Browsing by Author "Zou, Haotian"

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    Application of Longitudinal Item Response Theory Models to Modeling Parkinson's Disease Progression.
    (CPT: pharmacometrics & systems pharmacology, 2022-07-27) Zou, Haotian; Aggarwal, Varun; Stebbins, Glenn T; Müller, Martijn LTM; Cedarbaum, Jesse M; Pedata, Anne; Stephenson, Diane; Simuni, Tanya; Luo, Sheng
    The Movement Disorder Society revised version of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Parts 2 and 3 reflect patient-reported functional impact and clinician-reported severity of motor signs of Parkinson's disease (PD), respectively. Total scores are common clinical outcomes but may obscure important time-based changes in items. We aim to analyze longitudinal disease progression based on MDS-UPRDS Parts 2 and 3 item-level responses over time and as functions of Hoehn & Yahr (H&Y) stages 1 and 2 for subjects with early PD. The longitudinal IRT modeling is a novel statistical method addressing limitations in traditional linear regression approaches such as ignoring varying item sensitivities and the sum score balancing out improvements and declines. We utilized a harmonized dataset consisting of six studies with 3,573 early PD subjects and 14,904 visits, and mean follow-up time of 2.5 year (±1.57). We applied both a unidimensional (each Part separately) and multidimensional (both Parts combined) longitudinal item response theory (IRT) models. We assessed the progression rates for both parts, anchored to baseline Hoehn & Yahr (H&Y) stages 1 and 2. Both the uni- and multidimensional longitudinal IRT models indicate significant worsening time effects in both Parts 2 and 3. Baseline H&Y stage 2 was associated with significantly higher baseline severities, but slower progression rates in both parts, as compared with stage 1. Patients with baseline H&Y stage 1 demonstrated slower progression in Part 2 severity compared to Part 3, while patients with baseline H&Y stage 2 progressed faster in Part 2 than Part 3. The multidimensional model had a superior fit compared to the unidimensional models and it had excellent model performance.
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    Assessing tilavonemab efficacy in early Alzheimer's disease via longitudinal item response theory modeling
    (Alzheimer's & Dementia: Translational Research & Clinical Interventions, 2024-04) Zhou, Xiaoxiao; Zou, Haotian; Lutz, Michael W; Arbeev, Konstantin; Akushevich, Igor; Yashin, Anatoli; Welsh-Bohmer, Kathleen A; Luo, Sheng
    AbstractINTRODUCTIONAlzheimer's disease (AD) is a neurodegenerative disorder characterized by declines in cognitive and functional severities. This research utilized the Clinical Dementia Rating (CDR) to assess the influence of tilavonemab on these deteriorations.METHODSLongitudinal Item Response Theory (IRT) models were employed to analyze CDR domains in early‐stage AD patients. Both unidimensional and multidimensional models were contrasted to elucidate the trajectories of cognitive and functional severities.RESULTSWe observed significant temporal increases in both cognitive and functional severities, with the cognitive severity deteriorating at a quicker rate. Tilavonemab did not demonstrate a statistically significant effect on the progression in either severity. Furthermore, a significant positive association was identified between the baselines and progression rates of both severities.DISCUSSIONWhile tilavonemab failed to mitigate impairment progression, our multidimensional IRT analysis illuminated the interconnected progression of cognitive and functional declines in AD, suggesting a comprehensive perspective on disease trajectories.Highlights Utilized longitudinal Item Response Theory (IRT) models to analyze the Clinical Dementia Rating (CDR) domains in early‐stage Alzheimer's disease (AD) patients, comparing unidimensional and multidimensional models. Observed significant temporal increases in both cognitive and functional severities, with cognitive severity deteriorating at a faster rate, while tilavonemab showed no statistically significant effect on either domain's progression. Found a significant positive association between the baseline severities and their progression rates, indicating interconnected progression patterns of cognitive and functional declines in AD. Introduced the application of multidimensional longitudinal IRT models to provide a comprehensive perspective on the trajectories of cognitive and functional severities in early AD, suggesting new avenues for future research including the inclusion of time‐dependent random effects and data‐driven IRT models.
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    Bayesian inference and dynamic prediction for multivariate longitudinal and survival data
    (The Annals of Applied Statistics, 2023-09-01) Zou, Haotian; Zeng, Donglin; Xiao, Luo; Luo, Sheng
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    Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease.
    (Statistics in medicine, 2021-10-14) Zou, Haotian; Li, Kan; Zeng, Donglin; Luo, Sheng; Alzheimer's Disease Neuroimaging Initiative
    Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
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    Co-Existent Probable RBD and PD: Disease Progression, Medication Response, and Clinical Trial Implications
    (Movement Disorders Clinical Practice, 2023-01-01) Zou, Haotian; Guo, Yuanyuan; Goetz, Christopher G; Mestre, Tiago A; Stebbins, Glenn T; Al-Hajraf, Falah; Lawton, Michael; Hu, Michele; Luo, Sheng
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    Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling.
    (Movement disorders : official journal of the Movement Disorder Society, 2022-09) Luo, Sheng; Zou, Haotian; Stebbins, Glenn T; Schwarzschild, Michael A; Macklin, Eric A; Chan, James; Oakes, David; Simuni, Tanya; Goetz, Christopher G; Parkinson Study Group SURE-PD3 Investigators

    Background

    Longitudinal item response theory (IRT) models previously suggested that the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor examination has two salient domains, tremor and nontremor, that progress in time and in response to treatment differently.

    Objective

    Apply longitudinal IRT modeling, separating tremor and nontremor domains, to reanalyze outcomes in the previously published clinical trial (Study of Urate Elevation in Parkinson's Disease, Phase 3) that showed no overall treatment effects.

    Methods

    We applied unidimensional and multidimensional longitudinal IRT models to MDS-UPDRS motor examination items in 298 participants with Parkinson's disease from the Study of Urate Elevation in Parkinson's Disease, Phase 3 (placebo vs. inosine) study. We separated 10 tremor items from 23 nontremor items and used Bayesian inference to estimate progression rates and sensitivity to treatment in overall motor severity and tremor and nontremor domains.

    Results

    The progression rate was faster in the tremor domain than the nontremor domain before levodopa treatment. Inosine treatment had no effect on either domain relative to placebo. Levodopa treatment was associated with greater slowing of progression in the tremor domain than the nontremor domain regardless of inosine exposure. Linear patterns of progression were observed. Despite different domain-specific progression patterns, tremor and nontremor severities at baseline and over time were significantly correlated.

    Conclusions

    Longitudinal IRT analysis is a novel statistical method addressing limitations of traditional linear regression approaches. It is particularly useful because it can simultaneously monitor changes in different, but related, domains over time and in response to treatment interventions. We suggest that in neurological diseases with distinct impairment domains, clinical or anatomical, this application may identify patterns of change unappreciated by standard statistical methods. © 2022 International Parkinson and Movement Disorder Society.
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    Multivariate functional mixed model with MRI data: An application to Alzheimer's disease.
    (Statistics in medicine, 2023-02) Zou, Haotian; Xiao, Luo; Zeng, Donglin; Luo, Sheng; Alzheimer's Disease Neuroimaging Initiative
    Alzheimer's Disease (AD) is the leading cause of dementia and impairment in various domains. Recent AD studies, (ie, Alzheimer's Disease Neuroimaging Initiative (ADNI) study), collect multimodal data, including longitudinal neurological assessments and magnetic resonance imaging (MRI) data, to better study the disease progression. Adopting early interventions is essential to slow AD progression for subjects with mild cognitive impairment (MCI). It is of particular interest to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions. In this article, we propose a multivariate functional mixed model with MRI data (MFMM-MRI) that simultaneously models longitudinal neurological assessments, baseline MRI data, and the survival outcome (ie, dementia onset) for subjects with MCI at baseline. Two functional forms (the random-effects model and instantaneous model) linking the longitudinal and survival process are investigated. We use Markov Chain Monte Carlo (MCMC) method based on No-U-Turn Sampling (NUTS) algorithm to obtain posterior samples. We develop a dynamic prediction framework that provides accurate personalized predictions of longitudinal trajectories and survival probability. We apply MFMM-MRI to the ADNI study and identify significant associations among longitudinal outcomes, MRI data, and the risk of dementia onset. The instantaneous model with voxels from the whole brain has the best prediction performance among all candidate models. The simulation study supports the validity of the estimation and dynamic prediction method.
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    Novel Approach to Movement Disorder Society–Unified Parkinson's Disease Rating Scale Monitoring in Clinical Trials: Longitudinal Item Response Theory Models
    (Movement Disorders Clinical Practice, 2021-01-01) Luo, Sheng; Zou, Haotian; Goetz, Christopher G; Choi, Dongrak; Oakes, David; Simuni, Tanya; Stebbins, Glenn T
    Background: Although nontremor and tremor Part 3 Movement Disorder Society–Unified Parkinson's Disease Rating Scale items measure different impairment domains, their distinct progression and drug responsivity remain unstudied longitudinally. The total score may obscure important time-based and treatment-based changes occurring in the individual domains. Objective: Using the unique advantages of item response theory (IRT), we developed novel longitudinal unidimensional and multidimensional models to investigate nontremor and tremor changes occurring in an interventional Parkinson's disease (PD) study. Method: With unidimensional longitudinal IRT, we assessed the 33 Part 3 item data (22 nontremor and 10 tremor items) of 336 patients with early PD from the STEADY-PD III (Safety, Tolerability, and Efficacy Assessment of Isradipine for PD, placebo vs. isradipine) study. With multidimensional longitudinal IRT, we assessed the progression rates over time and treatment (in overall motor severity, nontremor, and tremor domains) using Markov Chain Monte Carlo implemented in Stan. Results: Regardless of treatment, patients showed significant but different time-based deterioration rates for total motor, nontremor, and tremor scores. Isradipine was associated with additional significant deterioration over placebo in total score and nontremor scores, but not in tremor score. Further highlighting the 2 separate latent domains, nontremor and tremor severity changes were positively but weakly correlated (correlation coefficient, 0.108). Conclusions: Longitudinal IRT analysis is a novel statistical method highly applicable to PD clinical trials. It addresses limitations of traditional linear regression approaches and previous IRT investigations that either applied cross-sectional IRT models to longitudinal data or failed to estimate all parameters simultaneously. It is particularly useful because it can separate nontremor and tremor changes both over time and in response to treatment interventions.
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    Reply to: Comment on "Summing MDS-UPDRS Parts 1 + 2 (Non-motor and Motor Experience of Daily Living): The Patient's Voice".
    (Movement disorders : official journal of the Movement Disorder Society, 2023-08) Goetz, Christopher G; Zou, Haotian; Stebbins, Glenn T; Schrag, Anette; Mestre, Tiago A; Luo, Sheng
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    Summing MDS-UPDRS Parts 1 + 2 (Non-motor and Motor Experience of Daily Living): The Patient's Voice.
    (Movement disorders : official journal of the Movement Disorder Society, 2023-04) Zou, Haotian; Goetz, Christopher G; Stebbins, Glenn T; Schrag, Anette; Mestre, Tiago A; Luo, Sheng