Dataset Information

Prediction intervals for future BMI values of individual children: a non-parametric approach by quantile boosting.

ABSTRACT:

Background

The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age.

Methods

We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data.

Results

The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child.

Conclusions

Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures.

SUBMITTER: Mayr A

PROVIDER: S-EPMC3292459 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction intervals for future BMI values of individual children: a non-parametric approach by quantile boosting.

Mayr Andreas A Hothorn Torsten T Fenske Nora N

BMC medical research methodology 20120125

<h4>Background</h4>The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age.<h4>Methods</h4>We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of co ...[more]

PMID: 22276940

Similar Datasets

Project description:BackgroundIdentification of bacterial taxa associated with diseases, exposures, and other variables of interest offers a more comprehensive understanding of the role of microbes in many conditions. However, despite considerable research in statistical methods for association testing with microbiome data, approaches that are generally applicable remain elusive. Classical tests often do not accommodate the realities of microbiome data, leading to power loss. Approaches tailored for microbiome data depend highly upon the normalization strategies used to handle differential read depth and other data characteristics, and they often have unacceptably high false positive rates, generally due to unsatisfied distributional assumptions. On the other hand, many non-parametric tests suffer from loss of power and may also present difficulties in adjusting for potential covariates. Most extant approaches also fail in the presence of heterogeneous effects. The field needs new non-parametric approaches that are tailored to microbiome data, robust to distributional assumptions, and powerful under heterogeneous effects, while permitting adjustment for covariates.MethodsAs an alternative to existing approaches, we propose a zero-inflated quantile approach (ZINQ), which uses a two-part quantile regression model to accommodate the zero inflation in microbiome data. For a given taxon, ZINQ consists of a valid test in logistic regression to model the zero counts, followed by a series of quantile rank-score based tests on multiple quantiles of the non-zero part with adjustment for the zero inflation. As a regression and quantile-based approach, the method is non-parametric and robust to irregular distributions, while providing an allowance for covariate adjustment. Since no distributional assumptions are made, ZINQ can be applied to data that has been processed under any normalization strategy.ResultsThorough simulations based on real data across a range of scenarios and application to real data sets show that ZINQ often has equivalent or higher power compared to existing tests even as it offers better control of false positives.ConclusionsWe present ZINQ, a quantile-based association test between microbiota and dichotomous or quantitative clinical variables, providing a powerful and robust alternative for the current microbiome differential abundance analysis. Video Abstract.

Project description:BackgroundCross-validation tools are used increasingly to validate and compare genetic evaluation methods but analytical properties of cross-validation methods are rarely described. There is also a lack of cross-validation tools for complex problems such as prediction of indirect effects (e.g. maternal effects) or for breeding schemes with small progeny group sizes.ResultsWe derive the expected value of several quadratic forms by comparing genetic evaluations including "partial" and "whole" data. We propose statistics that compare genetic evaluations including "partial" and "whole" data based on differences in means, covariance, and correlation, and term the use of these statistics "method LR" (from linear regression). Contrary to common belief, the regression of true on estimated breeding values is (on expectation) lower than 1 for small or related validation sets, due to family structures. For validation sets that are sufficiently large, we show that these statistics yield estimators of bias, slope or dispersion, and population accuracy for estimated breeding values. Similar results hold for prediction of future phenotypes although we show that estimates of bias, slope or dispersion using prediction of future phenotypes are sensitive to incorrect heritabilities or precorrection for fixed effects. We present an example for a set of 2111 Brahman beef cattle for which, in repeated partitioning of the data into training and validation sets, there is very good agreement of statistics of method LR with prediction of future phenotypes.ConclusionsAnalytical properties of cross-validation measures are presented. We present a new method named LR for cross-validation that is automatic, easy to use, and which yields the quantities of interest. The method compares predictions based on partial and whole data, which results in estimates of accuracy and biases. Prediction of observed records may yield biased results due to precorrection or use of incorrect heritabilities.

Project description:ObjectivesTo investigate the safety and immunogenicity of boosting BCG with modified vaccinia Ankara expressing antigen 85A (MVA85A), shortly after BCG vaccination, and to compare this first with the immunogenicity of BCG vaccination alone and second with a previous clinical trial where MVA85A was administered more than 10 years after BCG vaccination.DesignThere are two clinical trials reported here: a Phase I observational trial with MVA85A; and a Phase IV observational trial with BCG. These clinical trials were all conducted in the UK in healthy, HIV negative, BCG naïve adults. Subjects were vaccinated with BCG alone; or BCG and then subsequently boosted with MVA85A four weeks later (short interval). The outcome measures, safety and immunogenicity, were monitored for six months. The immunogenicity results from this short interval BCG prime-MVA85A boost trial were compared first with the BCG alone trial and second with a previous clinical trial where MVA85A vaccination was administered many years after vaccination with BCG.ResultsMVA85A was safe and highly immunogenic when administered to subjects who had recently received BCG vaccination. When the short interval trial data presented here were compared with the previous long interval trial data, there were no significant differences in the magnitude of immune responses generated when MVA85A was administered shortly after, or many years after BCG vaccination.ConclusionsThe clinical trial data presented here provides further evidence of the ability of MVA85A to boost BCG primed immune responses. This boosting potential is not influenced by the time interval between prior BCG vaccination and boosting with MVA85A. These findings have important implications for the design of efficacy trials with MVA85A. Boosting BCG induced anti-mycobacterial immunity in either infancy or adolescence are both potential applications for this vaccine, given the immunological data presented here.Trial registrationClinicalTrials.gov NCT00427453 (short boosting interval), NCT00427830 (long boosting interval), NCT00480714 (BCG alone).

Dataset Information

Prediction intervals for future BMI values of individual children: a non-parametric approach by quantile boosting.

Background

Methods

Results

Conclusions

Publications

Prediction intervals for future BMI values of individual children: a non-parametric approach by quantile boosting.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets