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The present longitudinal meta-analysis aimed to estimate the growth curves and age at peak height velocity (PHV) in young male athletes, considering anthropometric data from available longitudinal studies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, studies with repeated measurements in young male athletes were identified from searches across four databases (MEDLINE, SPORTDiscus, Web of Science, and SCOPUS). Estimations were based on multilevel polynomial models using a fully Bayesian framework. After a full-text screening of 317 studies meeting the eligibility criteria, 31 studies were considered. Studies were excluded mainly due to study design, repeated reporting, and incomplete reporting of the outcomes. Of the 31 studies analysed, 26 (84%) focused on young European athletes. The average age at PHV for the total sample of studies with young athletes was 13.1 years (90% credible interval: 12.9; 13.4). When considering data by sport, there was substantial variation in the age at PHV estimates (range: 12.4 to 13.5 years). As most studies in the meta-analysis focused on young European football players (52%), predictions for young athletes from other sports may be limited. The age at PHV in the available data occurred earlier than in general paediatric populations.

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Continuous clustered proportion data often arise in various areas of the social and political sciences where the response variable of interest is a proportion (or percentage). An example is the behavior of the proportion of voters favorable to a political party in municipalities (or cities) of a country over time. This behavior can be different depending on the region of the country, giving rise to groups (or clusters) with similar profiles. For this kind of data, we propose a finite mixture of a random effects regression model based on the L-Logistic distribution. A Markov chain Monte Carlo algorithm is tailored to obtain posterior distributions of the unknown quantities of interest through a Bayesian approach. To illustrate the proposed method, with emphasis on analysis of clusters, we analyze the proportion of votes for a political party in presidential elections in different municipalities observed over time, and then identify groups according to electoral behavior at different levels of favorable votes.

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Background: Applied research using the phase angle (PhA) in children and adolescents has increased notably. Using multilevel modeling in a fully Bayesian framework, we examined the relationships between PhA, age, sex, biological maturity status, and body size in 10-16-year-old adolescents. Methods: The sample comprised 519 adolescents (women, n = 241; men, n = 278) from Campinas, São Paulo, Brazil. Biological maturity status was assessed with self-examination of pubertal development for sexual maturity and maturity offset protocol to estimate age at peak height velocity (PHV) for somatic maturity status. Stature and body mass were measured by anthropometry. Phase angle was calculated based on raw resistance and reactance values (50 kHz frequency) obtained by bioelectrical impedance with the foot-to-hand technology. Results: The multilevel regression analysis revealed that boys had significantly higher values of phase angle than girls, adjusting for age group and sexual maturity status. Overall, older and more mature adolescents had higher values of phase angle. When considering aligning variation in the phase angle by distance to estimated PHV (maturity offset), there was a higher association between the phase angle and time before and after predicted age at PHV for boys (r = 0.31, 90% CI: 0.23 to 0.39) than girls (r = 0.2, 90% CI: 0.11 to 0.28). When including body mass in the multilevel models, corresponding changes in the overall body mass mediate most of the influence of the maturity status and age group on the phase angle. Conclusion: The present study demonstrated that the variability in phase angle is related to inter-individual variation in sex, age, and maturity status, as well as differences in body size. Research with adolescents considering phase angle should use multilevel modeling with standardized parameters as default to adjust for the concurrent influence of sex, age, maturity status, and body size.

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Genomic enabled prediction is playing a key role for the success of genomic selection (GS). However, according to the No Free Lunch Theorem, there is not a universal model that performs well for all data sets. Due to this, many statistical and machine learning models are available for genomic prediction. When multitrait data is available, models that are able to account for correlations between phenotypic traits are preferred, since these models help increase the prediction accuracy when the degree of correlation is moderate to large. For this reason, in this chapter we review multitrait models for genome-enabled prediction and we illustrate the power of this model with real examples. In addition, we provide details of the software (R code) available for its application to help users implement these models with its own data. The multitrait models were implemented under conventional Bayesian Ridge regression and best linear unbiased predictor, but also under a deep learning framework. The multitrait deep learning framework helps implement prediction models with mixed outcomes (continuous, binary, ordinal, and count, measured on different scales), which is not easy in conventional statistical models. The illustrative examples are very detailed in order to make the implementation of multitrait models in plant and animal breeding friendlier for breeders and scientists.

