Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records.

In medical research, the accuracy of data from electronic medical records (EMRs) is critical, particularly when analyzing dense functional data, where anomalies can severely compromise research integrity. Anomalies in EMRs often arise from human errors in data measurement and entry, and increase in frequency with the volume of data. Despite the established methods in computer science, anomaly detection in medical applications remains underdeveloped.

Nonparametric predictive model for sparse and irregular longitudinal data.

We propose a kernel-based estimator to predict the mean response trajectory for sparse and irregularly measured longitudinal data. The kernel estimator is constructed by imposing weights based on the subject-wise similarity on L2 metric space between predictor trajectories, where we assume that an analogous fashion in predictor trajectories over time would result in a similar trend in the response trajectory among subjects. In order to deal with the curse of dimensionality caused by the multiple predictors, we propose an appealing multiplicative model with multivariate Gaussian kernels.

Impact of the Dopamine System on Long-Term Cognitive Impairment in Parkinson Disease: An Exploratory Study.

Background: Little is known about the impact of the dopamine system on development of cognitive impairment (CI) in Parkinson disease (PD).

Objectives: We used data from a multi-site, international, prospective cohort study to explore the impact of dopamine system-related biomarkers on CI in PD.

Methods: PD participants were assessed annually from disease onset out to 7 years, and CI determined by applying cut-offs to four measures: (1) Montreal Cognitive Assessment; (2) detailed neuropsychological test battery; (3) Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) cognition score; and (4) site investigator diagnosis of CI (mild cognitive impairment or dementia).

Metformin use history and genome-wide DNA methylation profile: potential molecular mechanism for aging and longevity.

Background: Metformin, a commonly prescribed anti-diabetic medication, has repeatedly been shown to hinder aging in pre-clinical models and to be associated with lower mortality for humans. It is, however, not well understood how metformin can potentially prolong lifespan from a biological standpoint. We hypothesized that metformin's potential mechanism of action for longevity is through its epigenetic modifications.

Sex-Related Longitudinal Change of Motor, Non-Motor, and Biological Features in Early Parkinson's Disease.

Background: Investigation of sex-related motor and non-motor differences and biological markers in Parkinson's disease (PD) may improve precision medicine approach.

Objective: To examine sex-related longitudinal changes in motor and non-motor features and biologic biomarkers in early PD.

Methods: We compared 5-year longitudinal changes in de novo, untreated PD men and women (at baseline N = 423; 65.

Low soluble amyloid-β 42 is associated with smaller brain volume in Parkinson's disease.

Introduction: We sought to examine whether levels of soluble alpha-synuclein (α-syn), amyloid-beta (Aβ42), phosphorylated tau (p-tau), and total tau (t-tau), as measured in cerebrospinal fluid (CSF), are associated with changes in brain volume in Parkinson's disease.

Methods: We assessed the 4-year change in total brain volume (n = 99) and baseline CSF α-syn, Aβ42, p-tau, and t-tau of Parkinson Progression Markers Initiative participants. We used linear mixed models to assess the longitudinal effect of baseline CSF biomarkers on total and regional brain volume and thickness as well as linear regression for cross-sectional analyses at baseline and year 2.

Predictive generalized varying-coefficient longitudinal model.

We propose a nonparametric bivariate varying coefficient generalized linear model to predict a mean response trajectory in the future given an individual's characteristics at present or an earlier time point in a longitudinal study. Given the measurement time of the predictors, the coefficients vary as functions of the future time over which the prediction of the mean response is concerned and illustrate the dynamic association between the future response and the earlier measured predictors. We use a nonparametric approach that takes advantage of features of both the kernel and the spline methods for estimation.

Mortality among patients with sepsis associated with a bispectral electroencephalography (BSEEG) score.

We have previously developed a bispectral electroencephalography (BSEEG) device, which was shown to be effective in detecting delirium and predicting patient outcomes. In this study we aimed to apply the BSEEG approach for a sepsis. This was a retrospective cohort study conducted at a single center.

Bispectral EEG (BSEEG) quantifying neuro-inflammation in mice induced by systemic inflammation: A potential mouse model of delirium.

Most of the animal studies using inflammation-induced cognitive change have relied on behavioral testing without objective and biologically solid methods to quantify the severity of cognitive disturbances. We have developed a bispectral EEG (BSEEG) method using a novel algorithm in clinical study. This method effectively differentiates between patients with and without delirium, and predict long-term mortality.

Clinical and dopamine transporter imaging characteristics of non-manifest LRRK2 and GBA mutation carriers in the Parkinson's Progression Markers Initiative (PPMI): a cross-sectional study.

Background: The Parkinson's Progression Markers Initiative (PPMI) is an ongoing observational, longitudinal cohort study of participants with Parkinson's disease, healthy controls, and carriers of the most common Parkinson's disease-related genetic mutations, which aims to define biomarkers of Parkinson's disease diagnosis and progression. All participants are assessed annually with a battery of motor and non-motor scales, 123-I Ioflupane dopamine transporter (DAT) imaging, and biological variables. We aimed to examine whether non-manifesting carriers of LRRK2 and GBA mutations have prodromal features of Parkinson's disease that correlate with reduced DAT binding.

Adjusting a subject-specific time of event in longitudinal studies.

Biomedical studies often involve an event that occurs to individuals at different times and has a significant influence on individual trajectories of response variables over time. We propose a statistical model to capture the mean trajectory alteration caused by not only the occurrence of the event but also the subject-specific time of the event. The proposed model provides a post-event mean trajectory smoothly connected with the pre-event mean trajectory by allowing the model parameters associated with the post-event mean trajectory to vary over time of the event.

Initial severity-dependent longitudinal model with application to a randomized controlled trial of women with depression.

The assessment of initial severity of a disease is arguably one of the most important factors in identifying appropriate therapies. In this paper, we propose an initial severity-dependent longitudinal model to account for the influence of the initial severity of a disease on the posttreatment severity and the efficacy of medical treatments. The proposed model has the flexibility of nonparametric modeling, as it allows coefficients to vary with the initial severity of the disease.

Statistical inference in a growth curve quantile regression model for longitudinal data.

This article describes a polynomial growth curve quantile regression model that provides a comprehensive assessment about the treatment effects on the changes of the distribution of outcomes over time. The proposed model has the flexibility, as it allows the degree of a polynomial to vary across quantiles. A high degree polynomial model fits the data adequately, yet it is not desirable due to the complexity of the model.