubtedly a key requirement for high species accumulation, predictably, no one explanation holds the key to understanding SR. In the GCFR high SR is the product of a combination of adequate water, nutrient scarcity, spatial and temporal heterogeneity, and possibly biotic feedbacks. [ABSTRACT FROM AUTHOR]

Objectives: Determining which sites were important to differentiate polymyalgia rheumatica (PMR) from rheumatoid arthritis (RA) using 18F-fluorodeoxyglucose (FDG) positron emission tomography and computed tomography (PET-CT) is challenging. Methods: Patients with PMR or RA who were undergoing PET-CT were recruited at two mutual-aid hospitals in Japan between 2009 and 2018. Classification and regression tree (CART) analyses were performed to identify FDG uptake patterns that differentiated PMR from RA. Results: We enrolled 35 patients with PMR and 46 patients with RA. Univariate CART analysis showed that FDG uptake in the shoulder joints, spinous processes of the lumbar vertebrae, pubic symphysis, sternoclavicular joints, ischial tuberosities, greater trochanters, and hip joints differentiated PMR from RA. Multivariate CART analysis revealed that FDG uptake by at least one of the ischial tuberosities had the highest diagnostic value for distinguishing PMR from RA (sensitivity, 77.1%; specificity, 82.6%). We performed the same CART analysis to patients who had not undergone treatment (PMR, n = 28; RA, n = 9). Similar results were obtained, and sensitivity and specificity were increased (sensitivity, 89.3%; specificity, 88.8%). Conclusions: In PET-CT, FDG uptake by at least one of the ischial tuberosities best discriminates between PMR and RA. [ABSTRACT FROM AUTHOR]

Many previous studies on the changes in zooplankton communities considered only subsets of the total community. In this study, we investigated the temporal dynamics of the mesozooplankton community structure, considering all taxa (holo- and meroplankton) sampled over the last five decades at Helgoland Roads. We identified two tipping years. The first one occurred in 1983, which is consistent with previous studies conducted on copepods. The second shift, even more pronounced, took place in the mid-2000s. During the first shift, most taxa significantly increased in abundance and kept high densities until the end of the 1990s. Then, we observed in the mid-2000s a sharp decrease in community diversity and abundances of almost all taxa. One of the aims of the study was to test the robustness depending on the choice of taxa. To test this, we selected different subsets of the total zooplankton community, both randomly as well as based on functionality. We observed very similar trends over time for all groups, showing the complete community experienced the same changes. However, the timing of the tipping years depended on the organisms considered. These results highlight that the observed changes in the planktonic community are surprisingly robust and visible in most planktonic organisms. [ABSTRACT FROM AUTHOR]

Missing data are a pervasive issue in observational studies using electronic health records or patient registries. It presents unique challenges for statistical inference, especially causal inference. Inappropriately handling missing data in causal inference could potentially bias causal estimation. Besides missing data problems, observational health data structures typically have mixed‐type variables ‐ continuous and categorical covariates ‐ whose joint distribution is often too complex to be modeled by simple parametric models. The existence of missing values in covariates and outcomes makes the causal inference even more challenging, while most standard causal inference approaches assume fully observed data or start their works after imputing missing values in a separate preprocessing stage. To address these problems, we introduce a Bayesian nonparametric causal model to estimate causal effects with missing data. The proposed approach can simultaneously impute missing values, account for multiple outcomes, and estimate causal effects under the potential outcomes framework. We provide three simulation studies to show the performance of our proposed method under complicated data settings whose features are similar to our case studies. For example, Simulation Study 3 assumes the case where missing values exist in both outcomes and covariates. Two case studies were conducted applying our method to evaluate the comparative effectiveness of treatments for chronic disease management in juvenile idiopathic arthritis and cystic fibrosis. [ABSTRACT FROM AUTHOR]

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing interest in flexible Bayesian nonparametric methods for time‐to‐event data such as Bayesian additive regression trees (BART). We propose a novel approach that we call nonparametric failure time (NFT) BART in order to increase the flexibility beyond accelerated failure time (AFT) and proportional hazard models. NFT BART has three key features: (1) a BART prior for the mean function of the event time logarithm; (2) a heteroskedastic BART prior to deduce a covariate‐dependent variance function; and (3) a flexible nonparametric error distribution using Dirichlet process mixtures (DPM). Our proposed approach widens the scope of hazard shapes including nonproportional hazards, can be scaled up to large sample sizes, naturally provides estimates of uncertainty via the posterior and can be seamlessly employed for variable selection. We provide convenient, user‐friendly, computer software that is freely available as a reference implementation. Simulations demonstrate that NFT BART maintains excellent performance for survival prediction especially when AFT assumptions are violated by heteroskedasticity. We illustrate the proposed approach on a study examining predictors for mortality risk in patients undergoing hematopoietic stem cell transplant (HSCT) for blood‐borne cancer, where heteroskedasticity and nonproportional hazards are likely present. [ABSTRACT FROM AUTHOR]

Analysis of observational studies increasingly confronts the challenge of determining which of a possibly high‐dimensional set of available covariates are required to satisfy the assumption of ignorable treatment assignment for estimation of causal effects. We propose a Bayesian nonparametric approach that simultaneously (1) prioritizes inclusion of adjustment variables in accordance with existing principles of confounder selection; (2) estimates causal effects in a manner that permits complex relationships among confounders, exposures, and outcomes; and (3) provides causal estimates that account for uncertainty in the nature of confounding. The proposal relies on specification of multiple Bayesian additive regression trees models, linked together with a common prior distribution that accrues posterior selection probability to covariates on the basis of association with both the exposure and the outcome of interest. A set of extensive simulation studies demonstrates that the proposed method performs well relative to similarly‐motivated methodologies in a variety of scenarios. We deploy the method to investigate the causal effect of emissions from coal‐fired power plants on ambient air pollution concentrations, where the prospect of confounding due to local and regional meteorological factors introduces uncertainty around the confounding role of a high‐dimensional set of measured variables. Ultimately, we show that the proposed method produces more efficient and more consistent results across adjacent years than alternative methods, lending strength to the evidence of the causal relationship between SO2 emissions and ambient particulate pollution. [ABSTRACT FROM AUTHOR]

Marine fish experience shifts in their distribution due to changes in the physical and biological environments. These shifts pose challenges for fishery businesses and management in international fisheries organizations. In the western and central Pacific Ocean, changes in the spatial distribution of skipjack tuna (Katsuwonus pelamis) due to climate variability often influence fishing activities and economic benefits. This study provides an operational forecast to enhance the decision-making process for fishery managers and fishermen by informing them about fish distributions in the coming months. Monthly forecasts of skipjack tuna habitat are generated utilizing a species distribution model (boosted regression trees) in combination with real-time environmental forecasts. An alternative method (dynamic time warping) is proposed to improve monthly chlorophyll forecasts, which are crucial for enhancing skipjack tuna habitat forecasting. To assess the prediction skills of the model, a retrospective analysis was conducted, forecasting skipjack tuna distribution over 9-month periods and comparing the forecasts with observed occurrences. The results demonstrate that 67.9% of the observed skipjack tuna catches occurred within the forecasted habitats, indicating that the forecast model skillfully predicted skipjack tuna movements 9 months in advance. These monthly updated forecasts can serve as a potential tool for resource managers in developing effective management strategies and fishers in planning fishing operations sustainably and responsibly. [ABSTRACT FROM AUTHOR]