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The present study examined the influence of the specialization onset on the magnitude and patterns of changes in basketball-specific physical fitness within a competitive season and developmental fitness trends between 11 and 17 years in young basketball players. Repeated measures of 181 young basketball players (female, n = 40; male, n = 141) were examined. Anthropometry, age, estimated maturity status, and basketball-specific physical fitness (assessed with the countermovement jump, line drill, and yo-yo intermittent recovery level-1 and fitness score) were considered. Players were grouped by the onset of specialization as related to biological maturation milestones (pre-puberty, mid-puberty, and late-puberty specialization). The within-season and developmental changes in physical fitness were fitted using multilevel modeling in a fully Bayesian framework. The fitness outcomes were similar between-player and within-player changes when grouped by specialization across a season. Fitness improvements across a season were apparent for female players, while male players maintained their performance levels. There was no variation in the patterns of physical fitness development between 11 and 17 years associated with the onset of specialization. Conditional on our data and models, the assumption that early sport specialization provides a physical fitness advantage for future athletic success does not hold.

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The study's objective was to compare the genomic prediction ability methods for the traits milk yield, milk composition and somatic cell count of Saanen Brazilian goats. Nine hundred forty goats, genotyped with an Axiom_OviCap (Caprine) panel, Affimetrix customized array with 62,557 single nucleotide polymorphisms (SNPs), were used for the genomic selection analyses. The genomic methods studied to estimate the effects of SNPs and direct genomic values (DGV) were as follows: (a) genomic BLUP (GBLUP), (b) Bayes Cπ and (c) Bayesian Lasso (BLASSO). Estimated breeding values (EBV) and deregressed estimated breeding values (dEBV) were used as response variables for the genomic predictions. The prediction ability was assessed by Pearson's correlation between DGV and response variables (EBV and dEBV). Regression coefficients of the response variables on the DGV were obtained to verify if the genomic predictions were biased. In addition, the mean square error of prediction (MSE) was used as a measure of verification of model fit to the data. The means of prediction accuracy, when EBV was used as a response variable, were 0.68, 0.68 and 0.67 for GBLUP, Bayes Cπ and BLASSO, respectively. With dEBV, the mean prediction accuracy was 0.50 for all models. The averages of the EBV regression coefficients on DGV were 1.08 for all models (GBLUP, Bayes Cπ and BLASSO), higher than those obtained for the regression coefficient of dEBV on DGV, which presented values of 1.05, 1.05 and 1.08 for GBLUP, Bayes Cπ and BLASSO, respectively. None of the methods stood out in terms of prediction ability; however, the GBLUP method was the most appropriate for estimating the DGV, in a slightly more reliable and less biased way, besides presenting the lowest computational cost. In the context of the present study, EBV was the preferred response variables considering the genomic prediction accuracy despite dEBV also presented lower bias.

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In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.

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Border regions have been implicated as important hot spots of malaria transmission, particularly in Latin America, where free movement rights mean that residents can cross borders using just a national ID. Additionally, rural livelihoods largely depend on short-term migrants traveling across borders via the Amazon's river networks to work in extractive industries, such as logging. As a result, there is likely considerable spillover across country borders, particularly along the border between Peru and Ecuador. This border region exhibits a steep gradient of transmission intensity, with Peru having a much higher incidence of malaria than Ecuador. In this paper, we integrate 13 years of weekly malaria surveillance data collected at the district level in Peru and the canton level in Ecuador, and leverage hierarchical Bayesian spatiotemporal regression models to identify the degree to which malaria transmission in Ecuador is influenced by transmission in Peru. We find that increased case incidence in Peruvian districts that border the Ecuadorian Amazon is associated with increased incidence in Ecuador. Our results highlight the importance of coordinated malaria control across borders.

