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      The applications of Bayesian models in real-world studies of traditional Chinese medicine: a primer

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          Abstract

          Highlights

          Real-world study is valuable for traditional Chinese medicine. However, there are no gold standards of statistical approaches for analyzing data from real-world study of traditional Chinese medicine. In the present study, we discussed why and when to use Bayesian analysis and the challenge in the real-world study of traditional Chinese medicine.

          Abstract

          Real-world study is valuable for traditional Chinese medicine. However, there are no gold standards of statistical approaches for analyzing data from real-world study of traditional Chinese medicine. With the development of computer technology, researchers have increasingly paid attention to Bayesian statistics in the biomedical field. In present study, real-world study and Bayesian statistics were introduced. It was discussed that why and when to use Bayesian analysis and the challenge in the real-world study of traditional Chinese medicine.

          Translated abstract

          真实世界研究适用于中医药领域。目前,尚无公认的用于真实世界研究的统计分析方法。随着计算机技术的发展,贝叶斯统计越来越受到生物医学领域的重视。本文在介绍贝叶斯统计和真实世界研究的基础上,讨论了在中医药领域的真实世界研究中使用贝叶斯模型的注意事项和面临的挑战。

          Most cited references17

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          Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research.

          To raise awareness among clinicians and epidemiologists that single-patient (n-of-1) trials are potentially useful for informing personalized treatment decisions for patients with chronic conditions. We reviewed the clinical and statistical literature on methods and applications of single-patient trials and then critically evaluated the needs for further methodological developments. Existing literature reports application of 2,154 single-patient trials in 108 studies for diverse clinical conditions; various recent commentaries advocate for wider application of such trials in clinical decision making. Preliminary evidence from several recent pilot acceptability studies suggests that single-patient trials have the potential for widespread acceptance by patients and clinicians as an effective modality for increasing the therapeutic precision. Bayesian and adaptive statistical methods hold promise for increasing the informational yield of single-patient trials while reducing participant burden, but are not widely used. Personalized applications of single-patient trials can be enhanced through further development and application of methodologies on adaptive trial design, stopping rules, network meta-analysis, washout methods, and methods for communicating trial findings to patients and clinicians. Single-patient trials may be poised to emerge as an important part of the methodological armamentarium for comparative effectiveness research and patient-centered outcomes research. By permitting direct estimation of individual treatment effects, they can facilitate finely graded individualized care, enhance therapeutic precision, improve patient outcomes, and reduce costs. Copyright © 2013 Elsevier Inc. All rights reserved.
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            A real-world study of the effect of timing of insulin initiation on outcomes in older medicare beneficiaries with type 2 diabetes mellitus.

            To compare clinical and economic outcomes of early insulin initiation with those of delayed initiation in older adults with type 2 diabetes mellitus (T2DM).
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              Prediction of conversion from mild cognitive impairment to Alzheimer disease based on bayesian data mining with ensemble learning.

              Prediction of disease progress is of great importance to Alzheimer disease (AD) researchers and clinicians. Previous attempts at constructing predictive models have been hindered by undersampling, and restriction to linear associations among variables, among other problems. To address these problems, we propose a novel Bayesian data-mining method called Bayesian Outcome Prediction with Ensemble Learning (BOPEL). BOPEL uses a Bayesian-network representation with boosting, to allow the detection of nonlinear multivariate associations, and incorporates resampling-based feature selection to prevent over-fitting caused by undersampling. We demonstrate the use of this approach in predicting conversion to AD in individuals with mild cognitive impairment (MCI), based on structural magnetic-resonance and magnetic-resonance- spectroscopy data. This study includes 26 subjects with amnestic MCI: the converter group (n = 8) met MCI criteria at baseline, but converted to AD within five years, whereas the non-converter group (n = 18) met MCI criteria at baseline and at follow-up. We found that BOPEL accurately differentiates MCI converters from non-converters, based on the baseline volumes of the left hippocampus, the banks of the right superior temporal sulcus, the right entorhinal cortex, the left lingual gyrus, and the rostral aspect of the left middle frontal gyrus. Prediction accuracy was 0.81, sensitivity was 0.63 and specificity was 0.89. We validated the generated predictive model with an independent data set constructed from the Alzheimer Disease Neuroimaging Initiative database, and again found high predictive accuracy (0.75).
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                Author and article information

                Contributors
                Journal
                Traditional Medicine Research
                Traditional Medicine Research
                TMR Editorial Board (Jintang road, 99, Hedong district Tianjin,China, 300170. )
                2413-3973
                March 2017
                5 March 2017
                : 2
                : 2
                : 88-93
                Affiliations
                [1 ] Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 312 Anshanxi Road, Nankai District, Tianjin 300193, China
                [2 ]Program office for Cancer Screening in Urban China, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
                [3 ]Baokang hospital, Tianjin university of traditional Chinese medicine, Tianjin, 300193, China.
                Author notes
                *Corresponding to: Jing Chen, Baokang hospital, Tianjin university of traditional Chinese medicine, Tianjin 300193, China. E-mail: cjshcsyc@ 123456126.com
                Submitted: 3 March 2017
                Article
                2413-3973-2-2-88
                10.12032/TMR201706042
                d7b1a51b-a294-4fa9-a093-f2a949a1774e

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 14 March 2017
                Categories
                Orginal Article
                Medicine
                Traditional Medicine

                Medicine,Pharmacology & Pharmaceutical medicine,Health & Social care,Complementary & Alternative medicine
                Real-world study,贝叶斯模型,真实世界研究,中医药,Bayesian models,Traditional Chinese medicine

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