Normative reference data for intracranial volume in children: The results of CT volumetry.

BACKGROUND: Intracranial volume (ICV) is an important indicator of the development of the brain and skull in children. At present, there is a lack of ICV growth standards based on large infant and children samples. Our aim was to assess the normal range of the ICV variation in Russian children using a modern automatic system for constructing the endocranial cavity (Endex) and to provide growth standards of the ICV for clinical practice. METHODS: High-resolution head CT scans were obtained from 673 apparently healthy children (380 boys and 293 girls) aged 0-17 years and transformed into the ICV estimates using the Endex software. The open-source software RefCurv utilizing R and the GAMLSS add-on package with the LMS method was then used for the construction of smooth centile growth references for ICV according to age and sex. RESULTS: We demonstrated that the ICVs estimates calculated using the Endex software are perfectly comparable with those obtained by a conventional technique (i.e. seed feeling). Sex-specific pediatric growth charts for ICV were constructed. CONCLUSIONS: This study makes available for use in clinical practice ICV growth charts for the age from 0 to 17 based on a sample of 673 high-resolution CT images.

Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China.

PURPOSES: To establish a normative range of MemTrax (MTx) metrics in the Chinese population. METHODS: The correct response percentage (MTx-%C) and mean response time (MTx-RT) were obtained and the composite scores (MTx-Cp) calculated. Generalized additive models for location, shape and scale (GAMLSS) were applied to create percentile curves and evaluate goodness of fit, and the speed-accuracy trade-off was investigated. RESULTS: 26,633 subjects, including 13,771 (51.71%) men participated in this study. Age- and education-specific percentiles of the metrics were generated. Q tests and worm plots indicated adequate fit for models of MTx-RT and MTx-Cp. Models of MTx-%C for the low and intermediate education fit acceptably, but not well enough for a high level of education. A significant speed-accuracy trade-off was observed for MTx-%C from 72 to 94. CONCLUSIONS: GAMLSS is a reliable method to generate smoothed age- and education-specific percentile curves of MTx metrics, which may be adopted for mass screening and follow-ups addressing Alzheimer's disease or other cognitive diseases. HIGHLIGHTS: GAMLSS was applied to establish nonlinear percentile curves of cognitive decline. Subjects with a high level of education demonstrate a later onset and slower decline of cognition. Speed-accuracy trade-off effects were observed in a subgroup with moderate accuracy. MemTrax can be used as a mass-screen instrument for active cognition health management advice.

Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria.

Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the sample size. Further, we evaluate linear and non-linear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data. We created plausible ground truth distributions of hippocampal volumes in the age range of 45 to 80 years, as an example application. Based on these distributions we repeatedly simulated samples for sizes between 50 and 50,000 data points, and for each simulated sample we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1st, 5th, 10th) as these are the most clinically relevant. Our results quantify the expected decreasing trend in variance of the volume estimates with increasing sample size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large sample sizes. The uncertainty of model performance is substantial for what would often be considered large samples in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across sample sizes, especially for non-linear ground truth. Surprisingly large samples of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both bias and variance. Furthermore, caution is needed when attempting to go near the ends of the age range captured by the source data set and, as is a well known general principle, extrapolation beyond the age range should always be avoided. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.

Childhood to adulthood: Accounting for age dependence in healthy-reference distributions in 129 Xe gas-exchange MRI.

PURPOSE: Xenon-129 (129 Xe) gas-exchange MRI is a pulmonary-imaging technique that provides quantitative metrics for lung structure and function and is often compared to pulmonary-function tests. Unlike such tests, it does not normalize to predictive values based on demographic variables such as age. Many sites have alluded to an age dependence in gas-exchange metrics; however, a procedure for normalizing metrics has not yet been introduced. THEORY: We model healthy reference values for 129 Xe gas-exchange MRI against age using generalized additive models for location, scale, and shape (GAMLSS). GAMLSS takes signal data from an aggregated heathy-reference cohort and fits a distribution with flexible median, variation, skewness, and kurtosis to predict age-dependent centiles. This approach mirrors methods by the Global Lung Function Initiative for modeling pulmonary-function test data and applies it to binning methods widely used by the 129 Xe MRI community to interpret and quantify gas-exchange data. METHODS: Ventilation, membrane-uptake, red blood cell transfer, and red blood cell:membrane gas-exchange metrics were collected on 30 healthy subjects over an age range of 5 to 68 years. A GAMLSS model was fit against age and compared against widely used linear and generalized-linear binning 129 Xe MRI analysis schemes. RESULTS: All 4 gas-exchange metrics had significant skewness, and membrane-uptake had significant kurtosis compared to a normal distribution. Age has significant impact on distribution parameters. GAMLSS-binning produced narrower bins compared to the linear and generalized-linear binning schemes and distributed signal data closer to a normal distribution. CONCLUSION: The proposed "proof-of-concept" GAMLSS-binning approach can improve diagnostic accuracy of 129 Xe gas-exchange MRI by providing a means of modeling voxel distribution data against age.

