Statistical methods for river runoff prediction

Methods used to analyze one type of nonstationary stochastic processes?the periodically correlated process?are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.

COLOX: a new blood-based test for colorectal cancer screening

INTRODUCTION/OBJECTIVES: Detection rates for adenoma and early colorectal cancer (CRC) are insufficient due to low compliance towards invasive screening procedures, like colonoscopy.Available non-invasive screening tests have unfortunately low sensitivity and specificity performances.Therefore, there is a large unmet need calling for a cost-effective, reliable and non-invasive test to screen for early neoplastic and pre-neoplastic lesions AIMS & Methods: The objective is to develop a screening test able to detect early CRCs and adenomas.This test is based on a nucleic acids multi-gene assay performed on peripheral blood mononuclear cells (PBMCs).A colonoscopy-controlled feasibility study was conducted on 179 subjects.The first 92 subjects was used as training set to generate a statistical significant signature.Colonoscopy revealed 21 subjects with CRC,30 with adenoma bigger than 1 cm and 41 with no neoplastic or inflammatory lesions.The second group of 48 subjects (controls, CRC and polyps) was used as a test set and will be kept blinded for the entire data analysis.To determine the organ and disease specificity 38 subjects were used:24 with inflammatory bowel disease (IBD),14 with other cancers than CRC (OC).Blood samples were taken from each patient the day of the colonoscopy and PBMCs were purified. Total RNA was extracted following standard procedures.Multiplex RT-qPCR was applied on 92 different candidate biomarkers.Different univariate and multivariate statistical methods were applied on these candidates and among them 60 biomarkers with significant p-values (<0.01) were selected.These biomarkers are involved in several different biological functions as cellular movement,cell signaling and interaction,tissue and cellular development,cancer and cell growth and proliferation.Two distinct biomarker signatures are used to separate patients without lesion from those with cancer or with adenoma, named COLOX CRC and COLOX POL respectively.COLOX performances were validated using random resampling method, bootstrap. RESULTS: COLOX CRC and POL tests successfully separate patients without lesions from those with CRC (Se 67%,Sp 93%,AUC 0.87) and from those with adenoma bigger than 1cm (Se 63%,Sp 83%,AUC 0.77),respectively. 6/24 patients in the IBD group and 1/14 patients in the OC group have a positive COLOX CRC CONCLUSION: The two COLOX tests demonstrated a high sensitivity and specificity to detect the presence of CRCs and adenomas bigger than 1 cm.A prospective, multicenter, pivotal study is underway in order to confirm these promising results in a larger cohort.

Pathological Reorganization of NMDA Receptors Subunits and Postsynaptic Protein PSD-95 Distribution in Alzheimer's Disease.

In Alzheimer's disease (AD), synaptic alterations play a major role and are often correlated with cognitive changes. In order to better understand synaptic modifications, we compared alterations in NMDA receptors and postsynaptic protein PSD-95 expression in the entorhinal cortex (EC) and frontal cortex (FC; area 9) of AD and control brains. We combined immunohistochemical and image analysis methods to quantify on consecutive sections the distribution of PSD-95 and NMDA receptors GluN1, GluN2A and GluN2B in EC and FC from 25 AD and control cases. The density of stained receptors was analyzed using multivariate statistical methods to assess the effect of neurodegeneration. In both regions, the number of neuronal profiles immunostained for GluN1 receptors subunit and PSD-95 protein was significantly increased in AD compared to controls (3-6 fold), while the number of neuronal profiles stained for GluN2A and GluN2B receptors subunits was on the contrary decreased (3-4 fold). The increase in marked neuronal profiles was more prominent in a cortical band corresponding to layers 3 to 5 with large pyramidal cells. Neurons positive for GluN1 or PSD-95 staining were often found in the same localization on consecutive sections and they were also reactive for the anti-tau antibody AD2, indicating a neurodegenerative process. Differences in the density of immunoreactive puncta representing neuropile were not statistically significant. Altogether these data indicate that GluN1 and PSD-95 accumulate in the neuronal perikarya, but this is not the case for GluN2A and GluN2B, while the neuropile compartment is less subject to modifications. Thus, important variations in the pattern of distribution of the NMDA receptors subunits and PSD-95 represent a marker in AD and by impairing the neuronal network, contribute to functional deterioration.