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The purpose of this study was to examine the variation in accumulated basketball training experience, body size, functional performance, deliberate practice motivation, achievement and competitiveness motivation and sources of enjoyment among young female basketball players, partitioning the potential variation by individuals´ biological characteristics (menarche status) and contextual characteristics (competitive age group and competitive level). We considered 114 adolescent female basketball players aged 14.3 (1.8) years. We used multilevel regression and poststratification estimations. The adolescent female basketball players selected for state-level had more accumulated experience, were taller and with better functional performance. Conditional on the data, youth female coaches tend to value (probably overvalue) size and function when selecting/promoting players, even at early age groups, likely contributing to an overrepresentation of early maturing girls in at early age groups. Players from club- and state-level were similarly highly motivated for deliberate practice and to achievement. Only for competitiveness, state-level players had higher values than club level players. The sources of enjoyment were influenced by context (competitive levels) for self-referenced competencies and others-referenced competencies. Structured programs of training and competition in youth female basketball provide a nurturing environment for the development of players´ engagement and commitment to training and excellence attainment.

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We proposed a Bayesian analysis of pseudo-compositional data in presence of a latent factor, assuming a spatial structure. This development was motivated by a dataset containing information on the number of newborns of primiparous mothers living in each of the microregions of the state of Sao Paulo, Brazil, in the year of 2015, stratified by the age of the mothers (15-18, 19-29 and 30 years or more). Considering that data on newborns are not stochastically distributed among the three age groups, but they are explained in relation to women's population structure, we adopted the expression "pseudo-compositional data" to refer to this data structure. The hypothesis of interest establishes that the age of the first pregnancy is associated with the economic conditions of the geographic area where the mother lives. The incidence of poverty was included as an independent variable. Additive log-ratio (alr) and isometric log-ratio (ilr) transformations were considered, as is usually done in the analysis of compositional data. The model included a random effect related to the spatial effect assumed to have a conditional autoregressive structure. A Bayesian Markov Chain Monte Carlo (MCMC) simulation procedure was used to get the posterior summaries of interest. The model based on the (ilr) transformation was well fitted to the data, showing that in the microregions with the highest incidence of poverty, there are higher proportions of women who have their first child in adolescence, while in the microregions with the lowest incidence of poverty, there are higher proportions of women who have their first child after the age of 30 years. From these results it is possible to conclude that this Bayesian approach was very useful in the estimation of the parameters of the proposed model. The proposed method should have a broad application to other problems involving pseudo-compositional data.

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Motivated by a study tracking the progression of Parkinson's disease (PD) based on features extracted from voice recordings, an inhomogeneous hidden Markov model with continuous state-space is proposed. The approach addresses the measurement error in the response, the within-subject variability of the replicated covariates and presumed nondecreasing response. A Bayesian framework is described and an efficient Markov chain Monte Carlo method is developed. The model performance is evaluated through a simulation-based example and the analysis of a PD tracking progression dataset is presented. Although the approach was motivated by a PD tracking progression problem, it can be applied to any monotonic nondecreasing process whose continuous response variable is subject to measurement errors and where replicated covariates play a key role.

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Confidence intervals (CIs) depict the statistical uncertainty surrounding evolutionary divergence time estimates. They capture variance contributed by the finite number of sequences and sites used in the alignment, deviations of evolutionary rates from a strict molecular clock in a phylogeny, and uncertainty associated with clock calibrations. Reliable tests of biological hypotheses demand reliable CIs. However, current non-Bayesian methods may produce unreliable CIs because they do not incorporate rate variation among lineages and interactions among clock calibrations properly. Here, we present a new analytical method to calculate CIs of divergence times estimated using the RelTime method, along with an approach to utilize multiple calibration uncertainty densities in dating analyses. Empirical data analyses showed that the new methods produce CIs that overlap with Bayesian highest posterior density intervals. In the analysis of computer-simulated data, we found that RelTime CIs show excellent average coverage probabilities, that is, the actual time is contained within the CIs with a 94% probability. These developments will encourage broader use of computationally efficient RelTime approaches in molecular dating analyses and biological hypothesis testing.