Boosting distributional copula regression.

Capturing complex dependence structures between outcome variables (e.g., study endpoints) is of high relevance in contemporary biomedical data problems and medical research. Distributional copula regression provides a flexible tool to model the joint distribution of multiple outcome variables by disentangling the marginal response distributions and their dependence structure. In a regression setup, each parameter of the copula model, that is, the marginal distribution parameters and the copula dependence parameters, can be related to covariates via structured additive predictors. We propose a framework to fit distributional copula regression via model-based boosting, which is a modern estimation technique that incorporates useful features like an intrinsic variable selection mechanism, parameter shrinkage and the capability to fit regression models in high-dimensional data setting, that is, situations with more covariates than observations. Thus, model-based boosting does not only complement existing Bayesian and maximum-likelihood based estimation frameworks for this model class but rather enables unique intrinsic mechanisms that can be helpful in many applied problems. The performance of our boosting algorithm for copula regression models with continuous margins is evaluated in simulation studies that cover low- and high-dimensional data settings and situations with and without dependence between the responses. Moreover, distributional copula boosting is used to jointly analyze and predict the length and the weight of newborns conditional on sonographic measurements of the fetus before delivery together with other clinical variables.

Gender differences in the distribution of children's physical activity: evidence from nine countries.

BACKGROUND: Physical activity in childhood is thought to influences health and development. Previous studies have found that boys are typically more active than girls, yet the focus has largely been on differences in average levels or proportions above a threshold rather than the full distribution of activity across all intensities. We thus examined differences in the distribution of physical activity between girls and boys in a multi-national sample of children. METHODS: We used the harmonised International Children Accelerometry Database (ICAD), including waist-worn accelerometry data from 15,461 individuals (Boys: 48.3%) from 9 countries. Employing Generalised Additive Models of Location, Shape, and Scale (GAMLSS) we investigated gender differences in the distribution of individuals, including comparisons of variability (SD) and average physical activity levels (mean and median) and skewness. We conducted this analysis for each activity intensity (Sedentary, Light, and Moderate-to-Vigorous (MVPA)) and a summary measure (counts per minute (CPM)). RESULTS: Sizable gender differences in the distribution of activity were found for moderate to vigorous activity and counts per minute, with boys having higher average levels (38% higher mean volumes of MVPA, 20% higher CPM), yet substantially more between-person variability (30% higher standard deviation (SD) for MVPA, 17% higher SD for CPM); boys' distributions were less positively skewed than girls. Conversely, there was little to no difference between girls and boys in the distribution of sedentary or light-intensity activity. CONCLUSIONS: Inequality in activity between girls and boys was driven by MVPA. The higher mean volumes of MVPA in boys occurred alongside greater variability. This suggests a need to consider the underlying distribution of activity in future research; for example, interventions which target gender inequality in MVPA may inadvertently lead to increased inequality within girls.

Spatio-temporal trends of the age-at-menarche percentiles among Portuguese women since 1920.

BACKGROUND: This work aims to study the spatio-temporal evolution of a woman's age at menarche in the central region of Portugal. One of the concerns of the study is early or late menarches; thus, we consider percentile regression to build the respective curves as opposed to the more traditional mean regression approach. METHODS: We analysed the data from [Formula: see text] women born in the period 1920-1973 who attended a free breast cancer screening program between 1990 and 2019. Distributional regression models inside the package GAMLSS in R were considered. These methods allowed us not only to model the location (mean) of the specific probability distribution of the age at menarche, but also allowed for the scale (variance) parameter of this distribution to depend on covariates. Additionally, a spatial random-effect was considered in order to capture the correlation at the regional level. The obtained clustered spatial effects were analysed to assess geographical differences among the percentiles of the age at menarche by year of birth. RESULTS: A decreasing trend in the age at menarche (about 1.5 years in 5 decades) and regional differences for all the considered percentiles were found. Women living in the north-central areas of the central region of Portugal tend to have menarche at older ages. CONCLUSION: We obtained percentile estimates for the age at menarche by year of birth and region of residence and demonstrated that these two explanatory variables have an impact on the explanation about the decreasing trend in age at a woman's first menstruation.

