Assessing the Impact of Precision Parameter Prior in Bayesian Non-parametric Growth Curve Modeling.
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ABSTRACT:
SUBMITTER: Tong X
PROVIDER: S-EPMC8044365 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo assess the impact of using the recently published WHO growth standard, based on healthy, breastfed infants in multiple countries that excluded prematurely born infants, versus the Infant Health Development Program (IHDP) growth reference constructed from premature infants, on the interpretation of the growth of premature infants after hospital discharge.Study designA retrospective cohort was constructed of infants born at gestational age ≤35 weeks who initially presented for care at one of the 32 outpatient sites between 2006 and 2008 (N=2297). Kappa statistics measured overall agreement and agreement in ever classifying infants <5th percentile or ≥ 95th percentile for age between the WHO and IHDP. Logistic regression models identified factors associated with growth curve disagreement in classifying infants at the extremes of growth.ResultThe WHO and IHDP growth curves showed moderate agreement for all measurements (κ=0.40-0.52). When the curves disagreed on whether an infant was <5th percentile for weight (8.3% of cohort) or length (13.6% of cohort), the WHO curve classified the infant in this category over 90% of the time. For head circumference, the IHDP curve classified more infants below the 5th percentile. Gestational age <30 weeks was associated with growth curve disagreement for weight and length <5th percentile.ConclusionChoice of growth curve affects the assessment of growth and the classification of underweight status. Longitudinal studies are needed to determine which assessment identifies the greatest number of premature infants at risk for long-term growth issues.
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Project description:Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under-sampling and measurement errors and should be able to integrate multi-modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi-task learning based approach to modeling univariate trajectories across subjects that addresses these criteria. Our approach learns multiple subjects' trajectories within a single model that allows for different types of information sharing, that is, coupling, across subjects. It optimizes a combination of uncoupled, fully coupled and kernel coupled models. Kernel-based coupling allows linking subjects' trajectories based on one or more biomarker measures. We demonstrate this using Alzheimer's Disease Neuroimaging Initiative (ADNI) data, where we model longitudinal trajectories of MRI-derived cortical volumes in neurodegeneration, with coupling based on APOE genotype, cerebrospinal fluid (CSF) and amyloid PET-based biomarkers. In addition to detecting established disease effects, we detect disease related changes within the insula that have not received much attention within the literature. Due to its sensitivity in detecting disease effects, its competitive predictive performance and its ability to learn the optimal parameter covariance from data rather than choosing a specific set of random and fixed effects a priori, we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. A software implementation of the method is publicly available.
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Project description:PurposeThis study investigates deep-learning (DL) sequence modeling techniques to reliably fit dark adaptation (DA) curves and estimate their key parameters in patients with age-related macular degeneration (AMD) to improve robustness and curve predictions.MethodsA long-short-term memory autoencoder was used as the DL method to model the DA curve. The performance was compared against the classical nonlinear regression method using goodness-of-fit and repeatability metrics. Experiments were performed to predict the latter portion of the curve using data from early measurements. The prediction accuracy was quantified as the rod intercept time (RIT) prediction error between predicted and actual curves.ResultsThe two models had comparable goodness-of-fit measures, with root mean squared error (RMSE; SD) = 0.11 (0.04) log-units (LU) for the classical model and RMSE = 0.13 (0.06) LU for the DL model. Repeatability of the curve fits evaluated after introduction of random perturbations, and after performing repeated testing, demonstrated superiority of the DL method, especially among parameters related to cone decay. The DL method exhibited superior ability to predict the curve and RIT using points prior to -2 LU, with 3.1 ± 3.1 minutes RIT prediction error, compared to 19.1 ± 18.6 minutes RIT error for the classical method.ConclusionsThe parameters obtained from the DL method demonstrated superior robustness as well as predictability of the curve. These could provide important advances in using multiple DA curve parameters to characterize AMD severity.Translational relevanceDark adaptation is an important functional measure in studies of AMD and curve modeling using DL methods can lead to improved clinical trial end points.
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