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In many fields and applications, count data can be subject to delayed reporting. This is where the total count, such as the number of disease cases contracted in a given week, may not be immediately available, instead arriving in parts over time. For short-term decision making, the statistical challenge lies in predicting the total count based on any observed partial counts, along with a robust quantification of uncertainty. We discuss previous approaches to modeling delayed reporting and present a multivariate hierarchical framework where the count generating process and delay mechanism are modeled simultaneously in a flexible way. This framework can also be easily adapted to allow for the presence of underreporting in the final observed count. To illustrate our approach and to compare it with existing frameworks, we present a case study of reported dengue fever cases in Rio de Janeiro. Based on both within-sample and out-of-sample posterior predictive model checking and arguments of interpretability, adaptability, and computational efficiency, we discuss the relative merits of different approaches.

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Resumen ANTECEDENTES: El valor de p es el método más empleado para estimar la significación estadística de cualquier hallazgo; sin embargo, en los últimos años se ha intensificado su debate al respecto, debido a la baja credibilidad y reproducibilidad de diversos estudios. OBJETIVO: Describir el estado actual del concepto del valor de p y la significación estadística (prueba de significación de la hipótesis nula [por sus siglas en inglés: Null Hypothesis Significance Testing: NHST]), especificar los problemas más importantes y puntualizar las soluciones propuestas para la mejor utilización de los conceptos. METODOLOGÍA: Se llevó a cabo la búsqueda bibliográfica en MEDLINE y Google Scholar, con los términos: "NHST", "Statistical significance; P value" en idioma inglés y español, de 2018-2019, limitándose a la selección de artículos publicados entre 2005 y 2019, mediante la revisión de tipo narrativo con búsqueda manual; sobre todo estudios de metodología. RESULTADOS: La búsqueda global reportó 1411 artículos: 875 de PubMed y 536 de Google Scholar. Se excluyeron 817 por duplicación, 155 sin acceso completo y 414 ensayos clínicos (sin metodología estadística); los 25 restantes fueron el motivo del análisis. CONCLUSIONES: El concepto del valor de p no es simple, tiene varias falacias y malas interpretaciones que deben considerarse para evitarlas en lo posible. Se recomienda no usar el término "estadísticamente significativo" o "significativo", sustituir el umbral de 0.05 por 0.005, informar valores de p precisos y con IC95%, riesgo relativo, razón de momios, tamaño del efecto o potencia y métodos bayesianos.

Abstract BACKGROUND: The P value is the most widely used method of estimating the statistical significance of any finding, however, in recent years the debate over the P value has been increasingly intensified due to the low credibility and reproducibility of many studies. OBJECTIVE: To describe the current state of the concept of the value of P and the statistical significance (Null Hypothesis Significance Testing (NHST), specify the most important problems and point out the solutions proposed in the literature for their best use. METHODOLOGY: Search in MEDLINE and Google Scholar, with the terms: "NHST", "Statistical significance; P value "in English and Spanish, carried out from 2018-2019, limited to articles published from 2005 to 2019, and a narrative-type review with manual search. Articles on methodology were preferably selected. RESULTS: The global search yielded 1411 articles, 875 from PubMed and 536 from Google Scholar. 817 were excluded by duplication, 155 without full access, 414 from clinical trials, without statistical methodology. The 25 selected articles were the reason for the analysis. CONCLUSIONS: The concept of the value of P is not simple, and it has several fallacies and misinterpretations that must be taken into account to avoid them as much as possible. Recommendations: Do not use "statistically significant" or "significant", replace the threshold of 0.05 with 0.005, report accurate P values with 95% CI, relative risk, odds ratio, effect size or power, and Bayesian methods.