Personalized Reference Values for the Two-Minute Walk Test: An Analysis of Cross-Sectional Data From the National Institutes of Health Toolbox Study.

OBJECTIVES: To develop reference values for the Two-Minute Walk Test (TMWT) via 2 previously untested methods: (1) smooth age-based statistical models and (2) a neighbors-based approach accounting for age, sex, and height. DESIGN: Cross-sectional observational study. SETTING: National Institutes of Health Toolbox study sites across the United States. PARTICIPANTS: A total of 1385 healthy, community dwelling adult participants (age 18-85 years) in the National Institutes of Health Toolbox study were included in this analysis. INTERVENTION: None. MAIN OUTCOME MEASURES: Reference values for TMWT were generated using 2 approaches: (1) Generalized Additive Models for Location Scale and Shape, wherein TMWT values were modeled as a smooth function of age, and (2) a semiparametric neighbors-based approach. The performance of references values was then adjudicated by examining precision (ie, the average interquartile or interdecile range of reference values), and coverage (ie, the proportion of realized values included within a given inter-percentile interval). Agreement between methods was examined by intraclass correlation coefficient. RESULTS: Neighbors-based reference values demonstrated a smaller average interquartile range (149 ft; 95% confidence interval [CI], 146-152 ft), compared with age-based reference values (158 ft; 95% CI, 155-162 ft), but similar average interdecile range (neighbors-based: 369 ft; 95% CI, 360-377 ft; age-based: 374 ft; 95% CI, 366-383 ft). Coverage appeared accurate via both approaches. Agreement between approaches was high (intraclass correlation coefficient=0.96), although differences were apparent on a case-by-case basis. CONCLUSIONS: Both age-based and neighbors-based reference values offer viable options for interpreting a person's TMWT performance. In this analysis, the neighbors-based approach (adjusting for height) yielded potentially clinically relevant differences in reference values for persons at extremes of height.

Distributional regression in clinical trials: treatment effects on parameters other than the mean.

BACKGROUND: The classical linear model is widely used in the analysis of clinical trials with continuous outcomes. However, required model assumptions are frequently not met, resulting in estimates of treatment effect that can be inefficient and biased. In addition, traditional models assess treatment effect only on the mean response, and not on other aspects of the response, such as the variance. Distributional regression modelling overcomes these limitations. The purpose of this paper is to demonstrate its usefulness for the analysis of clinical trials, and superior performance to that of traditional models. METHODS: Distributional regression models are demonstrated, and contrasted with normal linear models, on data from the LIPID randomized controlled trial, which compared the effects of pravastatin with placebo in patients with coronary heart disease. Systolic blood pressure (SBP) and the biomarker midregional pro-adrenomedullin (MR-proADM) were analysed. Treatment effect was estimated in models that used response distributions more appropriate than the normal (Box-Cox-t and Johnson's Su for MR-proADM and SBP, respectively), applied censoring below the detection limit of MR-proADM, estimated treatment effect on distributional parameters other than the mean, and included random effects for longitudinal observations. A simulation study was conducted to compare the performance of distributional regression models with normal linear regression, under conditions mimicking the LIPID study. The R package gamlss (Generalized Additive Models for Location, Scale and Shape), which implements maximum likelihood estimation for distributional regression modelling, was used throughout. RESULTS: In all cases the distributional regression models fit the data well, in contrast to poor fits obtained for traditional models; for MR-proADM a small but significant treatment effect on the mean was detected by the distributional regression model and not the normal model; and for SBP a beneficial treatment effect on the variance was demonstrated. In the simulation study distributional models strongly outperformed normal models when the response variable was non-normal and heterogeneous; and there was no disadvantage introduced by the use of distributional regression modelling when the response satisfied the normal linear model assumptions. CONCLUSIONS: Distributional regression models are a rich framework, largely untapped in the clinical trials world. We have demonstrated a sample of the capabilities of these models for the analysis of trials. If interest lies in accurate estimation of treatment effect on the mean, or other distributional features such as variance, the use of distributional regression modelling will yield superior estimates to traditional normal models, and is strongly recommended. TRIAL REGISTRATION: The LIPID trial was retrospectively registered on ANZCTR on 27/04/2016, registration number ACTRN12616000535471 .

