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677 results on '"Zeng, Xian‐Tao"'

7. Research on the development methodology for clinical practice guidelines for organic integration of traditional Chinese and Western medicine

Author

Jin, Ying-Hui, Wang, Yan-Ping, Xie, Ying-Lan, Tian, Gui-Hua, Zhang, Xiao-Yu, Shi, Nan-Nan, Yang, Ke-Hu, Sun, Xin, Chen, Yao-Long, Wu, Da-Rong, Guo, Xin-Feng, Ge, Long, Zhao, Chen, Lu, Cheng, Jiang, Yin, Guo, Jing, Yan, Si-Yu, Wang, Yong-Bo, Huang, Qiao, Ren, Xiang-Ying, Rao, Ying-Yue, Wang, Yun-Yun, Yuan, Meng-Qian, Zeng, Xian-Tao, and Shang, Hong-Cai

Published
2023
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9. Optimally estimating the sample standard deviation from the five-number summary

Author

Shi, Jiandong, Luo, Dehui, Weng, Hong, Zeng, Xian-Tao, Lin, Lu, Chu, Haitao, and Tong, Tiejun

Subjects
Statistics - Methodology
Abstract

When reporting the results of clinical studies, some researchers may choose the five-number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation, particularly for skewed data. For these studies, when included in a meta-analysis, it is often desired to convert the five-number summary back to the sample mean and standard deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the sample standard deviation that fully utilizes the sample size information. For ease of implementation, we also derive an approximation formula for the optimal weight, as well as a shortcut formula for the sample standard deviation. Numerical results show that our new estimator provides a more accurate estimate for normal data and also performs favorably for non-normal data. Together with the optimal sample mean estimator in Luo et al., our new methods have dramatically improved the existing methods for data transformation, and they are capable to serve as "rules of thumb" in meta-analysis for studies reported with the five-number summary. Finally for practical use, an Excel spreadsheet and an online calculator are also provided for implementing our optimal estimators., Comment: 30 pages and 4 figures and 2 tables. arXiv admin note: substantial text overlap with arXiv:1801.01267

Published
2020

11. How to estimate the sample mean and standard deviation from the five number summary?

Author

Shi, Jiandong, Luo, Dehui, Weng, Hong, Zeng, Xian-Tao, Lin, Lu, and Tong, Tiejun

Subjects
Statistics - Methodology
Abstract

In some clinical studies, researchers may report the five number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation. To conduct meta-analysis for pooling studies, one needs to first estimate the sample mean and standard deviation from the five number summary. A number of studies have been proposed in the recent literature to solve this problem. However, none of the existing estimators for the standard deviation is satisfactory for practical use. After a brief review of the existing literature, we point out that Wan et al.'s method (BMC Med Res Methodol 14:135, 2014) has a serious limitation in estimating the standard deviation from the five number summary. To improve it, we propose a smoothly weighted estimator by incorporating the sample size information and derive the optimal weight for the new estimator. For ease of implementation, we also provide an approximation formula of the optimal weight and a shortcut formula for estimating the standard deviation from the five number summary. The performance of the proposed estimator is evaluated through two simulation studies. In comparison with Wan et al.'s estimator, our new estimator provides a more accurate estimate for normal data and performs favorably for non-normal data. In real data analysis, our new method is also able to provide a more accurate estimate of the true sample standard deviation than the existing method. In this paper, we propose an optimal estimator of the standard deviation from the five number summary. Together with the optimal mean estimator in Luo et al. (Stat Methods Med Res, in press, 2017), our new methods have improved the existing literature and will make a solid contribution to meta-analysis and evidence-based medicine., Comment: 18 pages, 5 figures, 2 tables