Background The Pittsburgh Performance Fatigability Index (PPFI) quantifies the percent decline in cadence using accelerometry during standardized walking tasks. Although PPFI has shown strong correlations with physical performance, the developmental sample was relatively homogenous and small, necessitating further validation. Methods Participants from the Study of Muscle, Mobility and Aging (N  = 805, age = 76.4 ± 5.0 years, 58% women, 85% White) wore an ActiGraph GT9X on the nondominant wrist during usual-paced 400 m walk. Tri-axial accelerations were analyzed to compute PPFI (higher score = greater fatigability). To evaluate construct and discriminant validity, Spearman correlations (r s) between PPFI and gait speed, Short Physical Performance Battery (SPPB), chair stand speed, leg peak power, VO2peak, perceived fatigability, and mood were examined. Sex-specific PPFI cut-points that optimally discriminated gait speed using classification and regression tree were then generated. Their discriminate power in relation to aforementioned physical performance were further evaluated. Results Median PPFI score was 1.4% (25th–75th percentile range: 0%–21.7%), higher among women than men (p  < .001). PPFI score was moderate-to-strongly correlated with gait speed (r s = −0.75), SPPB score (r s = −0.38), chair stand speed (r s = −0.36), leg peak power (r s = −0.34) and VO2peak (r s = −0.40), and less strongly with perceived fatigability (r s = 0.28–0.29), all p  < .001. PPFI score was not correlated with mood (| r s| < 0.08). Sex-specific PPFI cut-points (no performance fatigability: PPFI = 0%; mild performance fatigability: 0% < PPFI < 3.5% [women], 0% < PPFI < 5.4% [men]; moderate-to-severe performance fatigability: PPFI ≥ 3.5% [women], PPFI ≥ 5.4% [men]) discriminated physical performance (all p  < .001), adjusted for demographics and smoking status. Conclusion Our work underscores the utility of PPFI as a valid measure to quantify performance fatigability in future longitudinal epidemiologic studies and clinical/pharmaceutical trials. [ABSTRACT FROM AUTHOR]

Model selection aims to choose the most adequate model for the statistical analysis at hand. The model must be complex enough to capture the complexity of the data but should be simple enough not to overfit. In phylogenetics, the most common model selection scenario concerns selecting an adequate substitution and partition model for sequence evolution to infer a phylogenetic tree. Previously, several studies showed that substitution model under-parameterization can bias phylogenetic studies. Here, we explored the impact of substitution model over-parameterization in a Bayesian statistical framework. We performed simulations under the simplest substitution model, the Jukes–Cantor model, and compare posterior estimates of phylogenetic tree topologies and tree length under the true model to the most complex model, the |$\text{GTR}+\Gamma+\text{I}$| substitution model, including over-splitting the data into additional subsets (i.e. applying partitioned models). We explored 4 choices of prior distributions: the default substitution model priors of MrBayes, BEAST2, and RevBayes and a newly devised prior choice (Tame). Our results show that Bayesian inference of phylogeny is robust to substitution model over-parameterization and over-partitioning but only under our new prior settings. All 3 current default priors introduced biases for the estimated tree length. We conclude that substitution and partition model selection are superfluous steps in Bayesian phylogenetic inference pipelines if well-behaved prior distributions are applied and more effort should focus on more complex and biologically realistic substitution models. [ABSTRACT FROM AUTHOR]

The influence of abiotic and biotic factors on the temporal pattern of calling and breeding for many temperate anurans is well understood. However, few studies have documented patterns of reproduction in explosive-breeding tropical frogs or incorporated multiple environmental factors in their analyses, especially across multiple breeding seasons. We combine long-term natural history observations and automated data collection methods with boosted regression tree (BRT) analysis to determine the phenology and determinants of explosive breeding in the gliding treefrog, Agalychnis spurrelli. We monitored breeding for a total of 418 days across three breeding seasons and determined the relative importance of several environmental factors on the probability of calling and breeding activity. Our study population of A. spurrelli on Costa Rica's Osa Peninsula forms breeding aggregations up to 11 times per year during 1–2-day long explosive-breeding events, from late May to mid-September. Calling and breeding activity are strongly and positively related to accumulated rainfall during the previous 24 and 48–24 h before, particularly rainfall during the afternoon and evening. Day-of-year, days since breeding occurred, and lunar phase also influence reproductive activity. This study provides the first description and analysis of the phenology and factors that predict explosive breeding in A. spurrelli and illustrates the value of using automated data collection paired with BRTs for the analysis of complex ecological data. La influencia de factores abióticos y bióticos en el patrón temporal de cantos y reproducción de muchos anuros de zonas templadas es bien conocida. Sin embargo, pocos estudios han documentado patrones de reproducción en ranas tropicales, que se reproducen de manera explosiva, o han incorporado múltiples factores ambientales en sus análisis, especialmente a lo largo de múltiples temporadas de reproducción. Combinamos observaciones de historia natural a largo plazo y métodos automatizados de recolectar datos con análisis de árboles de regresión y técnicas de remuestreo ('boosted regression trees', BRT) para determinar la fenología y los determinantes de la reproducción explosiva en la rana planeadora, Agalychnis spurrelli. Monitoreamos la reproducción durante un total de 418 días a través de tres temporadas de reproducción y determinamos la importancia relativa de varios factores ambientales en la probabilidad de cantos y reproducción. Nuestra población de estudio de A. spurrelli en la Península de Osa en Costa Rica forma agregaciones reproductivas hasta 11 veces por año, los eventos de reproducción explosiva tienen 1 a 2 días de duración, desde finales de mayo hasta mediados de septiembre. Los cantos y la reproducción están fuerte y positivamente relacionados con la acumulación de lluvia en las 24 y 48 a 24 horas anteriores, particularmente durante la tarde y la noche. El día del año, los días desde la última reproducción, y la fase lunar también influyen la reproducción. Este estudio es la primera descripción y análisis de la fenología y los factores que predicen la reproducción explosiva en A. spurrelli e ilustra el valor de utilizar la recolección de datos automatizada junto con BRT para el análisis de datos ecológicos complejos. [ABSTRACT FROM AUTHOR]

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1‐type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2‐norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database. [ABSTRACT FROM AUTHOR]