A new blood-based screening test for colorectal cancer: a pilot study

Background: Colorectal cancer (CRC) can be cured when diagnosed in its early or precancerous (adenoma) stages. Mostly due to poor compliance towards invasive screening procedures, detection rates for adenoma and early CRCs are still low. Available non-invasive screening tests have unfortunately low sensitivity and specificity performances. Therefore, there is a large unmet need calling for a cost-effective, reliable and non-invasive test to screen for early neoplastic and pre-neoplastic lesions. Objective: To develop a routine screening test based on a nucleic acids multi-gene assay performed on peripheral blood mononuclear cells (PBMCs) that can detect early CRCs and adenomas. Methods: 116 patients (mean age: 55 years; range: 18 to 74 years; female/male ration 0.98) were included in this pilot, nonblinded, colonoscopy-controlled study. Colonoscopy revealed 21 patients with CRC, 30 patients with adenoma bigger than 1 cm, 24 patients with inflammatory bowel disease (IBD) and 41 patients had no neoplastic or inflammatory lesions. Blood samples were taken from each patient the day of the colonoscopy and PBMCs were purified. Total RNA was extracted following standard procedures. Multiplex RT-qPCR was applied on 92 different candidate biomarkers. Different univariate and multivariate statistical methods were applied on these candidates, and among them, 57 biomarkers with significant p values (<0.01, Wilcoxon test) were selected, including ADAMTS1, MMP9, CXCL10, CXCR4, VEGFA and CDH1. Two distinct biomarker signatures are used to separate patients without neoplastic lesion from those with cancer (named COLOX 1 test), respectively from those with adenoma (named COLOX 2 test). Result: COLOX 1 and 2 tests have successfully separated patients without neoplastic lesion from those with CRC (sensitivity 70%, specificity 90%, AUC 0.88), respectively from those with adenoma bigger than 1cm (sensitivity 61%, specificity 80%, AUC 0.80). 6/24 patients in the IBD group have a positive COLOX 1 test. Conclusion: These two COLOX tests demonstrated an acceptable sensitivity and a high specificity to detect the presence of CRCs and adenomas bigger than 1 cm. The false positives COLOX 1 test in IBD patients could possibly be due to the chronic inflammatory state. A prospective, multicenter, pivotal study is underway in order to confirm these promising results in a larger cohort.

Two Signatures For A New Blood-Based Test For Colorectal Cancer Screening

Background: Detection rates for adenoma and early colorectal cancer (CRC) are unsatisfactory due to low compliance towards invasive screening procedures such as colonoscopy. There is a large unmet screening need calling for an accurate, non-invasive and cost-effective test to screen for early neoplastic and pre-neoplastic lesions. Our goal is to identify effective biomarker combinations to develop a screening test aimed at detecting precancerous lesions and early CRC stages, based on a multigene assay performed on peripheral blood mononuclear cells (PBMC).Methods: A pilot study was conducted on 92 subjects. Colonoscopy revealed 21 CRC, 30 adenomas larger than 1 cm and 41 healthy controls. A panel of 103 biomarkers was selected by two approaches: a candidate gene approach based on literature review and whole transcriptome analysis of a subset of this cohort by Illumina TAG profiling. Blood samples were taken from each patient and PBMC purified. Total RNA was extracted and the 103 biomarkers were tested by multiplex RT-qPCR on the cohort. Different univariate and multivariate statistical methods were applied on the PCR data and 60 biomarkers, with significant p-value (< 0.01) for most of the methods, were selected.Results: The 60 biomarkers are involved in several different biological functions, such as cell adhesion, cell motility, cell signaling, cell proliferation, development and cancer. Two distinct molecular signatures derived from the biomarker combinations were established based on penalized logistic regression to separate patients without lesion from those with CRC or adenoma. These signatures were validated using bootstrapping method, leading to a separation of patients without lesion from those with CRC (Se 67%, Sp 93%, AUC 0.87) and from those with adenoma larger than 1cm (Se 63%, Sp 83%, AUC 0.77). In addition, the organ and disease specificity of these signatures was confirmed by means of patients with other cancer types and inflammatory bowel diseases.Conclusions: The two defined biomarker combinations effectively detect the presence of CRC and adenomas larger than 1 cm with high sensitivity and specificity. A prospective, multicentric, pivotal study is underway in order to validate these results in a larger cohort.

