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Relations among sensitivity,specificity and predictive values of medical tests based on biomarkers

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General Psychiatry 2021年第2期34卷 99-103页

作者： Hongyue wang Bokai Wang Xunan Zhang Changyong Feng Departments of Biostatistics&Computational Biology and Anesthesiology University of RochesterRochesterNew YorkUSA Departments of Biostatistics&Computational Biology and Computational Biology University of RochesterRochesterNew YorkUSA Egen Solutions IncNapervilleIllinoisUSA Department of Biostatistics and Computational Biology University of Rochester Medical CenterRochesterNew YorkUSA

Diagnostic tests are usually based on some quantitative biomarkers.Two key parameters used to characterise the quality of a test are test sensitivity and specificity.Predictive values of the disease status based on test results are also of interest in medical research and public health management.In this paper,we study the relations among sensitivity,specificity and predictive values of the test.The core concept is risk function,which is assumed to be an increasing function of the biomarker.Our results show that test sensitivity and specificity change in opposite directions.The positive predictive value and the sensitivity also change in opposite directions.Likewise,the negative predictive value and the specificity change in opposite directions.

关键词： specificity directions opposite

The p-value and model specification in statistics

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General Psychiatry 2019年第3期32卷 163-166页

作者： Bokai Wang Zhirou Zhou Hongyue wang Xin M Tu Changyong Feng Departments of Biostatistics and Computational Biology and Anesthesiology University of Rochester Rochester New York USA Family Medicine and Public Health University of California San Diego San Diego. California USA Naval Health Research Center San Diego California USA

SUMMARY The p value has been widely used as a way to summarise the significance in data analysis. However, misuse and misinterpretation of the p value is common in practice. Our result shows that if the model specification is wrong, the distribution of the p value may be inappropriate, which makes the decision based on the p value invalid.

关键词： p-value model specification statistics

Machine learning methods in psychiatry: a brief introduction

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General Psychiatry 2020年第1期33卷 20-22页

作者： Zhirou Zhou Tsung-Chin Wu Bokai Wang Hongyue wang Xin M Tu Changyong Feng Department of Biostatistics and Computational Biology University of RochesterRochesterNew YorkUSA Department of Mathematics University of California San DiegoLa JollaCaliforniaUSA Family Medicine and Public Health University of California San DiegoLa JollaCaliforniaUSA Naval Health Research Center San DiegoCaliforniaUSA

Machine learning(ML)techniques have been widely used to address mental health questions.We discuss two main aspects of ML in psychiatry in this paper,that is,supervised learning and unsupervised learning.Examples are ... 详细信息

关键词： learning illustrate brief

有效性试验、非劣效性试验、和等效试验之间的比较（英文）

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上海精神医学 2017年第6期29卷 385-388页

作者： Bokai Wang Hongyue wang Xin M. TU Changyong FENG Department of Biostatistics and Computational Biology University of Rochester Rochester NY USA Department of Anesthesiology University of Rochester Rochester NY USA Division of Biostatistics University of California San Diego La Jolla CA USA

概述:一种新型药物或治疗的效果通常是通过临床随机对照试验获得的。然而,假设的设定对生物医学研究者来说仍然是具有挑战性的问题。在本调查中,我们讨论了有效性试验、非劣效性试验、和等效性试验。这三类试验对治疗效果均有不同的假... 详细信息

概述:一种新型药物或治疗的效果通常是通过临床随机对照试验获得的。然而,假设的设定对生物医学研究者来说仍然是具有挑战性的问题。在本调查中,我们讨论了有效性试验、非劣效性试验、和等效性试验。这三类试验对治疗效果均有不同的假设。我们比较了这些试验中的假设并提供了样本量计算公式。

关键词：随机对照试验临床显著性的界值样本量大小计算

Advanced machine learning methods in psychiatry: an introduction

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General Psychiatry 2020年第2期33卷 107-110页

作者： Tsung-Chin Wu Zhirou Zhou Hongyue wang Bokai Wang Tuo Lin Changyong Feng Xin M Tu Department of Mathematics University of California San DiegoLa JollaCaliforniaUSA Department of Biostatistics and Computational Biology University of RochesterRochesterNew YorkUSA Departments of Biostatistics and Computational Biology and Anesthesiology University of RochesterRochesterNew YorkUSA Clinical and Translational Research Institute University of California San DiegoSan DiegoCaliforniaUSA Family Medicine and Public Health University of California San DiegoLa JollaCaliforniaUSA Naval Health Research Center San DiegoCaliforniaUSA

Mental health questions can be tackled through machine learning(ML)techniques.Apart from the two ML methods we introduced in our previous paper,we discuss two more advanced ML approaches in this paper:support vector machines and artificial neural networks.To illustrate how these ML methods have been employed in mental health,recent research applications in psychiatry were reported.

