Hierarchical regression for epidemiologic analyses of multiple exposures.

Many epidemiologic investigations are designed to study the effects of multiple exposures. Most of these studies are analyzed either by fitting a risk-regression model with all exposures forced in the model, or by using a preliminary-testing algorithm, such as stepwise regression, to produce a smaller model. Research indicates that hierarchical modeling methods can outperform these conventional approaches. These methods are reviewed and compared to two hierarchical methods, empirical-Bayes regression and a variant here called "semi-Bayes" regression, to full-model maximum likelihood and to model reduction by preliminary testing. The performance of the methods in a problem of predicting neonatal-mortality rates are compared. Based on the literature to date, it is suggested that hierarchical methods should become part of the standard approaches to multiple-exposure studies

Analysis of High Dimensional Data from Intensive Care Medicine

As high dimensional data occur as a rule rather than an exception in critical care today, it is of utmost importance to improve acquisition, storage, modelling, and analysis of medical data, which appears feasable only with the help of bedside computers. The use of clinical information systems offers new perspectives of data recording and also causes a new challenge for statistical methodology. A graphical approach for analysing patterns in statistical time series from online monitoring systems in intensive care is proposed here as an example of a simple univariate method, which contains the possibility of a multivariate extension and which can be combined with procedures for dimension reduction

Funnel plots, performance variation and the Myocardial Infarction National Audit Project 2003–2004

BACKGROUND: Clinical governance requires health care professionals to improve standards of care and has resulted in comparison of clinical performance data. The Myocardial Infarction National Audit Project (a UK cardiology dataset) tabulates its performance. However funnel plots are the display method of choice for institutional comparison. We aimed to demonstrate that funnel plots may be derived from MINAP data and allow more meaningful interpretation of data. METHODS: We examined the attainment of National Service Framework standards for all hospitals (n = 230) and all patients (n = 99,133) in the MINAP database between 1(st )April 2003 and 31(st )March 2004. We generated funnel plots (with control limits at 3 sigma) of Door to Needle and Call to Needle thrombolysis times, and the use of aspirin, beta-blockers and statins post myocardial infarction. RESULTS: Only 87,427 patients fulfilled criteria for analysis of the use of secondary prevention drugs and 15,111 patients for analysis by Door to Needle and Call to Needle times (163 hospitals achieved the standards for Door to Needle times and 215 were within or above their control limits). One hundred and sixteen hospitals fell outside the 'within 25%' and 'more than 25%' standards for Call to Needle times, but 28 were below the lower control limits. Sixteen hospitals failed to reach the standards for aspirin usage post AMI and 24 remained below the lower control limits. Thirty hospitals were below the lower CL for beta-blocker usage and 49 outside the standard. Statin use was comparable. CONCLUSION: Funnel plots may be applied to a complex dataset and allow visual comparison of data derived from multiple health-care units. Variation is readily identified permitting units to appraise their practices so that effective quality improvement may take place

An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer

<p>Abstract</p> <p>Background</p> <p>The nuclear transcription factor estrogen receptor alpha (ER-alpha) is the target of several antiestrogen therapeutic agents for breast cancer. However, many ER-alpha positive patients do not respond to these treatments from the beginning, or stop responding after being treated for a period of time. Because of the association of gene transcription alteration and drug resistance and the emerging evidence on the role of DNA methylation on transcription regulation, understanding of these relationships can facilitate development of approaches to re-sensitize breast cancer cells to treatment by restoring DNA methylation patterns.</p> <p>Methods</p> <p>We constructed a hierarchical empirical Bayes model to investigate the simultaneous change of gene expression and promoter DNA methylation profiles among wild type (WT) and OHT/ICI resistant MCF7 breast cancer cell lines.</p> <p>Results</p> <p>We found that compared with the WT cell lines, almost all of the genes in OHT or ICI resistant cell lines either do not show methylation change or hypomethylated. Moreover, the correlations between gene expression and methylation are quite heterogeneous across genes, suggesting the involvement of other factors in regulating transcription. Analysis of our results in combination with H3K4me2 data on OHT resistant cell lines suggests a clear interplay between DNA methylation and H3K4me2 in the regulation of gene expression. For hypomethylated genes with alteration of gene expression, most (~80%) are up-regulated, consistent with current view on the relationship between promoter methylation and gene expression.</p> <p>Conclusions</p> <p>We developed an empirical Bayes model to study the association between DNA methylation in the promoter region and gene expression. Our approach generates both global (across all genes) and local (individual gene) views of the interplay. It provides important insight on future effort to develop therapeutic agent to re-sensitize breast cancer cells to treatment.</p

Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

<p>Abstract</p> <p>Background</p> <p>The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.</p> <p>Results</p> <p>Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.</p> <p>Conclusions</p> <p>Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.</p

Challenging the heterogeneity of disease presentation in malignant melanoma-impact on patient treatment

There is an increasing global interest to support research areas that can assist in understanding disease and improving patient care. The National Cancer Institute (NIH) has identified precision medicine-based approaches as key research strategies to expedite advances in cancer research. The Cancer Moonshot program ( https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative ) is the largest cancer program of all time, and has been launched to accelerate cancer research that aims to increase the availability of therapies to more patients and, ultimately, to eradicate cancer. Mass spectrometry-based proteomics has been extensively used to study the molecular mechanisms of cancer, to define molecular subtypes of tumors, to map cancer-associated protein interaction networks and post-translational modifications, and to aid in the development of new therapeutics and new diagnostic and prognostic tests. To establish the basis for our melanoma studies, we have established the Southern Sweden Malignant Melanoma Biobank. Tissues collected over many years have been accurately characterized with respect to the tumor and patient information. The extreme variability displayed in the protein profiles and the detection of missense mutations has confirmed the complexity and heterogeneity of the disease. It is envisaged that the combined analysis of clinical, histological, and proteomic data will provide patients with a more personalized medical treatment. With respect to disease presentation, targeted treatment and medical mass spectrometry analysis and imaging, this overview report will outline and summarize the current achievements and status within malignant melanoma. We present data generated by our cancer research center in Lund, Sweden, where we have built extensive capabilities in biobanking, proteogenomics, and patient treatments over an extensive time period

Biomass and morphology of fine roots in temperate broad-leaved forests differing in tree species diversity: is there evidence of below-ground overyielding?

Biodiversity effects on ecosystem functioning in forests have only recently attracted increasing attention. The vast majority of studies in forests have focused on above-ground responses to differences in tree species diversity, while systematic analyses of the effects of biodiversity on root systems are virtually non-existent. By investigating the fine root systems in 12 temperate deciduous forest stands in Central Europe, we tested the hypotheses that (1) stand fine root biomass increases with tree diversity, and (2) ‘below-ground overyielding’ of species-rich stands in terms of fine root biomass is the consequence of spatial niche segregation of the roots of different species. The selected stands represent a gradient in tree species diversity on similar bedrock from almost pure beech forests to medium-diverse forests built by beech, ash, and lime, and highly-diverse stands dominated by beech, ash, lime, maple, and hornbeam. We investigated fine root biomass and necromass at 24 profiles per stand and analyzed species differences in fine root morphology by microscopic analysis. Fine root biomass ranged from 440 to 480 g m−2 in the species-poor to species-rich stands, with 63–77% being concentrated in the upper 20 cm of the soil. In contradiction to our two hypotheses, the differences in tree species diversity affected neither stand fine root biomass nor vertical root distribution patterns. Fine root morphology showed marked distinctions between species, but these root morphological differences did not lead to significant differences in fine root surface area or root tip number on a stand area basis. Moreover, differences in species composition of the stands did not alter fine root morphology of the species. We conclude that ‘below-ground overyielding’ in terms of fine root biomass does not occur in the species-rich stands, which is most likely caused by the absence of significant spatial segregation of the root systems of these late-successional species