This paper introduces a flexible and adaptive nonparametric method for estimating the association between multiple covariates and power spectra of multiple time series. The proposed approach uses a Bayesian sum of trees model to capture complex dependencies and interactions between covariates and the power spectrum, which are often observed in studies of biomedical time series. Local power spectra corresponding to terminal nodes within trees are estimated nonparametrically using Bayesian penalized linear splines. The trees are considered to be random and fit using a Bayesian backfitting Markov chain Monte Carlo (MCMC) algorithm that sequentially considers tree modifications via reversible‐jump MCMC techniques. For high‐dimensional covariates, a sparsity‐inducing Dirichlet hyperprior on tree splitting proportions is considered, which provides sparse estimation of covariate effects and efficient variable selection. By averaging over the posterior distribution of trees, the proposed method can recover both smooth and abrupt changes in the power spectrum across multiple covariates. Empirical performance is evaluated via simulations to demonstrate the proposed method's ability to accurately recover complex relationships and interactions. The proposed methodology is used to study gait maturation in young children by evaluating age‐related changes in power spectra of stride interval time series in the presence of other covariates. [ABSTRACT FROM AUTHOR]

Background In the absence of bacteriologic confirmation to diagnose tuberculosis (TB) in children, it is suggested that treatment should be initiated when sufficient clinical evidence of disease is available. However, it is unclear what clinical evidence is sufficient to make this decision. To identify children who would benefit from rapid initiation of TB treatment, we developed 2 clinical prediction tools. Methods We conducted a secondary analysis of a prospective intensified TB patient–finding intervention conducted in Pakistan in 2014–2016. TB disease was determined through either bacteriologic confirmation or a clinical diagnosis. We derived 2 tools: 1 uses classification and regression tree (CART) analysis to develop decision trees, while the second uses multivariable logistic regression to calculate a risk score. Results Of the 5162 and 5074 children included in the CART and prediction score, respectively, 1417 (27.5%) and 1365 (26.9%) were eligible for TB treatment. CART identified abnormal chest radiographs and family history of TB as the most important predictors (area under the receiver operating characteristic curve [AUC], 0.949). The final prediction score model included age group (0–4, 5–9, 10–14), weight <5th percentile, cough, fever, weight loss, chest radiograph suggestive of TB disease, and family history of TB; the identified best cutoff score was 9 (AUC, 0.985%). Conclusions Use of clinical evidence was sufficient to accurately identify children who would benefit from treatment initiation. Our tools performed well compared with existing algorithms, though these results need to be externally validated before operationalization. [ABSTRACT FROM AUTHOR]

Objectives: To describe the long-term clinical course of each manifestation of Behçet’s disease (BD) and clarify factors involved in oral ulcer (OU) remission using clinical information of BD patients. Methods: We retrospectively studied 155 BD patients visiting our hospital (1989–2020). We defined remission criteria for each manifestation and examined long-term clinical changes. Classification and regression trees and multivariable analyses were performed to investigate OU prognostic factors; hazard ratios were used to assign scores to prognostic factors deemed significant [OU prognosis score (OuP score)]. Risk stratification was examined by dividing the OuP scores into four stages. Results: OUs appeared earliest, with the slowest decline in prevalence observed post-BD diagnosis. OU presence was the most common factor inhibiting complete remission. Young age at OU onset, never smoker, presence of genital ulcers, positive pathergy test, no usage of tumour necrosis factor inhibitors or of immunosuppressants, and long-term non-treatment or symptomatic treatment for OUs were poor OU prognostic factors. Based on multivariable analysis, the area under the curve of the OuP score to predict OU prognosis was 0.678. Conclusions: Remission criteria for each symptom clarified that OU had the greatest impact on complete BD remission. Faster OU remission was associated with earlier OU therapeutic intervention other than symptomatic treatment. [ABSTRACT FROM AUTHOR]

Objectives: To describe the long-term clinical course of each manifestation of Behçet’s disease (BD) and clarify factors involved in oral ulcer (OU) remission using clinical information of BD patients. Methods: We retrospectively studied 155 BD patients visiting our hospital (1989–2020). We defined remission criteria for each manifestation and examined long-term clinical changes. Classification and regression trees and multivariable analyses were performed to investigate OU prognostic factors; hazard ratios were used to assign scores to prognostic factors deemed significant [OU prognosis score (OuP score)]. Risk stratification was examined by dividing the OuP scores into four stages. Results: OUs appeared earliest, with the slowest decline in prevalence observed post-BD diagnosis. OU presence was the most common factor inhibiting complete remission. Young age at OU onset, never smoker, presence of genital ulcers, positive pathergy test, no usage of tumour necrosis factor inhibitors or of immunosuppressants, and long-term non-treatment or symptomatic treatment for OUs were poor OU prognostic factors. Based on multivariable analysis, the area under the curve of the OuP score to predict OU prognosis was 0.678. Conclusions: Remission criteria for each symptom clarified that OU had the greatest impact on complete BD remission. Faster OU remission was associated with earlier OU therapeutic intervention other than symptomatic treatment. [ABSTRACT FROM AUTHOR]

We consider inference from nonrandom samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with survey inference being a special case. We propose a regularized prediction approach that predicts the outcomes in the population using a large number of auxiliary variables such that the ignorability assumption is reasonable and the Bayesian framework is straightforward for quantification of uncertainty. Besides the auxiliary variables, we also extend the approach by estimating the propensity score for a unit to be included in the sample and also including it as a predictor in the machine learning models. We find in simulation studies that the regularized predictions using soft Bayesian additive regression trees yield valid inference for the population means and coverage rates close to the nominal levels. We demonstrate the application of the proposed methods using two different real data applications, one in a survey and one in an epidemiologic study. [ABSTRACT FROM AUTHOR]

Maternal exposure to environmental chemicals during pregnancy can alter birth and children's health outcomes. Research seeks to identify critical windows, time periods when exposures can change future health outcomes, and estimate the exposure–response relationship. Existing statistical approaches focus on estimation of the association between maternal exposure to a single environmental chemical observed at high temporal resolution (e.g., weekly throughout pregnancy) and children's health outcomes. Extending to multiple chemicals observed at high temporal resolution poses a dimensionality problem and statistical methods are lacking. We propose a regression tree–based model for mixtures of exposures observed at high temporal resolution. The proposed approach uses an additive ensemble of tree pairs that defines structured main effects and interactions between time‐resolved predictors and performs variable selection to select out of the model predictors not correlated with the outcome. In simulation, we show that the tree‐based approach performs better than existing methods for a single exposure and can accurately estimate critical windows in the exposure–response relation for mixtures. We apply our method to estimate the relationship between five exposures measured weekly throughout pregnancy and birth weight in a Denver, Colorado, birth cohort. We identified critical windows during which fine particulate matter, sulfur dioxide, and temperature are negatively associated with birth weight and an interaction between fine particulate matter and temperature. Software is made available in the R package dlmtree. [ABSTRACT FROM AUTHOR]