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Ecology and life history affect different aspects of the population structure of 27 high-alpine plants.

A plant species' genetic population structure is the result of a complex combination of its life history, ecological preferences, position in the ecosystem and historical factors. As a result, many different statistical methods exist that measure different aspects of species' genetic structure. However, little is known about how these methods are interrelated and how they are related to a species' ecology and life history. In this study, we used the IntraBioDiv amplified fragment length polymorphisms data set from 27 high-alpine species to calculate eight genetic summary statistics that we jointly correlate to a set of six ecological and life-history traits. We found that there is a large amount of redundancy among the calculated summary statistics and that there is a significant association with the matrix of species traits. In a multivariate analysis, two main aspects of population structure were visible among the 27 species. The first aspect is related to the species' dispersal capacities and the second is most likely related to the species' postglacial recolonization of the Alps. Furthermore, we found that some summary statistics, most importantly Mantel's r and Jost's D, show different behaviour than expected based on theory. We therefore advise caution in drawing too strong conclusions from these statistics.

Les traces de pneumatiques en sciences forensiques : de l'analyse chimique à l'évaluation de la force probante

This work is focused on the development of a methodology for the use of chemical characteristic of tire traces to help answer the following question: "Is the offending tire at the origin of the trace found on the crime scene?". This methodology goes from the trace sampling on the road to statistical analysis of its chemical characteristics. Knowledge about the composition and manufacture of tread tires as well as a review of instrumental techniques used for the analysis of polymeric materials were studied to select, as an ansi vi cal technique for this research, pyrolysis coupled to a gas Chromatograph with a mass spectrometry detector (Py-GC/MS). An analytical method was developed and optimized to obtain the lowest variability between replicates of the same sample. Within-variability of the tread was evaluated regarding width and circumference with several samples taken from twelve tires of different brands and/or models. The variability within each of the treads (within-variability) and between the treads (between-variability) could be quantified. Different statistical methods have shown that within-variability is lower than between-variability, which helped differentiate these tires. Ten tire traces were produced with tires of different brands and/or models by braking tests. These traces have been adequately sampled using sheets of gelatine. Particles of each trace were analysed using the same methodology as for the tires at their origin. The general chemical profile of a trace or of a tire has been characterized by eighty-six compounds. Based on a statistical comparison of the chemical profiles obtained, it has been shown that a tire trace is not differentiable from the tire at its origin but is generally differentiable from tires that are not at its origin. Thereafter, a sample containing sixty tires was analysed to assess the discrimination potential of the developed methodology. The statistical results showed that most of the tires of different brands and models are differentiable. However, tires of the same brand and model with identical characteristics, such as country of manufacture, size and DOT number, are not differentiable. A model, based on a likelihood ratio approach, was chosen to evaluate the results of the comparisons between the chemical profiles of the traces and tires. The methodology developed was finally blindly tested using three simulated scenarios. Each scenario involved a trace of an unknown tire as well as two tires possibly at its origin. The correct results for the three scenarios were used to validate the developed methodology. The different steps of this work were useful to collect the required information to test and validate the