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学者姓名：林月琼
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Abstract ：
The response variable in clinical studies is frequently ordinal. In the comparison of treatment efficacies, the latent Weibull model has been shown to be a powerful tool that is appropriate for testing even when the data are highly skewed, a condition under which the traditional proportional odds model may be unsuitable owing to the possible inflation of the type-I error rate. A response-adaptive treatment randomization scheme based on the latent Weibull model has recently been proposed to allow more patients to receive the better treatment while maintaining competitive test power. Nevertheless, the proposed procedure has been restricted to the comparison of two treatments. This paper generalizes the aforementioned idea to include trials with multiple treatments. Statistical procedures for pairwise comparisons and multiple comparisons with a control are discussed. Furthermore, a simulation study is performed to illustrate the superiority of the proposed method. The practical benefits of the suggested procedures are demonstrated through the redesign of two clinical trials.
Keyword ：
Allocation target Allocation target Latent Weibull model Latent Weibull model Multiple comparisons Multiple comparisons Ordinal responses Ordinal responses Response-adaptive treatment allocation Response-adaptive treatment allocation
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| GB/T 7714 | Lu, Tong-Yu , Lin, Yueqiong , Zhong, Junjiang . Response-adaptive treatment randomization for trials comparing multiple treatments with ordinal responses [J]. | JOURNAL OF THE KOREAN STATISTICAL SOCIETY , 2025 . |
| MLA | Lu, Tong-Yu 等. "Response-adaptive treatment randomization for trials comparing multiple treatments with ordinal responses" . | JOURNAL OF THE KOREAN STATISTICAL SOCIETY (2025) . |
| APA | Lu, Tong-Yu , Lin, Yueqiong , Zhong, Junjiang . Response-adaptive treatment randomization for trials comparing multiple treatments with ordinal responses . | JOURNAL OF THE KOREAN STATISTICAL SOCIETY , 2025 . |
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Abstract ：
随着海量数据的涌现,多重假设检验越来越多地被用来做大规模统计推断.基于有序分类数据,对常用的统计分析模型和多个样本组差异性的多重假设检验方法,包括秩和检验法和潜变量模型法,及其最新进展进行了综述,并对多重假设检验在各个学科领域的分析应用进行了探讨.
Keyword ：
多重假设检验 多重假设检验 整体第一类错误 整体第一类错误 有序分类数据 有序分类数据 潜变量模型 潜变量模型 秩和检验 秩和检验
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| GB/T 7714 | 林月琼 , 陈兰娟 . 基于有序分类数据的多重假设检验研究综述 [J]. | 区域治理 , 2019 , (36) : 210-213 . |
| MLA | 林月琼 等. "基于有序分类数据的多重假设检验研究综述" . | 区域治理 36 (2019) : 210-213 . |
| APA | 林月琼 , 陈兰娟 . 基于有序分类数据的多重假设检验研究综述 . | 区域治理 , 2019 , (36) , 210-213 . |
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Abstract ：
In clinical studies, pairwise comparisons are frequently performed to examine differences in efficacy between treatments. The statistical methods of pairwise comparisons are available when treatment responses are measured on an ordinal scale. The Wilcoxon-Mann-Whitney test and the latent normal model are popular examples. However, these procedures cannot be used to compare treatments in parallel groups (a two-way design) when overall type I error must be controlled. In this paper, we explore statistical approaches to the pairwise testing of treatments that satisfy the requirements of a two-way layout. The results of our simulation indicate that the latent normal approach is superior to the Wilcoxon-Mann-Whitney test. Clinical examples are used to illustrate our suggested testing methods.