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Background and Objectives: This study considered the use of a generalized additive multilevel model to describe the joint-angle-specific functional hamstring to quadriceps ratio (H:Q ratio) in the knee, using all of the available truly isokinetic data within the range. Materials and Methods: Thirty healthy male basketball players aged 15.0 (1.4) years (average stature = 180.0 cm, SD = 11.1 cm; average body mass = 71.2, SD = 14.9 kg) years were considered. All players considered had no history of lower extremity musculoskeletal injury at the time of testing or during the 6 months before testing, and had been engaged in formal basketball training and competition for 5.9 (2.4) years. Moments of force of the reciprocal concentric and eccentric muscular actions for the knee extensors and flexors assessed by isokinetic dynamometry at 60°âˆ™s-1 were used. Results: Maximum moments of force were attained at different angle positions for knee extension. For knee flexion, it was apparent that there was an ability to maintain high levels of moment of force between 30° and 60° in the concentric muscular action, corresponding to the concentric action of the hamstrings. However, for the eccentric knee flexion, corresponding to the quadriceps action, there was a marked peak of moment of force at about 55°. The functional H:Q ratio for the knee extension was non-linear, remaining higher than 1.0 (i.e., point of equality) from the beginning of the extension until approximately 40° of the knee extension, leveling off below the point of equality thereafter. On average, the functional H:Q ratio for the knee flexion did not attain 1.0 across the range of motion. The functional H:Q ratio for the knee in the present sample peaked at 20° and 80°, declining between these angle positions to below 0.50 at about 0.54. Conclusions: Estimating the form of the non-linear relationship on-the-fly using a generalized additive multilevel model provides joint-angle-specific curves and joint-angle-specific functional H:Q ratio patterns, allowing the identification and monitoring of strength development, with potential implications for injury and performance.

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In this study, we examined the probability of coaches' survival in the top Brazilian professional football championship considering variation across the competitive seasons between 2012 and 2017, considering a multilevel framework. We also considered whether previous coaching experience in the top Brazilian professional football championship would change the probability of coaches' survival across the season. The data considered 4,560 games from the top professional Brazilian football league (Campeonato Brasileiro Série A) between the 2012 and 2017 seasons. At the start of each season, the coach from each team was followed, being recorded at the time the event occurred, i.e., the coach being sacked. A total survival of 120 coaches was considered between the seasons of 2012 and 2017, i.e., 20 coaches at the beginning of each season. Coaches were assigned as novice (no previous experience as head coach in the top Brazilian championship) or experienced (with at least some previous experience as head coach in the top Brazilian championship). Data were available and extracted from the official website of the Brazilian Football Confederation. On average and considering un-pooled observations, the median life of a coach was about 16.5 rounds. Considering variation between 2012 and 2017 seasons, only about 26.3% (95% CI: 18.2-36.1) of the coaches ended a season without being sacked. By mid-season, at round 19, the probability of coaches' survival was 0.42 (95% CI: 0.32-0.53). Variation between season on survival estimates per round was substantial (between-season standard deviation = 0.48, 95% credible intervals: 0.25-0.95; corresponding to an inverse logit = 0.62, 95% CI: 0.56-0.72). There was no substantial variation between novice and experienced coaches' survival probability. The present results expose the vulnerability of the coaching context in Brazilian football, potentially highlighting an excessive emphasis on short-term results to mediate club management decisions.

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The problem of event detection in general noisy signals arises in many applications; usually, either a functional form of the event is available, or a previous annotated sample with instances of the event that can be used to train a classification algorithm. There are situations, however, where neither functional forms nor annotated samples are available; then, it is necessary to apply other strategies to separate and characterize events. In this work, we analyze 15-min samples of an acoustic signal, and are interested in separating sections, or segments, of the signal which are likely to contain significant events. For that, we apply a sequential algorithm with the only assumption that an event alters the energy of the signal. The algorithm is entirely based on Bayesian methods.