underlying assumption that it is possible to help determine if an offending tire » or is not at the origin of a trace, by means of a statistical comparison of their chemical profile. This aid was formalized by a measure of the probative value of the evidence, which is represented by the chemical profile of the trace of the tire. - Ce travail s'est proposé de développer une méthodologie pour l'exploitation des caractéristiques chimiques des traces de pneumatiques dans le but d'aider à répondre à la question suivante : «Est-ce que le pneumatique incriminé est ou n'est pas à l'origine de la trace relevée sur les lieux ? ». Cette méthodologie s'est intéressée du prélèvement de la trace de pneumatique sur la chaussée à l'exploitation statistique de ses caractéristiques chimiques. L'acquisition de connaissances sur la composition et la fabrication de la bande de roulement des pneumatiques ainsi que la revue de techniques instrumentales utilisées pour l'analyse de matériaux polymériques ont permis de choisir, comme technique analytique pour la présente recherche, la pyrolyse couplée à un chromatographe en phase gazeuse avec un détecteur de spectrométrie de masse (Py-GC/MS). Une méthode analytique a été développée et optimisée afin d'obtenir la plus faible variabilité entre les réplicas d'un même échantillon. L'évaluation de l'intravariabilité de la bande de roulement a été entreprise dans sa largeur et sa circonférence à l'aide de plusieurs prélèvements effectués sur douze pneumatiques de marques et/ou modèles différents. La variabilité au sein de chacune des bandes de roulement (intravariabilité) ainsi qu'entre les bandes de roulement considérées (intervariabilité) a pu être quantifiée. Les différentes méthodes statistiques appliquées ont montré que l'intravariabilité est plus faible que l'intervariabilité, ce qui a permis de différencier ces pneumatiques. Dix traces de pneumatiques ont été produites à l'aide de pneumatiques de marques et/ou modèles différents en effectuant des tests de freinage. Ces traces ont pu être adéquatement prélevées à l'aide de feuilles de gélatine. Des particules de chaque trace ont été analysées selon la même méthodologie que pour les pneumatiques à leur origine. Le profil chimique général d'une trace de pneumatique ou d'un pneumatique a été caractérisé à l'aide de huitante-six composés. Sur la base de la comparaison statistique des profils chimiques obtenus, il a pu être montré qu'une trace de pneumatique n'est pas différenciable du pneumatique à son origine mais est, généralement, différenciable des pneumatiques qui ne sont pas à son origine. Par la suite, un échantillonnage comprenant soixante pneumatiques a été analysé afin d'évaluer le potentiel de discrimination de la méthodologie développée. Les méthodes statistiques appliquées ont mis en évidence que des pneumatiques de marques et modèles différents sont, majoritairement, différenciables entre eux. La méthodologie développée présente ainsi un bon potentiel de discrimination. Toutefois, des pneumatiques de la même marque et du même modèle qui présentent des caractéristiques PTD (i.e. pays de fabrication, taille et numéro DOT) identiques ne sont pas différenciables. Un modèle d'évaluation, basé sur une approche dite du likelihood ratio, a été adopté pour apporter une signification au résultat des comparaisons entre les profils chimiques des traces et des pneumatiques. La méthodologie mise en place a finalement été testée à l'aveugle à l'aide de la simulation de trois scénarios. Chaque scénario impliquait une trace de pneumatique inconnue et deux pneumatiques suspectés d'être à l'origine de cette trace. Les résultats corrects obtenus pour les trois scénarios ont permis de valider la méthodologie développée. Les différentes étapes de ce travail ont permis d'acquérir les informations nécessaires au test et à la validation de l'hypothèse fondamentale selon laquelle il est possible d'aider à déterminer si un pneumatique incriminé est ou n'est pas à l'origine d'une trace, par le biais d'une comparaison statistique de leur profil chimique. Cette aide a été formalisée par une mesure de la force probante de l'indice, qui est représenté par le profil chimique de la trace de pneumatique.

Urinary oxalate and urate to creatinine ratios in a healthy pediatric population.