Keyword ：
Family-wise error rate Family-wise error rate latent normal method latent normal method ordinal response ordinal response pairwise comparisons pairwise comparisons two-way design two-way design
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| GB/T 7714 | Lu, Tong-Yu , Lin, Yueqiong , Zhong, Junjiang . Pairwise comparisons of treatments with ordinal responses in a two-way setting [J]. | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , 2017 , 46 (21) : 10549-10563 . |
| MLA | Lu, Tong-Yu 等. "Pairwise comparisons of treatments with ordinal responses in a two-way setting" . | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 46 . 21 (2017) : 10549-10563 . |
| APA | Lu, Tong-Yu , Lin, Yueqiong , Zhong, Junjiang . Pairwise comparisons of treatments with ordinal responses in a two-way setting . | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , 2017 , 46 (21) , 10549-10563 . |
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Abstract ：
In clinical studies, multiple comparisons of several treatments to a control with ordered categorical responses are often encountered. A popular statistical approach to analyzing the data is to use the logistic regression model with the proportional odds assumption. As discussed in several recent research papers, if the proportional odds assumption fails to hold, the undesirable consequence of an inflated familywise type I error rate may affect the validity of the clinical findings. To remedy the problem, a more flexible approach that uses the latent normal model with single-step and stepwise testing procedures has been recently proposed. In this paper, we introduce a step-up procedure that uses the correlation structure of test statistics under the latent normal model. A simulation study demonstrates the superiority of the proposed procedure to all existing testing procedures. Based on the proposed step-up procedure, we derive an algorithm that enables the determination of the total sample size and the sample size allocation scheme with a pre-determined level of test power before the onset of a clinical trial. A clinical example is presented to illustrate our proposed method. Copyright (c) 2014 John Wiley & Sons, Ltd.
Keyword ：
familywise error rate familywise error rate latent normal variable model latent normal variable model ordered categorical response ordered categorical response sample size determination sample size determination
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| GB/T 7714 | Lin, Yueqiong , Kwong, Koon Shing , Cheung, Siu Hung et al. Step-up testing procedure for multiple comparisons with a control for a latent variable model with ordered categorical responses [J]. | STATISTICS IN MEDICINE , 2014 , 33 (21) : 3629-3638 . |
| MLA | Lin, Yueqiong et al. "Step-up testing procedure for multiple comparisons with a control for a latent variable model with ordered categorical responses" . | STATISTICS IN MEDICINE 33 . 21 (2014) : 3629-3638 . |
| APA | Lin, Yueqiong , Kwong, Koon Shing , Cheung, Siu Hung , Poon, Wai-Yin . Step-up testing procedure for multiple comparisons with a control for a latent variable model with ordered categorical responses . | STATISTICS IN MEDICINE , 2014 , 33 (21) , 3629-3638 . |
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Abstract ：
Clinical trials frequently involve pairwise comparisons of different treatments to evaluate their relative efficacy. In this study, we examine methods for conducting pairwise tests of treatments with ordered categorical responses. A modified version of the Wilcoxon-Mann-Whitney test based on a logistic regression model assuming proportional odds is a popular choice for comparing two treatments. This paper discusses the extension of this test to pairwise comparisons involving more than two treatments. However, when the proportional odds assumption is not valid, the Wilcoxon-Mann-Whitney-type test procedure cannot control the overall type I error rate at the prespecified level of significance. We therefore propose a better strategy in which a latent normal model is employed. We presented a simulated comparative study of power and the overall type I error rate to illustrate the superiority of the latent normal model. Examples are also given for illustrative purposes. Copyright (c) 2013 John Wiley & Sons, Ltd.
Keyword ：
familywise error rate familywise error rate latent variable model latent variable model log-odds ratio log-odds ratio ordered categorical response ordered categorical response proportional odds model proportional odds model
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| GB/T 7714 | Lin, Yueqiong , Cheung, Siu Hung , Poon, Wai-Yin et al. Pairwise comparisons with ordered categorical data [J]. | STATISTICS IN MEDICINE , 2013 , 32 (18) : 3192-3205 . |
| MLA | Lin, Yueqiong et al. "Pairwise comparisons with ordered categorical data" . | STATISTICS IN MEDICINE 32 . 18 (2013) : 3192-3205 . |
| APA | Lin, Yueqiong , Cheung, Siu Hung , Poon, Wai-Yin , Lu, Tong-Yu . Pairwise comparisons with ordered categorical data . | STATISTICS IN MEDICINE , 2013 , 32 (18) , 3192-3205 . |
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