Missing data remains a very common problem in large datasets, including survey and census data containing many ordinal responses, such as political polls and opinion surveys. Multiple imputation (MI) is usually the go-to approach for analyzing such incomplete datasets, and there are indeed several implementations of MI, including methods using generalized linear models, tree-based models, and Bayesian non-parametric models. However, there is limited research on the statistical performance of these methods for multivariate ordinal data. In this article, we perform an empirical evaluation of several MI methods, including MI by chained equations (MICE) using multinomial logistic regression models, MICE using proportional odds logistic regression models, MICE using classification and regression trees, MICE using random forest, MI using Dirichlet process (DP) mixtures of products of multinomial distributions, and MI using DP mixtures of multivariate normal distributions. We evaluate the methods using simulation studies based on ordinal variables selected from the 2018 American Community Survey. Under our simulation settings, the results suggest that MI using proportional odds logistic regression models, classification and regression trees, and DP mixtures of multinomial distributions generally outperform the other methods. In certain settings, MI using multinomial logistic regression models is able to achieve comparable performance, depending on the missing data mechanism and amount of missing data. [ABSTRACT FROM AUTHOR]

Significant progress in previous decades has led to several methodologies developed to facilitate the design of optimal antimicrobial dosing. In this review, we highlight common pharmacokinetic/pharmacodynamic (PKPD) modeling techniques and their roles in guiding rational dosing regimen design. In the early drug development phases, dose fractionation studies identify the PKPD index most closely associated with bacterial killing. Once discerned, this index is linked to clinical efficacy end points, and classification and regression tree analysis can be used to define the PKPD target goal. Monte Carlo simulations integrate PKPD and microbiological data to identify dosing strategies with a high probability of achieving the established PKPD target. Results then determine dosing regimens to investigate and/or validate the findings of randomized controlled trials. Further improvements in PKPD modeling could lead to an era of precision dosing and personalized therapeutics. [ABSTRACT FROM AUTHOR]

Ciliates represent a crucial link between phytoplankton and bacteria and mesozooplankton in pelagic food webs, but little is known about the processes influencing the dynamics of individual species. Using long-term, high-frequency observations, we compared the diversity and the temporal variability in biomass and species composition of the ciliate community in large, deep, mesotrophic Lake Constance to that of the phytoplankton and rotifer communities in the same lake. Furthermore, we used boosted regression trees to evaluate possible environmental predictors (temperature, three prey groups, four predator/competitor groups) influencing ciliate net growth. The biomass of all ciliate species showed a common, recurrent seasonal pattern, often with peaks in spring and summer. The ciliate community was more diverse than the rotifer community, exhibited highly synchronous dynamics and its species were regularly encountered during the season. The top-down control by copepods likely contributes to the ciliates' synchronized decline prior to the clear-water phase when food concentration is still high. The high temporal autocorrelation of the ciliate biomasses together with the inter-annual recurrent seasonal patterns and the low explanatory power of the environmental predictors suggest that the dynamics of individual ciliate species are strictly controlled, yet it remains difficult to determine the responsible factors. [ABSTRACT FROM AUTHOR]

Accurate and effective prediction of mutation-induced protein energy change remains a great challenge and of great interest in computational biology. However, high resource consumption and insufficient structural information of proteins severely limit the experimental techniques and structure-based prediction methods. Here, we design a structure-independent protocol to accurately and effectively predict the mutation-induced protein folding free energy change with only sequence, physicochemical and evolutionary features. The proposed clustered tree regression protocol is capable of effectively exploiting the inherent data patterns by integrating unsupervised feature clustering by K-means and supervised tree regression using XGBoost, and thus enabling fast and accurate protein predictions with different mutations, with an average Pearson correlation coefficient of 0.83 and an average root-mean-square error of 0.94kcal/mol. The proposed sequence-based method not only eliminates the dependence on protein structures, but also has potential applications in protein predictions with rare structural information. [ABSTRACT FROM AUTHOR]

In causal inference problems, one is often tasked with estimating causal effects which are analytically intractable functionals of the data‐generating mechanism. Relevant settings include estimating intention‐to‐treat effects in longitudinal problems with missing data or computing direct and indirect effects in mediation analysis. One approach to computing these effects is to use the g‐formula implemented via Monte Carlo integration; when simulation‐based methods such as the nonparametric bootstrap or Markov chain Monte Carlo are used for inference, Monte Carlo integration must be nested within an already computationally intensive algorithm. We develop a widely‐applicable approach to accelerating this Monte Carlo integration step which greatly reduces the computational burden of existing g‐computation algorithms. We refer to our method as accelerated g‐computation (AGC). The algorithms we present are similar in spirit to multiple imputation, but require removing within‐imputation variance from the standard error rather than adding it. We illustrate the use of AGC on a mediation analysis problem using a beta regression model and in a longitudinal clinical trial subject to nonignorable missingness using a Bayesian additive regression trees model. [ABSTRACT FROM AUTHOR]

Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a robust semiparametric model for clustered interval‐censored survival data under a paradigm of Bayesian ensemble learning, called soft Bayesian additive regression trees or SBART (Linero and Yang, 2018), which combines multiple sparse (soft) decision trees to attain excellent predictive accuracy. We develop a novel semiparametric hazards regression model by modeling the hazard function as a product of a parametric baseline hazard function and a nonparametric component that uses SBART to incorporate clustering, unknown functional forms of the main effects, and interaction effects of various covariates. In addition to being applicable for left‐censored, right‐censored, and interval‐censored survival data, our methodology is implemented using a data augmentation scheme which allows for existing Bayesian backfitting algorithms to be used. We illustrate the practical implementation and advantages of our method via simulation studies and an analysis of a prostate cancer surgery study where dependence on the experience and skill level of the physicians leads to clustering of survival times. We conclude by discussing our method's applicability in studies involving high‐dimensional data with complex underlying associations. [ABSTRACT FROM AUTHOR]

In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort. [ABSTRACT FROM AUTHOR]

We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the classification and regression tree algorithm that use splitting criteria based on subgroup‐specific treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g‐formula, and doubly robust estimators of subgroup‐specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients. [ABSTRACT FROM AUTHOR]

