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

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

The Cost of Autism Spectrum Disorders

Objective: A diagnosis of an autism spectrum disorders is usually associated with substantial lifetime costs to an individual, their family and the community. However, there remains an elusive factor in any cost-benefit analysis of ASD diagnosis, namely the cost of not obtaining a diagnosis. Given the infeasibility of estimating the costs of a population that, by its nature, is inaccessible, the current study compares expenses between families whose children received a formal ASD diagnosis immediately upon suspecting developmental atypicality and seeking advice, with families that experienced a delay between first suspicion and formal diagnosis. Design: A register based questionnaire study covering all families with a child with ASD in Western Australia. Participants: Families with one or more children diagnosed with an ASD, totalling 521 children diagnosed with an ASD; 317 records were able to be included in the final analysis.Results: The median family cost of ASD was estimated to be AUD $34,900 per annum with almost 90$29,200) due to loss of income from employment. For each additional symptom reported, approximately $1,400 cost for the family per annum was added. While there was little direct influence on costs associated with a delay in the diagnosis, the delay was associated with a modest increase in the number of ASD symptoms, indirectly impacting the cost of ASD. Conclusions: A delay in diagnosis was associated with an indirect increased financial burden to families. Early and appropriate access to early intervention is known to improve a child's long-term outcomes and reduce lifetime costs to the individual, family and society. Consequently, a per symptom dollar value may assist in allocation of individualised funding amounts for interventions rather than a nominal amount allocated to all children below a certain age, regardless of symptom presentation, as is the case in Western Australia

Integrated genomics of susceptibility to alkylator-induced leukemia in mice

<p>Abstract</p> <p>Background</p> <p>Therapy-related acute myeloid leukemia (t-AML) is a secondary, generally incurable, malignancy attributable to chemotherapy exposure. Although there is a genetic component to t-AML susceptibility in mice, the relevant loci and the mechanism(s) by which they contribute to t-AML are largely unknown. An improved understanding of susceptibility factors and the biological processes in which they act may lead to the development of t-AML prevention strategies.</p> <p>Results</p> <p>In this work we applied an integrated genomics strategy in inbred strains of mice to find novel factors that might contribute to susceptibility. We found that the pre-exposure transcriptional state of hematopoietic stem/progenitor cells predicts susceptibility status. More than 900 genes were differentially expressed between susceptible and resistant strains and were highly enriched in the apoptotic program, but it remained unclear which genes, if any, contribute directly to t-AML susceptibility. To address this issue, we integrated gene expression data with genetic information, including single nucleotide polymorphisms (SNPs) and DNA copy number variants (CNVs), to identify genetic networks underlying t-AML susceptibility. The 30 t-AML susceptibility networks we found are robust: they were validated in independent, previously published expression data, and different analytical methods converge on them. Further, the networks are enriched in genes involved in cell cycle and DNA repair (pathways not discovered in traditional differential expression analysis), suggesting that these processes contribute to t-AML susceptibility. Within these networks, the putative regulators (e.g., <it>Parp2</it>, <it>Casp9</it>, <it>Polr1b</it>) are the most likely to have a non-redundant role in the pathogenesis of t-AML. While identifying these networks, we found that current CNVR and SNP-based haplotype maps in mice represented distinct sources of genetic variation contributing to expression variation, implying that mapping studies utilizing either source alone will have reduced sensitivity.</p> <p>Conclusion</p> <p>The identification and prioritization of genes and networks not previously implicated in t-AML generates novel hypotheses on the biology and treatment of this disease that will be the focus of future research.</p

Contrast in Edge Vegetation Structure Modifies the Predation Risk of Natural Ground Nests in an Agricultural Landscape