The purpose of the study was to determine reference percentiles for the urinary (U) oxalate (Ox) and urate (Ura) to creatinine (Cr) concentration ratios in the second morning urine of healthy infants, children, and adolescents. The urinary oxalate and urate to creatinine ratios were determined in the spontaneously voided second morning urine sample. To test reproducibility, two urine samples were analyzed on 2 consecutive weeks in 63% of the subjects. Three hundred eighty-four healthy children (181 girls, 203 boys), aged 1 month to 17 years, from nurseries, kindergartens, and schools of Lausanne, Switzerland, were studied. The 5th and 95th percentiles were determined from the total number of urine samples (627) after confirmation that there was no order effect between repeated measurements and there were no significant sex differences. A nonlinear regression analysis in terms of age was used to smooth the calculated percentiles. In this manner, curves were obtained from which the reference values can be read at any given age. The 95th percentiles decreased with age: for UOx/Cr from 0.175 mg/mg (0.22 mol/mol) at 1 to 6 months to 0.048 mg/mg (0.06 mol/mol) from 7 years and beyond; and UUra/Cr from 2.378 mg/mg (1.6 mol/mol) at 1 to 6 months to 0.594 mg/mg (0.4 mol/mol) in adolescence. We provide 5th and 95th percentile curves for the UOx/Cr and UUra/Cr ratios determined from the second morning urine samples in a large cohort of healthy infants, children, and adolescents. Values were determined by standard analytical chemical techniques and were analyzed by powerful statistical methods. The calculated 95th percentile for the UOx/Cr values fell rather rapidly and reached normal adult values by the age of 7 years, whereas for UUra/Cr, the 95th percentile decreased slowly and stabilized in adolescence.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study.

OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.

Ultra-high pressure liquid chromatography-mass spectrometry for plant metabolomics: a systematic comparison of high-resolution quadrupole-time-of-flight and single stage Orbitrap mass spectrometers.

The response of Arabidopsis to stress caused by mechanical wounding was chosen as a model to compare the performances of high resolution quadrupole-time-of-flight (Q-TOF) and single stage Orbitrap (Exactive Plus) mass spectrometers in untargeted metabolomics. Both instruments were coupled to ultra-high pressure liquid chromatography (UHPLC) systems set under identical conditions. The experiment was divided in two steps: the first analyses involved sixteen unwounded plants, half of which were spiked with pure standards that are not present in Arabidopsis. The second analyses compared the metabolomes of mechanically wounded plants to unwounded plants. Data from both systems were extracted using the same feature detection software and submitted to unsupervised and supervised multivariate analysis methods. Both mass spectrometers were compared in terms of number and identity of detected features, capacity to discriminate between samples, repeatability and sensitivity. Although analytical variability was lower for the UHPLC-Q-TOF, generally the results for the two detectors were quite similar, both of them proving to be highly efficient at detecting even subtle differences between plant groups. Overall, sensitivity was found to be comparable, although the Exactive Plus Orbitrap provided slightly lower detection limits for specific compounds. Finally, to evaluate the potential of the two mass spectrometers for the identification of unknown markers, mass and spectral accuracies were calculated on selected identified compounds. While both instruments showed excellent mass accuracy (<2.5ppm for all measured compounds), better spectral accuracy was recorded on the Q-TOF. Taken together, our results demonstrate that comparable performances can be obtained at acquisition frequencies compatible with UHPLC on Q-TOF and Exactive Plus MS, which may thus be equivalently used for plant metabolomics.

Assessing the Relationship between the Baseline Value of a Continuous Variable and Subsequent Change over Time

Analyzing the relationship between the baseline value and subsequent change of a continuous variable is a frequent matter of inquiry in cohort studies. These analyses are surprisingly complex, particularly if only two waves of data are available. It is unclear for non-biostatisticians where the complexity of this analysis lies and which statistical method is adequate.With the help of simulated longitudinal data of body mass index in children,we review statistical methods for the analysis of the association between the baseline value and subsequent change, assuming linear growth with time. Key issues in such analyses are mathematical coupling, measurement error, variability of change between individuals, and regression to the mean. Ideally, it is better to rely on multiple repeated measurements at different times and a linear random effects model is a standard approach if more than two waves of data are available. If only two waves of data are available, our simulations show that Blomqvist's method - which consists in adjusting for measurement error variance the estimated regression coefficient of observed change on baseline value - provides accurate estimates. The adequacy of the methods to assess the relationship between the baseline value and subsequent change depends on the number of data waves, the availability of information on measurement error, and the variability of change between individuals.