Data synthesis is an effective statistical approach for reducing data disclosure risk. Generating fully synthetic data might minimize such risk, but its modeling and application can be difficult for data from large, complex surveys. This article extended the two-stage imputation to simultaneously impute item missing values and generate fully synthetic data. A new combining rule for making inferences using data generated in this manner was developed. Two semiparametric missing data imputation models were adapted to generate fully synthetic data for skewed continuous variable and sparse binary variable, respectively. The proposed approach was evaluated using simulated data and real longitudinal data from the Health and Retirement Study. The proposed approach was also compared with two existing synthesis approaches: (1) parametric regressions models as implemented in IVEware ; and (2) nonparametric Classification and Regression Trees as implemented in synthpop package for R using real data. The results show that high data utility is maintained for a wide variety of descriptive and model-based statistics using the proposed strategy. The proposed strategy also performs better than existing methods for sophisticated analyses such as factor analysis. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is the problem of determining the likelihood of a landslide occurrence in a particular area with respect to the geographical and morphological properties of the area. This paper presents a hybrid method, namely SkySlide , that incorporates clustering, skyline operator, classification and majority voting principle for region-scale landslide susceptibility assessment. Clustering and skyline operator are utilized to model landslides while classification and majority voting principle are utilized to assess landslide susceptibility. The contribution of the study is 2-fold. First, the proposed method requires properties of landslide-occurring data only to model landslides. Second, the proposed method is evaluated on imbalanced data and experimental results include performance metrics of imbalanced data. Experiments conducted on two real-life datasets show that clustering greatly improves performance of SkySlide. Experiments further demonstrate that SkySlide achieves higher class balance accuracy, Matthews correlation coefficient, geometric mean and bookmaker informedness scores compared with the most commonly used methods for landslide susceptibility assessment such as support vector machines, logistic regression and decision trees. [ABSTRACT FROM AUTHOR]

Background People commencing a carbohydrate-restricted diet (CRD) experience markedly heterogenous responses in LDL cholesterol, ranging from extreme elevations to reductions. Objectives The aim was to elucidate possible sources of heterogeneity in LDL cholesterol response to a CRD and thereby identify individuals who may be at risk for LDL cholesterol elevation. Methods Hypothesis-naive analyses were conducted on web survey data from 548 adults consuming a CRD. Univariate and multivariate regression models and regression trees were built to evaluate the interaction between body mass index (BMI) and baseline lipid markers. Data were also collected from a case series of five clinical patients with extremely high LDL cholesterol consuming a CRD. Results BMI was inversely associated with LDL cholesterol change. Low triglyceride (TG) to HDL cholesterol ratio, a marker of good metabolic health, predicted larger LDL cholesterol increases. A subgroup of respondents with LDL cholesterol ≥200 mg/dL, HDL cholesterol ≥80 mg/dL, and TG ≤70 mg/dL were characterized as "lean mass hyper-responders." Respondents with this phenotype (n  = 100) had a lower BMI and, remarkably, similar prior LDL cholesterol versus other respondents. In the case series, moderate reintroduction of carbohydrate produced a marked decrease in LDL cholesterol. Conclusions These data suggest that, in contrast to the typical pattern of dyslipidemia, greater LDL cholesterol elevation on a CRD tends to occur in the context of otherwise low cardiometabolic risk. [ABSTRACT FROM AUTHOR]

Background There is a dearth of research to understand which children, among those who are exposed at home to tuberculosis (TB), are at the highest risk of TB disease, to tailor care. We sought to identify predictors of TB progression in children. Methods We conducted a prospective cohort study of children living with adults with pulmonary TB in Lima, Peru (2009–2012). We applied classification and regression tree analysis to examine potential predictors of incident TB disease during 12 months in 3 age groups (0–4, 5–9, and 10–14 years). We calculated the relative risk (RR) for top predictors in each age group. Results Among 4545 children 0–14 years old, 156 (3.4%) were diagnosed with TB within 1 year of household exposure to TB (3.4%, 2.3%, and 4.7% in children 0–4, 5–9, and 10–14 years old, respectively). The most important predictor of TB was having a positive tuberculin skin test (TST) result, with RRs of 6.6 (95% CI, 4.0–10.7), 6.6 (95% CI, 3.2–13.6), and 5.2 (95% CI, 3.0–9.0) in the age groups 0–4, 5–9, and 10–14 years, respectively. In young children with a positive TST, not using isoniazid preventive treatment further increased risk of disease (RR, 12.2 [95% CI, 3.8–39.2]). Conclusions We present a tool that identifies child household contacts at high risk of TB disease progression based on data collected during contact tracing. In addition to the use of TB preventive therapy for all children exposed at home to TB, those children at highest risk of progressing to TB disease may benefit from more frequent follow-up. [ABSTRACT FROM AUTHOR]

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level. [ABSTRACT FROM AUTHOR]

Background: Cut-points to define slow walking speed have largely been derived from expert opinion.Methods: Study participants (13 589 men and 5043 women aged ≥65years) had walking speed (m/s) measured over 4-6 m (mean ± SD: 1.20 ± 0.27 m/s in men and 0.94 ± 0.24 m/s in women.) Mobility limitation was defined as any self-reported difficulty with walking approximately 1/4 mile (prevalence: 12.6% men, 26.4% women). Sex-stratified classification and regression tree (CART) models with 10-fold cross-validation identified walking speed cut-points that optimally discriminated those who reported mobility limitation from those who did not.Results: Among 5043 women, CART analysis identified 2 cut-points, classifying 4144 (82.2%) with walking speed ≥0.75 m/s, which we labeled as "fast"; 478 (9.5%) as "intermediate" (walking speed ≥0.62 m/s but <0.75 m/s); and 421 (8.3%) as "slow" (walking speed <0.62 m/s). Among 13 589 men, CART analysis identified 3 cut-points, classifying 10 001 (73.6%) with walking speed ≥1.00 m/s ("very fast"); 2901 (21.3%) as "fast" (walking speed ≥0.74 m/s but <1.00 m/s); 497 (3.7%) as "intermediate" (walking speed ≥0.57 m/s but <0.74 m/s); and 190 (1.4%) as "slow" (walking speed <0.57 m/s). Prevalence of self-reported mobility limitation was lowest in the "fast" or "very fast" (11% for men and 19% for women) and highest in the "slow" (60.5% in men and 71.0% in women). Rounding the 2 slower cut-points to 0.60 m/s and 0.75 m/s reclassified very few participants.Conclusions: Cut-points in walking speed of approximately 0.60 m/s and 0.75 m/s discriminate those with self-reported mobility limitation from those without. [ABSTRACT FROM AUTHOR]

