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JOINT MEAN AND COVARIANCE MODELING OF MULTIPLE HEALTH OUTCOME MEASURES.

ABSTRACT: Health exams determine a patient's health status by comparing the patient's measurement with a population reference range, a 95% interval derived from a homogeneous reference population. Similarly, most of the established relation among health problems are assumed to hold for the entire population. We use data from the 2009-2010 National Health and Nutrition Examination Survey (NHANES) on four major health problems in the U.S. and apply a joint mean and covariance model to study how the reference ranges and associations of those health outcomes could vary among subpopulations. We discuss guidelines for model selection and evaluation, using standard criteria such as AIC in conjunction with posterior predictive checks. The results from the proposed model can help identify subpopulations in which more data need to be collected to refine the reference range and to study the specific associations among those health problems.

SUBMITTER: Niu X

PROVIDER: S-EPMC6699761 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

JOINT MEAN AND COVARIANCE MODELING OF MULTIPLE HEALTH OUTCOME MEASURES.

Niu Xiaoyue X Hoff Peter D PD

The annals of applied statistics 20190301 1

Health exams determine a patient's health status by comparing the patient's measurement with a population reference range, a 95% interval derived from a homogeneous reference population. Similarly, most of the established relation among health problems are assumed to hold for the entire population. We use data from the 2009-2010 National Health and Nutrition Examination Survey (NHANES) on four major health problems in the U.S. and apply a joint mean and covariance model to study how the referenc ...[more]

PMID: 31428218

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Project description:Background and Purpose- The ability to model long-term functional outcomes after acute ischemic stroke represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club organization, a high capacity backbone system of brain function. Methods- In a hospital-based cohort of 41 acute ischemic stroke patients, we investigated the effect of acute infarcts on the brain's prestroke rich club backbone and poststroke functional connectomes with respect to poststroke outcome. Functional connectomes were created using 3 anatomic atlases, and characteristic path-length (L) was calculated for each connectome. The number of rich club regions affected were manually determined using each patient's diffusion weighted image. We investigated differences in L with respect to outcome (modified Rankin Scale score; 90 days) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including number of rich club regions and L in outcome models, using linear regression and assessing the explained variance (R2). Results- Of 41 patients (mean age [range]: 70 [45-89] years), 61% were male. Lower L was generally associated with better outcome. Including number of rich club regions in the backward selection models of outcome, R2 increased between 1.3- and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and modified Rankin Scale score. Conclusions- In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of poststroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.

Dataset Information

JOINT MEAN AND COVARIANCE MODELING OF MULTIPLE HEALTH OUTCOME MEASURES.

Publications

JOINT MEAN AND COVARIANCE MODELING OF MULTIPLE HEALTH OUTCOME MEASURES.

Similar Datasets

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