关键词： networks. ed. learning

Sample sizes based on three popular indices of risks

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General Psychiatry 2018年第6期31卷 150-152页

作者： Hongyue wang Bokai Wang Xin M Tu Jinyuan Liu Changyong Feng Department of Biostatistics and Computational Biology University of Rochester Division of Biostatistics and Bioinformatics University of California San Diego Department of Anesthesiology University of Rochester

Sample size justification is a very crucial part in the design of clinical trials. In this paper, the authors derive a new formula to calculate the sample size for a binary outcome given one of the three popular indices of risk difference.The sample size based on the absolute difference is the fundamental one, which can be easily used to derive sample size given the risk ratio or OR.

关键词： size sample derive indices

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辛普森悖论的范例（英文）

上海精神医学 2018年第2期30卷 139-143页

作者： Bokai Wang Pan WU Brian KWAN Xin M.TU Changyong FENG Department of Biostatistics and Computational Biology University of Rochester Value Institute Christiana Care Health System John H Ammon Medical Education Center Division of Biostatistics and Bioinformatics University of California San Diego Department of Anesthesiology University of Rochester

概述:辛普森悖论普遍存在于很多领域。它具有数据的条件性和边缘性解释之间的不一致特征。在本文中,我们通过一些例子来阐述辛普森悖论是如何在连续性、分类和时间-事件数据中产生的。

关键词：悖论多区域

Tests for paired count outcomes

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General Psychiatry 2018年第4期31卷 48-53页

作者： James A Proudfoot Tuo Lin Bokai Wang Xin M Tu Clinical and Translational Research Institute University of California San Diego Departments of Biostatistics& Computational Biology and Anesthesiology University of Rochester Department of Family Medicine and Public Health University of California San Diego

Paired t-tests are widely used to compare differences in paired continuous outcomes. This test, however, does not apply to non-continuous outcomes. For binary outcomes,McNemar's test is the de facto method for comparing differences in paired binary variables. For count or frequency outcomes, there is not much discussion in the literature. The Wilcoxon signed-rank test is often used to assess differences in paired count outcomes. However, like the Mann-Whitney U test, or Wilcoxon rank-sum test for comparing two independent groups, the Wilcoxon signedrank test is generally not efficient(powerful). In this essay,we consider some alternative methods for comparing paired count outcomes and compare them with the Wilcoxon signed-rank test using Monte Carlo simulations.

关键词： Tests for paired count outcomes

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单因素与多因素逻辑回归的不一致性（英文）

上海精神医学 2017年第2期29卷 124-128页

作者： Hongyue wang Jing PENG Bokai Wang Xiang LU Julia Z.ZHENG Kejia wang Xin M.TU Changyong FENG Department of Biostatistics and Computational Biology University of Rochester Rochester NY USA Department of Anesthesiology University of Rochester Rochester NY USA Department of Microbiology and Immunology McGill University Montreal QC Canada Department of Family Medicine and Public Health University of California San Diego La Jolla CA USA

逻辑回归是研究协变量对二元结果影响的一种常用的统计方法。它已被广泛应用于临床试验和观察性研究。然而,单因素回归得到的结果和多元逻辑回归得到的结果往往是相互矛盾的。在多元回归中可能对结果会显示出非常强烈的影响的一个协变... 详细信息

逻辑回归是研究协变量对二元结果影响的一种常用的统计方法。它已被广泛应用于临床试验和观察性研究。然而,单因素回归得到的结果和多元逻辑回归得到的结果往往是相互矛盾的。在多元回归中可能对结果会显示出非常强烈的影响的一个协变量在单因素回归中可能不会,反之亦然。这些事实在生物医学研究中并没有引起足够的重视。误用逻辑回归在医学出版物中非常普遍。在本文中,我们研究了单因素和多因素逻辑回归分析的不一致性,并在多元逻辑回归分析的模型部分中给出建议。

关键词：条件期望模型选择逻辑回归