Background Clinical studies have demonstrated inferior cure rates when metronidazole (MTZ) is used to treat Clostridioides difficile infection (CDI). We hypothesized that a newly identified, heme-inducible form of reduced MTZ susceptibility in C. difficile leads to higher odds of initial clinical failure in patients with CDI treated with MTZ. Methods This multicenter cohort study included adults diagnosed with CDI between 2017 and 2018. C. difficile isolated from stool samples underwent agar dilution MTZ susceptibility testing with incorporation of fresh heme. Blinded investigators reviewed medical records for initial clinical failure and other relevant clinical variables. Classification and regression tree (CART) analysis was used to identify the MTZ minimum inhibitory concentration (MIC) breakpoint that was predictive of initial clinical failure. Results were confirmed using univariate and multivariable logistic regression analyses to account for potential confounders. Results Of the 356 patients included, 72% received MTZ-based therapy and 27% experienced initial clinical failure. CART analysis identified an MTZ MIC ≥1 µg/mL above which patients had a higher rate of initial clinical failure. MTZ MICs ranged from 0.25 to 8 µg/mL (MIC50/90 = 0.25/2 µg/mL), and approximately 18% of isolates had MTZ MICs ≥1 µg/mL. In multivariable analysis, an MTZ MIC ≥1 µg/mL was an independent predictor of initial clinical failure in patients receiving an MTZ-based treatment regimen (odds ratio, 2.27 [95% confidence interval, 1.18–4.34]). Conclusions Using a reproducible method to determine C. difficile MICs to MTZ, a breakpoint of ≥1 µg/mL identified patients at higher risk of initial clinical failure. [ABSTRACT FROM AUTHOR]

A utility‐based Bayesian population finding (BaPoFi) method was proposed by Morita and Müller to analyze data from a randomized clinical trial with the aim of identifying good predictive baseline covariates for optimizing the target population for a future study. The approach casts the population finding process as a formal decision problem together with a flexible probability model using a random forest to define a regression mean function. BaPoFi is constructed to handle a single continuous or binary outcome variable. In this paper, we develop BaPoFi‐TTE as an extension of the earlier approach for clinically important cases of time‐to‐event (TTE) data with censoring, and also accounting for a toxicity outcome. We model the association of TTE data with baseline covariates using a semiparametric failure time model with a Pólya tree prior for an unknown error term and a random forest for a flexible regression mean function. We define a utility function that addresses a trade‐off between efficacy and toxicity as one of the important clinical considerations for population finding. We examine the operating characteristics of the proposed method in extensive simulation studies. For illustration, we apply the proposed method to data from a randomized oncology clinical trial. Concerns in a preliminary analysis of the same data based on a parametric model motivated the proposed more general approach. [ABSTRACT FROM AUTHOR]

We investigate whether generating synthetic data can be a viable strategy for providing access to detailed geocoding information for external researchers, without compromising the confidentiality of the units included in the database. Our work was motivated by a recent project at the Institute for Employment Research in Germany that linked exact geocodes to the Integrated Employment Biographies, a large administrative database containing several million records. We evaluate the performance of three synthesizers regarding the trade-off between preserving analytical validity and limiting disclosure risks: one synthesizer employs Dirichlet Process mixtures of products of multinomials, while the other two use different versions of Classification and Regression Trees (CART). In terms of preserving analytical validity, our proposed synthesis strategy for geocodes based on categorical CART models outperforms the other two. If the risks of the synthetic data generated by the categorical CART synthesizer are deemed too high, we demonstrate that synthesizing additional variables is the preferred strategy to address the risk-utility trade-off in practice, compared to limiting the size of the regression trees or relying on the strategy of providing geographical information only on an aggregated level. We also propose strategies for making the synthesizers scalable for large files, present analytical validity measures and disclosure risk measures for the generated data, and provide general recommendations for statistical agencies considering the synthetic data approach for disseminating detailed geographical information. [ABSTRACT FROM AUTHOR]

The scientific value of the next generation of large continuum surveys would be greatly increased if the redshifts of the newly detected sources could be rapidly and reliably estimated. Given the observational expense of obtaining spectroscopic redshifts for the large number of new detections expected, there has been substantial recent work on using machine learning techniques to obtain photometric redshifts. Here, we compare the accuracy of the predicted photometric redshifts obtained from deep learning (DL) with the k- nearest neighbour (kNN) and the decision tree regression (DTR) algorithms. We find using a combination of near-infrared, visible, and ultraviolet magnitudes, trained upon a sample of Sloan Digital Sky Survey quasi-stellar objects, that the kNN and DL algorithms produce the best self-validation result with a standard deviation of σΔ z = 0.24 (σΔ z (norm) = 0.11). Testing on various subsamples, we find that the DL algorithm generally has lower values of σΔ z , in addition to exhibiting a better performance in other measures. Our DL method, which uses an easy to implement off-the-shelf algorithm with neither filtering nor removal of outliers, performs similarly to other, more complex, algorithms, resulting in an accuracy of Δ z < 0.1 up to z ∼ 2.5. Applying the DL algorithm trained on our 70 000 strong sample to other independent (radio-selected) data sets, we find σΔ z ≤ 0.36 (σΔ z (norm) ≤ 0.17) over a wide range of radio flux densities. This indicates much potential in using this method to determine photometric redshifts of quasars detected with the Square Kilometre Array. [ABSTRACT FROM AUTHOR]

Write HT ht for the training data estimator. To calculate the first term in (1), they regress the full estimator on HT ht . Rather than permuting HT ht , they replace the estimated coefficients for HT ht in the full model with values of zero. This brings us to the authors second estimator HT ht (Algorithm 2), which we have discussed in more detail. [Extracted from the article]

Identifying causal relationships on a large-scale system is highly complicated, and when not careful, statistical theories may be violated, and biological spurious claims generated. The causality between livestock health/performance and wildfire smoke has not been investigated. To critically evaluate the effects of wildfire, air quality data was obtained and validated with sensors (1.6 km from feedlot) before analyses. Herein, we examine the influence of environmental quality parameters effects on dry matter intake (DMI), body weight (BW), water intake (WI), and average daily gain (ADG) over a period of three years in Reno, Nevada. The datasets comprised represented controlled no-smoke exposure instances and days with air quality indexes (AQI) as high as 453 (hazardous). The dataset consisted of over 10,000 observations with daily mapping of WI, DMI, ADG, and BW. To evaluate the potential relationship between air quality and animal performance, variable tree length Bayesian additive regression trees (BART) were utilized to investigate the relationships between AQI, particulate matter (PM) 2.5 um and 10 um, NO2, SO2, ozone, and CO levels in the air were utilized as covariates for BART. All statistical analyses were performed on R Statistical Software (R Core Team 2022), with use of the bartMachine package. The correlation values (), 95 % confidence intervals (CI), root mean squared error (RMSE), and coefficient of determination (r2) are reported. A significant correlation was detected between the environmental parameters and BW, (= 0.863, CI = [0.849, 0.877], P > 0.001, r2 = 0.75, RMSE = 51.27) with PM2.5 and PM10 being the most included parameters in the regression trees. For WI, correlation was also significant (= 0.447, CI = [0.402, 0.490], P < 0.001, r2 = 0.20, RMSE = 14.57), the most included variables on the regression trees were the 10-um particulate matter followed by the CO. For DMI (= 0.727, CI = [0.700, 0.752], r2 = 0.53, RMSE = 2.58) PM10 and AQI were the most important inclusion proportion parameters. Similarly, for ADG (= 0.354, CI = [0.305, 0.401], r2 = 13, RMSE = 1.60) with ozone and PM10 being the most included parameters. In conclusion, our results more carefully examine relationships between smoke parameters and livestock performance. Uncertainty of relationship estimates, and variable importance highlights how performance parameters are differently affected by different environmental parameters highlighting the need for experiments with controlled exposure through time. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to establish age- and sex-specific pediatric reference intervals of serum potassium (K), sodium (Na), chlorine (Cl), calcium (Ca), magnesium (Mg), and phosphorus (P) using a direct sampling technique.Methods: In accordance with the a priori approach, healthy individuals (n = 6,466, aged 1 month to <18 years) were enrolled from five regions in Jilin Province, China, and all analytes were performed in the center laboratory. Reference intervals were divided according to the regression tree and Harris and Boyd's method, and then they were calculated by the nonparametric rank method. The dynamic changes of reference intervals were evaluated by the lambda-mu-sigma (LMS) method.Results: Reference intervals of serum Na and Ca were divided into three age-specific partitions. The concentrations of K, Cl, and Mg remained stable with age. However, only dramatic sex-specific changes of P were shown in those 11 to less than 13 years old and 13 to less than 15 years old, with an earlier peak time in females than in males. The correlation between Na and Cl was the strongest among all serum electrolytes (r = 0.31).Conclusions: Serum electrolyte reference intervals for children and adolescents were established by regression tree, z test, and the LMS method, which provide a more accurate interpretation for diagnosis and prognosis evaluation of clinical pediatric diseases. [ABSTRACT FROM AUTHOR]

