Joint model of multiple longitudinal measures and a binary outcome: An application to predict orthostatic hypertension for subacute stroke patients

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105822

Title:	Joint model of multiple longitudinal measures and a binary outcome: An application to predict orthostatic hypertension for subacute stroke patients
Authors:	曾議寬;Hwang, Yi-Ting;Wang, Chun-Chao;Wang, Chiuan He;Tseng, Yi-Kuan;Chang, Yeu-Jhy
Contributors:	理學院統計研究所
Keywords:	Biometry - methods;Blood Pressure;General Medicine;Heart Rate;Humans;Hypertension;Hypertension - complications;Hypertension - diagnosis;Hypertension - physiopathology;Joint model;Logistic regression;Longitudinal Studies;Models, Statistical;Monte Carlo Method;Monte Carlo simulation;Multiple longitudinal measures;Orthostatic hypertension;Prognosis;Random effect model;Receiving operating characteristic curve;ROC Curve;Statistics and Probability;Statistics, Probability and Uncertainty;Stroke;Stroke - complications
Date:	2015-01-01
Issue Date:	2026-04-23 12:55:15 (UTC+8)
Publisher:	Wiley-VCH Verlag;Germany: Blackwell Publishing Ltd
Abstract:	摘要： Stroke patients with orthostatic hypertensive responses that are one of the blood pressure regulation problems can easily fall down while doing rehabilitation, which may result in prolonged hospitalization and delayed treatment and recovery. This may result in increasing the medical cost and burden. In turn, developing a diagnostic test for the orthostatic hypertension (OH) is clinically important for patients who are suffering from stroke. Clinically, a patient needs to have a tilt testing that requires measuring the change of blood pressures and heart rate at all angles to determine whether a stroke patient has OH. It takes lots of time and effort to perform the test. Assuming there exist measurement errors when obtaining the blood pressures and heart rate at all angles, this paper proposes using multiple mixed‐effect models to obtain the true trajectories of these measurements, which take into account the measurement error and the possible correlation among multiple measurements, and a logistic regression uses these true trajectories at a given time and other fixed‐effect covariates as predictors to predict the status of OH. The joint likelihood function is derived to estimate parameters and the area under the receiver operating characteristics curve is used to estimate the predictive power of the model. Monte Carlo simulations are performed to evaluate the feasibility of the proposed methods. Also, the proposed model is implemented in the real data and provides an acceptable predictive power. 其他題名： Biom. J 出版者： Germany: Blackwell Publishing Ltd 出版日期： 2015-07 出處： Biometrical Journal, 2015-07, Vol.57 (4), p.661-675 資源來源： Wiley Journals 版權： 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim 版權： 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. 版權： 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 識別號： ISSN: 0323-3847 識別號： EISSN: 1521-4036 識別號： DOI: 10.1002/bimj.201400044 識別號： PMID: 25921394
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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