Browse
Submit Data
Databases
API
Help

Dataset Information

15 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Nonparametric percentile curve estimation for a nonnegative marker with excessive zeros.

ABSTRACT: Norm curves for the head circumference, height, and weight of newborns and infants are widely known examples of percentile curves over age, and early accounts date back 50 years. The advent of the Agatston score for coronary calcification based on coronary computed tomography in 1990 heralded the era of a new marker in preventive medicine, in addition to well-known cardiovascular risk factors. A peculiarity of the nonnegative Agatston score in populations that are free of coronary artery disease is the overexpression of zeros. In a case study, we have demonstrated a nonparametric approach for percentile curve estimation using markers such as the Agatston score. This method is based on lowess smoothing of marker-positive scores on age, and the resulting percentile curves are subsequently transposed according to the estimated proportions of zeros. The approach does not involve any parametric assumptions, is robust against outliers, and fulfills the noncrossing property for percentile curves. A simulation study using samples of N=1,000, 2,000, 5,000, and 10,000 subjects illuminates the closeness of the estimated 50^th, 75^th, and 90^th percentile curves to the respective true curves, assuming an exponentially distributed marker and a proportion of zero scores that increase with age.•The method is applicable to highly skewed data and exemplified here with subgroup data of the referenced procedure.•The consistency and general performance of the method is shown by means of simulation.•The method is an explicit, transferable, and reproducible procedure that is applicable to a wide spectrum of markers and scores across various scientific disciplines, far beyond cardiovascular medicine.

SUBMITTER: Gerke O

PROVIDER: S-EPMC9241054 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

An Integrated Bayesian Nonparametric Approach for Stochastic and Variability Orders in ROC Curve Estimation: An Application to Endometriosis Diagnosis.

Project description:In estimating ROC curves of multiple tests, some a priori constraints may exist, either between the healthy and diseased populations within a test or between tests within a population. In this paper, we proposed an integrated modeling approach for ROC curves that jointly accounts for stochastic and variability orders. The stochastic order constrains the distributional centers of the diseased and healthy populations within a test, while the variability order constrains the distributional spreads of the tests within each of the populations. Under a Bayesian nonparametric framework, we used features of the Dirichlet process mixture to incorporate these order constraints in a natural way. We applied the proposed approach to data from the Physician Reliability Study that investigated the accuracy of diagnosing endometriosis using different clinical information. To address the issue of no gold standard in the real data, we used a sensitivity analysis approach that exploited diagnosis from a panel of experts. To demonstrate the performance of the methodology, we conducted simulation studies with varying sample sizes, distributional assumptions and order constraints. Supplementary materials for this article are available online.

| S-EPMC4733471 | biostudies-literature

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts.

Project description:We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.

| S-EPMC5513232 | biostudies-literature

High throughput nonparametric probability density estimation.

Project description:In high throughput applications, such as those found in bioinformatics and finance, it is important to determine accurate probability distribution functions despite only minimal information about data characteristics, and without using human subjectivity. Such an automated process for univariate data is implemented to achieve this goal by merging the maximum entropy method with single order statistics and maximum likelihood. The only required properties of the random variables are that they are continuous and that they are, or can be approximated as, independent and identically distributed. A quasi-log-likelihood function based on single order statistics for sampled uniform random data is used to empirically construct a sample size invariant universal scoring function. Then a probability density estimate is determined by iteratively improving trial cumulative distribution functions, where better estimates are quantified by the scoring function that identifies atypical fluctuations. This criterion resists under and over fitting data as an alternative to employing the Bayesian or Akaike information criterion. Multiple estimates for the probability density reflect uncertainties due to statistical fluctuations in random samples. Scaled quantile residual plots are also introduced as an effective diagnostic to visualize the quality of the estimated probability densities. Benchmark tests show that estimates for the probability density function (PDF) converge to the true PDF as sample size increases on particularly difficult test probability densities that include cases with discontinuities, multi-resolution scales, heavy tails, and singularities. These results indicate the method has general applicability for high throughput statistical inference.

| S-EPMC5947915 | biostudies-literature

Efficient estimation of Pareto model: Some modified percentile estimators.

Project description:The article proposes three modified percentile estimators for parameter estimation of the Pareto distribution. These modifications are based on median, geometric mean and expectation of empirical cumulative distribution function of first-order statistic. The proposed modified estimators are compared with traditional percentile estimators through a Monte Carlo simulation for different parameter combinations with varying sample sizes. Performance of different estimators is assessed in terms of total mean square error and total relative deviation. It is determined that modified percentile estimator based on expectation of empirical cumulative distribution function of first-order statistic provides efficient and precise parameter estimates compared to other estimators considered. The simulation results were further confirmed using two real life examples where maximum likelihood and moment estimators were also considered.

| S-EPMC5929531 | biostudies-literature

Age-specific percentile-based reference curve of serum procalcitonin concentrations in Japanese preterm infants.