Background: It remains uncertain whether the association between physical activity (PA) and falls is U-shaped, and few studies have explored the potential mediation of PA accumulation pattern.Methods: We measured PA in 671 community-dwelling older adults (82.7 ± 3.8 years) using wrist-worn accelerometer for 7 days. PA was further classified to bouted PA (≥10 minutes bout length) and sporadic PA (<10 minutes bout length) for subanalysis. Fall incidence in the following 12-month was recorded through tri-monthly telephone interviews. Classification and Regression Tree analysis was used to identify two optimal cutoff values of each PA measurement to predict falls. Participants were then divided into "inactive," "moderately active," and "highly active" groups accordingly. Negative binomial regression models were used to estimate the association between the PA measures and fall incidence.Results: Six hundred and thirty-nine participants completed 12-month follow-up. Ninety-three (14.6%) experienced a total of 118 falls. Inactive and highly active older adults had higher falls per person month relative to the moderately active group (inactive: incidence rate ratios [IRR] = 2.372, 95% confidence interval [CI] = 1.317-4.271; highly active: IRR = 2.731, 95% CI = 1.196-6.232). Subanalyses found similar significant finding with bouted PA (p < .001) but not sporadic PA (p ≥ .221). The association between bouted PA and falls remained significant even after adjusting fall incidence for bouted activity time (inactive: IRR = 3.636, 95% CI = 2.238-5.907; highly active: IRR = 1.823, 95% CI = 1.072-3.1). Further adjustments for fall-related risk factors did not meaningfully change the results.Conclusion: A U-shaped relationship was identified between bouted but not sporadic PA and fall incidence. There is an approximately twofold increase in fall rate in highly active older adults even after adjusting for activity time. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide Open in new tab Download slide OBJECTIVES Many adults with repaired tetralogy of Fallot will require a pulmonary valve replacement (PVR), but there is no consensus on the best timing. In this study, we aim to evaluate the impact of age at PVR on outcomes. METHODS This is a national multicentre retrospective study including all patients >15 years of age with repaired tetralogy of Fallot who underwent their first PVR between 2000 and 2013. The optimal age cut-off was identified using Cox regression and classification and regression tree analysis. RESULTS A total of 707 patients were included, median age 26 (15–72) years. The mortality rate at 10 years after PVR was 4.2%, and the second PVR rate of 6.8%. Age at PVR of 35 years was identified as the optimal cut-off in relation to late mortality. Patients above 35 years of age had a 5.6 fold risk of death at 10 years compared with those with PVR under 35 years (10.4% vs 1.3%, P  < 0.001), more concomitant tricuspid valve repair/replacement (15.1% vs 5.7%, P  < 0.001) and surgical arrhythmia treatment (18.4% vs 5.9%, P  < 0.001). In those under 50 years, there was an 8.7 fold risk of late death compared with the general population, higher for those with PVR after 35 than those with PVR below 35 years (hazard ratio 9.9 vs 7.4). CONCLUSIONS Patients above 35 years of age with repaired tetralogy of Fallot have significantly worse mortality after PVR, compared with younger patients and a higher burden of mortality relative to the general population. This suggests that there are still cases where the timing of initial PVR is not optimal, warranting a re-evaluation of criteria for intervention. [ABSTRACT FROM AUTHOR]

Empirical studies are popular in estimating fish natural mortality rate (⁠ M ⁠). However, these empirical methods derive M from other life-history parameters and are often perceived as being less reliable than direct methods. To improve the predictive performance and reliability of empirical methods, we develop ensemble learning models, including bagging trees, random forests, and boosting trees, to predict M based on a dataset of 256 records of both Chondrichthyes and Osteichthyes. Three common life-history parameters are used as predictors: the maximum age and two growth parameters (growth coefficient and asymptotic length). In addition, taxonomic variable class is included to distinguish Chondrichthyes and Osteichthyes. Results indicate that tree-based ensemble learning models significantly improve the accuracy of M estimate, compared to the traditional statistical regression models and the basic regression tree model. Among ensemble learning models, boosting trees and random forests perform best on the training dataset, but the former performs a slightly better on the test dataset. We develop four boosting trees models for estimating M based on varying life-history parameters, and an R package is provided for interested readers to estimate M of their new species. [ABSTRACT FROM AUTHOR]

Algorithms are regularly used to identify persons living with diagnosed human immunodeficiency virus (HIV) (PLWDH) in Medicaid data. To our knowledge, there are no published reports of an HIV algorithm from Medicaid claims codes that have been compared with an HIV surveillance system to assess its sensitivity, specificity, positive predictive value, and negative predictive value in identifying PLWDH. Therefore, our aims in this study were to 1) develop an algorithm that could identify PLWDH in New York State Medicaid data from 2006–2014 and 2) validate this algorithm using the New York State HIV surveillance system. Classification and regression tree analysis identified 16 nodes that we combined to create a case-finding algorithm with 5 criteria. This algorithm identified 86,930 presumed PLWDH, 88.0% of which were verified by matching to the surveillance system. The algorithm yielded a sensitivity of 94.5%, a specificity of 94.4%, a positive predictive value of 88.0%, and a negative predictive value of 97.6%. This validated algorithm has the potential to improve the utility of Medicaid data for assessing health outcomes and programmatic interventions. [ABSTRACT FROM AUTHOR]