Assessment of height growth in Indian children using growth centiles and growth curves.

BACKGROUND: Growth centiles and growth curves are two ways to present child anthropometry; however, they differ in the type of data used, the method of analysis, the biological parameters fitted and the form of interpretation. AIM: To fit and compare height growth centiles and curves in Indian children. SUBJECTS AND METHODS: 1468 children (796 boys) from Pune India aged 6-18 years with longitudinal data on age and height (n = 7781) were analysed using GAMLSS (Generalised Additive Models for Location Scale and Shape) for growth centiles, and SITAR (SuperImposition by Rotation and Translation) for growth curves. RESULTS: SITAR explained 98.7% and 98.8% of the height variance in boys and girls, with mean age at peak height velocity 13.1 and 11.0 years, and mean peak velocity 9.0 and 8.0 cm/year, respectively. GAMLSS (Box-Cox Cole Green model) also captured the pubertal growth spurt but the centiles were shallower than the SITAR mean curve. Boys showed a mid-growth spurt at age 8 years. CONCLUSION: GAMLSS displays the distribution of height in the population by age and sex, while SITAR effectively and parsimoniously summarises the pattern of height growth in individual children. The two approaches provide distinct, useful information about child growth.

Sex- and age-specific normative values for handgrip strength and components of the Senior Fitness Test in community-dwelling older adults aged 65-75 years in Germany: results from the OUTDOOR ACTIVE study.

BACKGROUND: Physical fitness is a key component of independent living and healthy ageing. For the measurement of physical fitness in older adults, the Senior Fitness Test is a commonly used tool. The objective of this study is to calculate sex- and age-specific normative values for handgrip strength and components of the Senior Fitness Test for older adults (65-75 years) in Germany. METHODS: Cross-sectional data of 1657 community-dwelling older adults residing in Bremen, Germany (53% female) were included in this study. Physical fitness was assessed using the following measurements of the Senior Fitness Test battery: 30s-chair stand test, 2 min-step test, sit-and-reach test, and back scratch test. In addition, handgrip strength was measured using a Saehan DHD-3 digital hand dynamometer SH1003. Sex- and age specific normative values were calculated for the 1st, 3rd, 10th, 25th, 50th, 75th, 90th, 97th, and 99th percentile using the GAMLSS method. RESULTS: The normative values show differences dependent on sex and age. For handgrip strength, the 30s-chair stand test and the 2 min-step test, normative values were higher for men, while women reached higher values in the sit-and-reach test and the back scratch test. For both, men and women, normative values declined with age. CONCLUSIONS: This study provides sex- and age-specific normative values for handgrip strength and components of the Senior Fitness Test for older adults in Germany. They might be useful for future research and for the application in practice.

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data.

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems.

A distributional regression approach to income-related inequality of health in Australia.

BACKGROUND: Several studies have confirmed the existence of a significant positive relationship between income and health. Conventional regression techniques such as Ordinary Least Squares only help identify the effect of the covariates on the mean of the health variable. In this way, important information of the income-health relationship could be overlooked. As an alternative, we apply and compare unconventional regression techniques. METHODS: We adopt a distributional approach because we want to allow the effect of income on health to vary according to people's health status. We start by analysing the income-health relationship using a distributional regression model that falls into the GAMLSS (Generalized Additive Models for Location, Scale and Shape) framework. We assume a gamma distribution to model the health variable and specify the parameters of this distribution as linear functions of a set of explanatory variables. For comparison, we also adopt a quantile regression analysis. Based on predicted health quantiles, we use both a parametric and a non-parametric approach to estimate the lower tail of the health distribution. RESULTS: Our data come from Wave 13 of the Household, Income and Labour Dynamics in Australia (HILDA) survey, collected in 2013-2014. According to GAMLSS, we find that the risk of ending up in poor, fair or average health is lower for those who have relatively high incomes ($80,000) than for those who have relatively low incomes ($20,000), for both smokers and non-smokers. In relative terms, the risk-lowering effect of income appears to be the largest for those who are in poor health, again for both smokers and non-smokers. The results obtained on the basis of quantile regression are to a large extent comparable to those obtained by means of GAMLSS regression. CONCLUSIONS: Both distributional regression techniques point in the direction of a non-uniform effect of income on health, and are therefore promising complements to conventional regression techniques as far as the analysis of the income-health relationship is concerned.