Nest predation risk generally increases nearer forest-field edges in agricultural landscapes. However, few studies test whether differences in edge contrast (i.e. hard versus soft edges based on vegetation structure and height) affect edge-related predation patterns and if such patterns are related to changes in nest conspicuousness between incubation and nestling feeding. Using data on 923 nesting attempts we analyse factors influencing nest predation risk at different edge types in an agricultural landscape of a ground-cavity breeding bird species, the Northern Wheatear (Oenanthe oenanthe). As for many other bird species, nest predation is a major determinant of reproductive success in this migratory passerine. Nest predation risk was higher closer to woodland and crop field edges, but only when these were hard edges in terms of ground vegetation structure (clear contrast between tall vs short ground vegetation). No such edge effect was observed at soft edges where adjacent habitats had tall ground vegetation (crop, ungrazed grassland). This edge effect on nest predation risk was evident during the incubation stage but not the nestling feeding stage. Since wheatear nests are depredated by ground-living animals our results demonstrate: (i) that edge effects depend on edge contrast, (ii) that edge-related nest predation patterns vary across the breeding period probably resulting from changes in parental activity at the nest between the incubation and nestling feeding stage. Edge effects should be put in the context of the nest predator community as illustrated by the elevated nest predation risk at hard but not soft habitat edges when an edge is defined in terms of ground vegetation. These results thus can potentially explain previously observed variations in edge-related nest predation risk

Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection

Background When constructing new biomarker or gene signature scores for time-to-event outcomes, the underlying aims are to develop a discrimination model that helps to predict whether patients have a poor or good prognosis and to identify the most influential variables for this task. In practice, this is often done fitting Cox models. Those are, however, not necessarily optimal with respect to the resulting discriminatory power and are based on restrictive assumptions. We present a combined approach to automatically select and fit sparse discrimination models for potentially high-dimensional survival data based on boosting a smooth version of the concordance index (C-index). Due to this objective function, the resulting prediction models are optimal with respect to their ability to discriminate between patients with longer and shorter survival times. The gradient boosting algorithm is combined with the stability selection approach to enhance and control its variable selection properties. Results The resulting algorithm fits prediction models based on the rankings of the survival times and automatically selects only the most stable predictors. The performance of the approach, which works best for small numbers of informative predictors, is demonstrated in a large scale simulation study: C-index boosting in combination with stability selection is able to identify a small subset of informative predictors from a much larger set of non-informative ones while controlling the per-family error rate. In an application to discover biomarkers for breast cancer patients based on gene expression data, stability selection yielded sparser models and the resulting discriminatory power was higher than with lasso penalized Cox regression models. Conclusion The combination of stability selection and C-index boosting can be used to select small numbers of informative biomarkers and to derive new prediction rules that are optimal with respect to their discriminatory power. Stability selection controls the per-family error rate which makes the new approach also appealing from an inferential point of view, as it provides an alternative to classical hypothesis tests for single predictor effects. Due to the shrinkage and variable selection properties of statistical boosting algorithms, the latter tests are typically unfeasible for prediction models fitted by boosting

Behavioural and Developmental Interventions for Autism Spectrum Disorder: A Clinical Systematic Review

Background: Much controversy exists regarding the clinical efficacy of behavioural and developmental interventions for improving the core symptoms of autism spectrum disorders (ASD). We conducted a systematic review to summarize the evidence on the effectiveness of behavioural and developmental interventions for ASD. Methods and Findings: Comprehensive searches were conducted in 22 electronic databases through May 2007. Further information was obtained through hand searching journals, searching reference lists, databases of theses and dissertations, and contacting experts in the field. Experimental and observational analytic studies were included if they were written in English and reported the efficacy of any behavioural or developmental intervention for individuals with ASD. Two independent reviewers made the final study selection, extracted data, and reached consensus on study quality. Results were summarized descriptively and, where possible, meta-analyses of the study results were conducted. One-hundred-and-one studies at predominantly high risk of bias that reported inconsistent results across various interventions were included in the review. Meta-analyses of three controlled clinical trials showed that Lovaas treatment was superior to special education on measures of adaptive behaviour, communication and interaction, comprehensive language, daily living skills, expressive language, overall intellectual functioning and socialization. High-intensity Lovaas was superior to low-intensity Lovaas on measures of intellectual functioning in two retrospective cohort studies. Pooling the results of two randomized controlle