Background Malaria presents with unspecific clinical symptoms that frequently overlap with other infectious diseases and is also a risk factor for coinfections, such as non-Typhi Salmonella. Malaria rapid diagnostic tests are sensitive but unable to distinguish between an acute infection requiring treatment and asymptomatic malaria with a concomitant infection. We set out to test whether cytokine profiles could predict disease status and allow the differentiation between malaria and a bacterial bloodstream infection. Methods We created a classification model based on cytokine concentration levels of pediatric inpatients with either Plasmodium falciparum malaria or a bacterial bloodstream infection using the Luminex platform. Candidate markers were preselected using classification and regression trees, and the predictive strength was calculated through random forest modeling. Results Analyses revealed that a combination of 7–15 cytokines exhibited a median disease prediction accuracy of 88% (95th percentile interval, 73%–100%). Haptoglobin, soluble Fas-Ligand, and complement component C2 were the strongest single markers with median prediction accuracies of 82% (with 95th percentile intervals of 71%–94%, 62%–94%, and 62%–94%, respectively). Conclusions Cytokine profiles possess good median disease prediction accuracy and offer new possibilities for the development of innovative point-of-care tests to guide treatment decisions in malaria-endemic regions. [ABSTRACT FROM AUTHOR]

Three approaches to estimation from nonprobability samples are quasi-randomization, superpopulation modeling, and doubly robust estimation. In the first, the sample is treated as if it were obtained via a probability mechanism, but unlike in probability sampling, that mechanism is unknown. Pseudo selection probabilities of being in the sample are estimated by using the sample in combination with some external data set that covers the desired population. In the superpopulation approach, observed values of analysis variables are treated as if they had been generated by some model. The model is estimated from the sample and, along with external population control data, is used to project the sample to the population. The specific techniques are the same or similar to ones commonly employed for estimation from probability samples and include binary regression, regression trees, and calibration. When quasi-randomization and superpopulation modeling are combined, this is referred to as doubly robust estimation. This article reviews some of the estimation options and compares them in a series of simulation studies. [ABSTRACT FROM AUTHOR]

This paper demonstrates the advantages of sharing information about unknown features of covariates across multiple model components in various nonparametric regression problems including multivariate, heteroscedastic, and semicontinuous responses. In this paper, we present a methodology which allows for information to be shared nonparametrically across various model components using Bayesian sum‐of‐tree models. Our simulation results demonstrate that sharing of information across related model components is often very beneficial, particularly in sparse high‐dimensional problems in which variable selection must be conducted. We illustrate our methodology by analyzing medical expenditure data from the Medical Expenditure Panel Survey (MEPS). To facilitate the Bayesian nonparametric regression analysis, we develop two novel models for analyzing the MEPS data using Bayesian additive regression trees—a heteroskedastic log‐normal hurdle model with a "shrink‐toward‐homoskedasticity" prior and a gamma hurdle model. [ABSTRACT FROM AUTHOR]

Stable carbon isotope ratios (δ 13C) in tree rings have been widely used to study changes in intrinsic water-use efficiency (iWUE), sometimes with limited consideration of how C-isotope discrimination is affected by tree height and canopy position. Our goals were to quantify the relationships between tree size or tree microenvironment and wood δ 13C for eight functionally diverse temperate tree species in northern New England and to better understand the physical and physiological mechanisms underlying these differences. We collected short increment cores in closed-canopy stands and analyzed δ 13C in the most recent 5 years of growth. We also sampled saplings in both shaded and sun-exposed environments. In closed-canopy stands, we found strong tree-size effects on δ 13C, with 3.7–7.2‰ of difference explained by linear regression vs height (0.11–0.28‰ m−1), which in some cases is substantially stronger than the effect reported in previous studies. However, open-grown saplings were often isotopically more similar to large codominant trees than to shade-grown saplings, indicating that light exposure contributes more to the physiological and isotopic differences between small and large trees than does height. We found that in closed-canopy forests, δ 13C correlations with diameter at breast height were nonlinear but also strong, allowing a straightforward procedure to correct tree- or stand-scale δ 13C-based iWUE chronologies for changing tree size. We demonstrate how to use such data to correct and interpret multi-decadal composite isotope chronologies in both shade-regenerated and open-grown tree cohorts, and we highlight the importance of understanding site history when interpreting δ 13C time series. [ABSTRACT FROM AUTHOR]

Big Data are a "big challenge" for finite population inference. Lack of control over data-generating processes by researchers in the absence of a known random selection mechanism may lead to biased estimates. Further, larger sample sizes increase the relative contribution of selection bias to squared or absolute error. One approach to mitigate this issue is to treat Big Data as a random sample and estimate the pseudo-inclusion probabilities through a benchmark survey with a set of relevant auxiliary variables common to the Big Data. Since the true propensity model is usually unknown, and Big Data tend to be poor in such variables that fully govern the selection mechanism, the use of flexible non-parametric models seems to be essential. Traditionally, a weighted logistic model is recommended to account for the sampling weights in the benchmark survey when estimating the propensity scores. However, handling weights is a hurdle when seeking a broader range of predictive methods. To further protect against model misspecification, we propose using an alternative pseudo-weighting approach that allows us to fit more flexible modern predictive tools such as Bayesian Additive Regression Trees (BART), which automatically detect non-linear associations as well as high-order interactions. In addition, the posterior predictive distribution generated by BART makes it easier to quantify the uncertainty due to pseudo-weighting. Our simulation findings reveal further reduction in bias by our approach compared with conventional propensity adjustment method when the true model is unknown. Finally, we apply our method to the naturalistic driving data from the Safety Pilot Model Deployment using the National Household Travel Survey as a benchmark. [ABSTRACT FROM AUTHOR]

Motivation Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject's baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training dataset that can be used to compute an estimation of the individual treatment effect with any adequate method. Results The merits of the proposed method are investigated with a simulation study where it is compared to numerous competitors, including recent state-of-the-art methods. The results indicate that the proposed method has a very good and stable performance to estimate the individual treatment effects. Two examples of application with a colon cancer data and breast cancer data show that the proposed method can detect a treatment effect in a sub-population even when the overall effect is small or nonexistent. Availability and implementation The authors are working on an R package implementing the proposed method and it will be available soon. In the meantime, the code can be obtained from the first author at sami.tabib@hec.ca. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Much of survival analysis is concerned with absorbing events, i.e., subjects can only experience a single event such as mortality. This article is focused on non-absorbing or recurrent events, i.e., subjects are capable of experiencing multiple events. Recurrent events have been studied by many; however, most rely on the restrictive assumptions of linearity and proportionality. We propose a new method for analyzing recurrent events with Bayesian Additive Regression Trees (BART) avoiding such restrictive assumptions. We explore this new method via a motivating example of hospital admissions for diabetes patients and simulated data sets. [ABSTRACT FROM AUTHOR]