Modeling risks from natural hazards with generalized additive models for location, scale and shape.

We investigate a new framework for estimating the frequency and severity of losses associated with catastrophic risks such as bushfires, storms and floods. We explore generalized additive models for location, scale and shape (GAMLSS) for the quantification of regional risk factors - geographical, weather and climate variables - with the aim of better quantifying the frequency and severity of catastrophic losses from natural perils. Due to the flexibility of the GAMLSS approach, we find a superior fit to empirical loss data for the applied models in comparison to generalized linear regression models typically applied in the literature. In particular the generalized beta distribution of the second kind (GB2) provides a good fit to the severity of losses. Including covariates in the calibration of the scale parameter, we obtain vastly differently shaped distributions for the predicted individual losses at different levels of the covariates. Testing the GAMLSS approach in an out-of-sample validation exercise, we also find support for a correct specification of the estimated models. More accurate models for the losses from natural hazards will help state and local government policy development, in particular for risk management and scenario planning for emergency services with respect to these perils.

metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effects models.

BACKGROUND: The rapid growth of high-throughput sequencing-based microbiome profiling has yielded tremendous insights into human health and physiology. Data generated from high-throughput sequencing of 16S rRNA gene amplicons are often preprocessed into composition or relative abundance. However, reproducibility has been lacking due to the myriad of different experimental and computational approaches taken in these studies. Microbiome studies may report varying results on the same topic, therefore, meta-analyses examining different microbiome studies to provide consistent and robust results are important. So far, there is still a lack of implemented methods to properly examine differential relative abundances of microbial taxonomies and to perform meta-analysis examining the heterogeneity and overall effects across microbiome studies. RESULTS: We developed an R package 'metamicrobiomeR' that applies Generalized Additive Models for Location, Scale and Shape (GAMLSS) with a zero-inflated beta (BEZI) family (GAMLSS-BEZI) for analysis of microbiome relative abundance datasets. Both simulation studies and application to real microbiome data demonstrate that GAMLSS-BEZI well performs in testing differential relative abundances of microbial taxonomies. Importantly, the estimates from GAMLSS-BEZI are log (odds ratio) of relative abundances between comparison groups and thus are analogous between microbiome studies. As such, we also apply random effects meta-analysis models to pool estimates and their standard errors across microbiome studies. We demonstrate the meta-analysis examples and highlight the utility of our package on four studies comparing gut microbiomes between male and female infants in the first six months of life. CONCLUSIONS: GAMLSS-BEZI allows proper examination of microbiome relative abundance data. Random effects meta-analysis models can be directly applied to pool comparable estimates and their standard errors to evaluate the overall effects and heterogeneity across microbiome studies. The examples and workflow using our 'metamicrobiomeR' package are reproducible and applicable for the analyses and meta-analyses of other microbiome studies.

Gene expression variability: the other dimension in transcriptome analysis.

Transcriptome sequencing is a powerful technique to study molecular changes that underlie the differences in physiological conditions and disease progression. A typical question that is posed in such studies is finding genes with significant changes between sample groups. In this respect expression variability is regarded as a nuisance factor that is primarily of technical origin and complicates the data analysis. However, it is becoming apparent that the biological variation in gene expression might be an important molecular phenotype that can affect physiological parameters. In this review we explore the recent literature on technical and biological variability in gene expression, sources of expression variability, (epi-)genetic hallmarks, and evolutionary constraints in genes with robust and variable gene expression. We provide an overview of recent findings on effects of external cues, such as diet and aging, on expression variability and on other biological phenomena that can be linked to it. We discuss metrics and tools that were developed for quantification of expression variability and highlight the importance of future studies in this direction. To assist the adoption of expression variability analysis, we also provide a detailed description and computer code, which can easily be utilized by other researchers. We also provide a reanalysis of recently published data to highlight the value of the analysis method.

Analysis of paediatric visual acuity using Bayesian copula models with sinh-arcsinh marginal densities.

