Dataset Information

Variable-Domain Functional Regression for Modeling ICU Data.

ABSTRACT: We introduce a class of scalar-on-function regression models with subject-specific functional predictor domains. The fundamental idea is to consider a bivariate functional parameter that depends both on the functional argument and on the width of the functional predictor domain. Both parametric and nonparametric models are introduced to fit the functional coefficient. The nonparametric model is theoretically and practically invariant to functional support transformation, or support registration. Methods were motivated by and applied to a study of association between daily measures of the Intensive Care Unit (ICU) Sequential Organ Failure Assessment (SOFA) score and two outcomes: in-hospital mortality, and physical impairment at hospital discharge among survivors. Methods are generally applicable to a large number of new studies that record a continuous variables over unequal domains.

SUBMITTER: Gellar JE

PROVIDER: S-EPMC4315944 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Variable-Domain Functional Regression for Modeling ICU Data.

Gellar Jonathan E JE Colantuoni Elizabeth E Needham Dale M DM Crainiceanu Ciprian M CM

Journal of the American Statistical Association 20141201 508

We introduce a class of scalar-on-function regression models with subject-specific functional predictor domains. The fundamental idea is to consider a bivariate functional parameter that depends both on the functional argument and on the width of the functional predictor domain. Both parametric and nonparametric models are introduced to fit the functional coefficient. The nonparametric model is theoretically and practically invariant to functional support transformation, or support registration. ...[more]

PMID: 25663725

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Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT, RDX and HMX. One important goal of microarray experiments is to discover novel biomarker genes for quantitative phenotypic prediction. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can be used to quantitatively predict earthworm tissue residues of the explosives compounds that they were exposed to and took in from the HMX-spiked soil. Results: We collected a large microarray gene expression and earthworm tissue residue dataset. First, differentially expressed genes were identified for each exposure duration (4, 14 and 28 days). These genes were used in multivariate regression modeling for HMX residue prediction. Eighteen different regression models were tested and compared. The best performing model was able to achieve very high prediction accuracies with R2 values of 0.715, 0.728 and 0.822 for 4 days, 14 days and 28 days exposures, separately. Conclusions: This study demonstrated that multivariate regression coupled with high throughput microarray gene expression was a promising approach to quantitative phenotypic prediction. Adult earthworms (Eisenia fetida) were exposed in soil spiked with HMX (0, 8, 16, 32, 64, or 128 mg/kg) for 4, 14 or 28 days. Each treatment originally had 10 replicate worms with all 10 worms survived at the end of exposure. Total RNA was isolated from the surviving worms. A total of 120 worm RNA samples were hybridized to a custom-designed oligo array using AgilentM-bM-^@M-^Ys one-color Low RNA Input Linear Amplification Kit. The array contains 15,208 non-redundant 60-mer probes, each targeting a unique E. fetida transcript. After hybridization and scanning, gene expression data were acquired using AgilentM-bM-^@M-^Ys Feature Extraction Software (v.9.1.3). The 120-array dataset consists of three exposure groups (4 day, 14 day and 28 day) with each group having the following 8 treatments: day 0 control, blank control, solvent control, 8, 16, 32, 64, and 128 mg HMX/g soil. Each treatment had 5 biological replicates (i.e., five individual worms).

Dataset Information

Variable-Domain Functional Regression for Modeling ICU Data.

Publications

Variable-Domain Functional Regression for Modeling ICU Data.

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