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Inconsistency between overall and subgroup analyses

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General Psychiatry 2022年第3期35卷 201-204页

作者： Hongyue Wang Bokai Wang Xin M Tu Changyong Feng departments of biostatistics&computational biology University of RochesterRochesterNew YorkUSA Division of biostatistics and Bioinformatics Herbert Wertheim School of Public Health and Human Longevity ScienceUC San DiegoLa JollaCaliforniaUSA

Suppose we have a sample of subjects in two treatment groups.To study the difference of the treatment effects,we can analyse the data using all subjects(overall analysis).We may also divide the subjects into several subgroups based on some covariates of interest(eg,gender),and study the treatment effects within each subgroup.The results of these two analyses may be different or even in opposite directions.In this paper,we give a general sufficient condition of consistency between the overall and subgroup analyses.

关键词： subgroup consistency groups.

生物医学研究中因检测范围所限致数据缺失时简单填补法对人口几何均数推断的影响（英文）

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上海精神医学 2015年第5期27卷 319-325页

作者： Hongyue WANG Guangqing CHEN Xiang LU Hui ZHANG Changyong FENG departments of biostatistics and computational biology University of Rochester Department of biostatistics St. Jude Children's Research Hospital Department of anesthesiology University of Rochester

概述:在生物医学和社会心理学研究中采用样本几何均值估计、比较人口几何均值的方法十分普遍。然而,由于测量工具的检测局限,有时无法观察到测量的实际值。处理这个问题的一种常见做法是用较小的正值常数来替代缺失值,然后在这些填补数... 详细信息

概述:在生物医学和社会心理学研究中采用样本几何均值估计、比较人口几何均值的方法十分普遍。然而,由于测量工具的检测局限,有时无法观察到测量的实际值。处理这个问题的一种常见做法是用较小的正值常数来替代缺失值,然后在这些填补数据基础上进行统计推断。然而,这种简单的填补方法对推论的影响还没有研究过。我们在本文中阐明了这种简单的填补方法可能会大幅度地改变一项研究所报告的结果,因此即使检测限非常小,也会使结果难以解释。

关键词：生物医学检测限数据丢失归责几何平均值原因推论测量仪器

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

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

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

The differences and similarities between two-sample t-test and paired t-test

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上海精神医学 2017年第3期29卷 184-188页

作者：徐曼菲 Drew FRALICK Julia Z.ZHENG Bokai Wang Xin M.TU Changyong FENG Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Department of Immunology and Microbiology McG ill University departments of biostatistics & computational biology University of Rochester Department of Family Medicine and Public Health University of California San Diego School of Medicine departments of anesthesiology University of Rochester

临床研究中经常遇到比较实验组和对照组之间的结果。双样本t检验(又称为独立样本t检验)和配对t检验可能是运用于比较两个样本之间均值的最广泛的统计方法。然而,这两种方法的运用会产生混淆,从而导致使用不当。本文中,我们讨论了这两种... 详细信息

临床研究中经常遇到比较实验组和对照组之间的结果。双样本t检验(又称为独立样本t检验)和配对t检验可能是运用于比较两个样本之间均值的最广泛的统计方法。然而,这两种方法的运用会产生混淆,从而导致使用不当。本文中,我们讨论了这两种t检验之间的异同性,并运用三个范例来阐述双样本t检验和配对t检验的计算过程。

关键词：样本均值 t检验配对异同独立样本测试方法使用不当计算过程

三种常用危险度测量方法之间的关系:相对危险度、危险差和比值比（英文）

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上海精神医学 2016年第1期28卷 56-60页

作者： Changyong FENG Hongyue WANG Bokai WANG Xiang LU Hao SUN Xin M.TU Department of biostatistics & computational biology University of Rochester Department of anesthesiology University of Rochester

概述:相对危险度(relative risk)、危险差(risk difference)和比值比(odds ratio)是最常用的三种比较不同群体之间疾病风险的方法。虽然它们在生物医学和社会心理学研究中广泛流行,但是尚无文献明确这三种方法之间的关系。许多研究人员... 详细信息

概述:相对危险度(relative risk)、危险差(risk difference)和比值比(odds ratio)是最常用的三种比较不同群体之间疾病风险的方法。虽然它们在生物医学和社会心理学研究中广泛流行,但是尚无文献明确这三种方法之间的关系。许多研究人员误以为它们之间的关系是单调变化的,即某种测量方式中较高(或较低)的值与其它测量方式中的较高(或较低)的值相关。本文中,我们讨论了三个定理,并提供例子解释之前大部分研究人员所认为这三种方法之间的关系是不对的;这些测量方法相互之间并没有逻辑关系。研究人员在说明不同种测量方法之间的相关性时或结合使用不同种风险测量方法所得的研究结果时必须非常谨慎。

关键词：比值比相对危险度危险差

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