We analyse paediatric ophthalmic data from a large sample of children aged between 3 and 8 years. We use a Bayesian additive conditional bivariate copula regression model with sinh-arcsinh marginal densities with location, scale, and shape parameters that depend smoothly on a covariate. We perform Bayesian inference about the unknown quantities of our model using a specially tailored Markov chain Monte Carlo algorithm. We gain new insights about the processes, which determine transformations in visual acuity with respect to age, including the nature of joint changes in both eyes as modelled with the age-related copula dependence parameter. We analyse posterior predictive distributions to identify children with unusual sight characteristics, distinguishing those who are bivariate, but not univariate outliers. In this way, we provide an innovative tool that enables clinicians to identify children with unusual sight who may otherwise be missed. We compare our simultaneous Bayesian method with a two-step frequentist generalised additive modelling approach.

Neotropical ferns community phenology: climatic triggers in subtropical climate in Araucaria forest.

Climate regulates the fern phenology and climatic triggers influence plants from tropical and subtropical regions differently. Ferns depend on climate to regulate their life cycle, because they do not require animal interaction to reproduce. Through the pioneering study of the phenology of Araucaria forest understory in subtropical climate of Brazil, our main aims were (i) to verify which climatic variables influenced the phenological pattern of the community, (ii) to identify the differences in seasonality of ferns in distinct climatic zones of Brazil, and (iii) to compare the phenological pattern of ferns growing in other subtropical regions of the world. In an Araucaria forest fragment, we monitored the phenology of the fern community (leaf production, leaf senescence, and sporangium formation) over 2 years. At the same time, we collected photoperiod, temperature, and precipitation data. Ferns phenology was classified as continuous, discontinuous, regular, and irregular. Our results showed photoperiod and mean temperature as the best predictors for phenology. The reproductive event was seasonal, and the fern community presented themselves as continuous, irregular (activity index), and regular (intensity index) phenophases. Unlike ferns from tropical regions that generally regulate themselves by the rainfall, some ferns in a non-seasonal environment have seasonal behavior in their phenophases due to the greater amplitude of photoperiod and temperature. The community showed the same pattern of leaf production observed in populations of other subtropical regions in the world. This behavior represented the biological response of the vegetation dynamics in relation to the climatic variability of subtropical environment.

Improving confidence intervals for normed test scores: Include uncertainty due to sampling variability.

Test publishers usually provide confidence intervals (CIs) for normed test scores that reflect the uncertainty due to the unreliability of the tests. The uncertainty due to sampling variability in the norming phase is ignored. To express uncertainty due to norming, we propose a flexible method that is applicable in continuous norming and allows for a variety of score distributions, using Generalized Additive Models for Location, Scale, and Shape (GAMLSS; Rigby & Stasinopoulos, 2005). We assessed the performance of this method in a simulation study, by examining the quality of the resulting CIs. We varied the population model, procedure of estimating the CI, confidence level, sample size, value of the predictor, extremity of the test score, and type of variance-covariance matrix. The results showed that good quality of the CIs could be achieved in most conditions. The method is illustrated using normative data of the SON-R 6-40 test. We recommend test developers to use this approach to arrive at CIs, and thus properly express the uncertainty due to norm sampling fluctuations, in the context of continuous norming. Adopting this approach will help (e.g., clinical) practitioners to obtain a fair picture of the person assessed.

Growth in achondroplasia: Development of height, weight, head circumference, and body mass index in a European cohort.

As growth references for achondroplasia are limited to reports from United States, Japan, Argentina, and Australia, the aim of this study was to construct growth references for height, weight, head circumference, and body mass index (BMI) from a European cohort of children with achondroplasia and to discuss the development of these anthropometric variables. A mix of cross-sectional and longitudinal, retrospective, and prospective data from 466 children with achondroplasia and 4,375 measuring occasions were modeled with generalized additive model for location, scale and shape (GAMLSS) to sex-specific references for ages 0 to 20 years. Loss in height position, that is, reduction in height standard deviation scores, occurred mainly during first 2 years of life while pubertal growth seemed normal if related to adult height. Adult height was 132 cm in boys and 124 cm in girls with a variability comparable to that of the general population and seems to be remarkably similar in most studies of children with achondroplasia. BMI had a syndrome-specific development that was not comparable to BMI development in the general population. Weight and BMI might be misleading when evaluating, for example, metabolic health in achondroplasia. Head circumference reached adult head size earlier than in the general population. Increased tempo of head circumference growth necessitates thus close clinical follow-up during first postnatal years.