Project description:Procalcitonin (PCT) levels are elevated early after birth in newborn infants; however, the physiological features and reference of serum PCT concentrations have not been fully studied in preterm infants. The aims of the current study were to establish an age-specific percentile-based reference curve of serum PCT concentrations in preterm infants and determine the features. The PCT concentration peaked in infants at 1 day old and decreased thereafter. At 1 day old, serum PCT concentrations in preterm infants <34 weeks' gestational age were higher than those in late preterm infants between 34 and 36 weeks' gestational age or term infants ≥37 weeks' gestational age. Although the 50-percentile value in late preterm and term infants reached the adult normal level (0.1 ng/mL) at 5 days old, it did not in preterm infants. It took 9 weeks for preterm infants to reach it. Serum PCT concentrations at onset in late-onset infected preterm infants were over the 95-percentile value. We showed that the physiological feature in preterm infants was significantly different from that in late preterm infants, even in those <37 weeks' gestational age. To detect late-onset bacterial infection and sepsis, an age-specific percentile-based reference curve may be useful in preterm infants.

| S-EPMC4817150 | biostudies-other

NONPARAMETRIC ESTIMATION OF MULTIVARIATE CONVEX-TRANSFORMED DENSITIES.

Project description:We study estimation of multivariate densities p of the form p(x) = h(g(x)) for x ? ?(d) and for a fixed monotone function h and an unknown convex function g. The canonical example is h(y) = e(-y) for y ? ?; in this case, the resulting class of densities [Formula: see text]is well known as the class of log-concave densities. Other functions h allow for classes of densities with heavier tails than the log-concave class.We first investigate when the maximum likelihood estimator p? exists for the class P(h) for various choices of monotone transformations h, including decreasing and increasing functions h. The resulting models for increasing transformations h extend the classes of log-convex densities studied previously in the econometrics literature, corresponding to h(y) = exp(y).We then establish consistency of the maximum likelihood estimator for fairly general functions h, including the log-concave class P(e(-y)) and many others. In a final section, we provide asymptotic minimax lower bounds for the estimation of p and its vector of derivatives at a fixed point x(0) under natural smoothness hypotheses on h and g. The proofs rely heavily on results from convex analysis.

| S-EPMC3058562 | biostudies-literature

A Nonparametric Prior for Simultaneous Covariance Estimation.

Project description:In the modeling of longitudinal data from several groups, appropriate handling of the dependence structure is of central importance. Standard methods include specifying a single covariance matrix for all groups or independently estimating the covariance matrix for each group without regard to the others, but when these model assumptions are incorrect, these techniques can lead to biased mean effects or loss of efficiency, respectively. Thus, it is desirable to develop methods to simultaneously estimate the covariance matrix for each group that will borrow strength across groups in a way that is ultimately informed by the data. In addition, for several groups with covariance matrices of even medium dimension, it is difficult to manually select a single best parametric model among the huge number of possibilities given by incorporating structural zeros and/or commonality of individual parameters across groups. In this paper we develop a family of nonparametric priors using the matrix stick-breaking process of Dunson et al. (2008) that seeks to accomplish this task by parameterizing the covariance matrices in terms of the parameters of their modified Cholesky decomposition (Pourahmadi, 1999). We establish some theoretic properties of these priors, examine their effectiveness via a simulation study, and illustrate the priors using data from a longitudinal clinical trial.

| S-EPMC3852937 | biostudies-literature

Bayesian estimation of rainfall dispersion in Thailand using gamma distribution with excess zeros.

Project description:The gamma distribution is commonly used to model environmental data. However, rainfall data often contain zero observations, which violates the assumption that all observations must be positive in a gamma distribution, and so a gamma model with excess zeros treated as a binary random variable is required. Rainfall dispersion is important and interesting, the confidence intervals for the variance of a gamma distribution with excess zeros help to examine rainfall intensity, which may be high or low risk. Herein, we propose confidence intervals for the variance of a gamma distribution with excess zeros by using fiducial quantities and parametric bootstrapping, as well as Bayesian credible intervals and highest posterior density intervals based on the Jeffreys', uniform, or normal-gamma-beta prior. The performances of the proposed confidence interval were evaluated by establishing their coverage probabilities and average lengths via Monte Carlo simulations. The fiducial quantity confidence interval performed the best for a small probability of the sample containing zero observations (δ) whereas the Bayesian credible interval based on the normal-gamma-beta prior performed the best for large δ. Rainfall data from the Kiew Lom Dam in Lampang province, Thailand, are used to illustrate the efficacies of the proposed methods in practice.

| S-EPMC9484466 | biostudies-literature

Probability machines: consistent probability estimation using nonparametric learning machines.

Project description:BackgroundMost machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem.ObjectivesThe aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities.MethodsTwo random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians.ResultsSimulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software.ConclusionsRandom forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

| S-EPMC3250568 | biostudies-literature

Robust and efficient estimation of nonparametric generalized linear models

Project description:Generalized linear models are flexible tools for the analysis of diverse datasets, but the classical formulation requires that the parametric component is correctly specified and the data contain no atypical observations. To address these shortcomings, we introduce and study a family of nonparametric full-rank and lower-rank spline estimators that result from the minimization of a penalized density power divergence. The proposed class of estimators is easily implementable, offers high protection against outlying observations and can be tuned for arbitrarily high efficiency in the case of clean data. We show that under weak assumptions, these estimators converge at a fast rate and illustrate their highly competitive performance on a simulation study and two real-data examples. Supplementary Information The online version contains supplementary material available at 10.1007/s11749-023-00866-x.

| S-EPMC10187526 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data