Published
2018

14. Treatment and surveillance for non-muscle-invasive bladder cancer: a clinical practice guideline (2021 edition)

Author

Jin, Ying-Hui, Zeng, Xian-Tao, Liu, Tong-Zu, Bai, Zhi-Ming, Dou, Zhong-Ling, Ding, De-Gang, Fan, Zhi-Lu, Han, Ping, Huang, Yi-Ran, Huang, Xing, Li, Ming, Li, Xiao-Dong, Li, Yi-Ning, Li, Xu-Hui, Liang, Chao-Zhao, Liu, Jiu-Min, Ma, Hong-Shun, Qi, Juan, Shi, Jia-Qi, Wang, Jian, Wang, De-Lin, Wang, Zhi-Ping, Wang, Yun-Yun, Wang, Yong-Bo, Wei, Qiang, Xia, Hai-Bo, Xing, Jin-Chun, Yan, Si-Yu, Zhang, Xue-Pei, Zheng, Guo-You, Xing, Nian-Zeng, He, Da-Lin, and Wang, Xing-Huan

Published
2022
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16. Correction: Management of granulomatous lobular mastitis: an international multidisciplinary consensus (2021 edition)

Author

Yuan, Qian-Qian, Xiao, Shu-Yuan, Farouk, Omar, Du, Yu-Tang, Sheybani, Fereshte, Tan, Qing Ting, Akbulut, Sami, Cetin, Kenan, Alikhassi, Afsaneh, Yaghan, Rami Jalal, Durur-Subasi, Irmak, Altintoprak, Fatih, Eom, Tae Ik, Alper, Fatih, Hasbahceci, Mustafa, Martínez-Ramos, David, Oztekin, Pelin Seher, Kwong, Ava, Pluguez-Turull, Cedric W., Brownson, Kirstyn E., Chandanwale, Shirish, Habibi, Mehran, Lan, Liu-Yi, Zhou, Rui, Zeng, Xian-Tao, Bai, Jiao, Bai, Jun-Wen, Chen, Qiong-Rong, Chen, Xing, Zha, Xiao-Ming, Dai, Wen-Jie, Dai, Zhi-Jun, Feng, Qin-Yu, Gao, Qing-Jun, Gao, Run-Fang, Han, Bao-San, Hou, Jin-Xuan, Hou, Wei, Liao, Hai-Ying, Luo, Hong, Liu, Zheng-Ren, Lu, Jing-Hua, Luo, Bin, Ma, Xiao-Peng, Qian, Jun, Qin, Jian-Yong, Wei, Wei, Wei, Gang, Xu, Li-Ying, Xue, Hui-Chao, Yang, Hua-Wei, Yang, Wei-Ge, Zhang, Chao-Jie, Zhang, Fan, Zhang, Guan-Xin, Zhang, Shao-Kun, Zhang, Shu-Qun, Zhang, Ye-Qiang, Zhang, Yue-Peng, Zhang, Sheng-Chu, Zhao, Dai-Wei, Zheng, Xiang-Min, Zheng, Le-Wei, Xu, Gao-Ran, Zhou, Wen-Bo, and Wu, Gao-Song

Published
2022
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19. Clinical practice guideline for transurethral plasmakinetic resection of prostate for benign prostatic hyperplasia (2021 Edition)

Author

Zeng, Xian-Tao, Jin, Ying-Hui, Liu, Tong-Zu, Chen, Fang-Ming, Ding, De-Gang, Fu, Meng, Gu, Xin-Quan, Han, Bang-Min, Huang, Xing, Hou, Zhi, Hu, Wan-Li, Kang, Xin-Li, Li, Gong-Hui, Li, Jian-Xing, Li, Pei-Jun, Liang, Chao-Zhao, Liu, Xiu-Heng, Liu, Zhi-Yu, Liu, Chun-Xiao, Liu, Jiu-Min, Luo, Guang-Heng, Luo, Yi, Qin, Wei-Jun, Qiu, Jian-Hong, Qiu, Jian-Xin, Shang, Xue-Jun, Shi, Ben-Kang, Sun, Fa, Tian, Guo-Xiang, Tian, Ye, Wang, Feng, Wang, Feng, Wang, Yin-Huai, Wang, Yu-Jie, Wang, Zhi-Ping, Wang, Zhong, Wei, Qiang, Xiao, Min-Hui, Xu, Wan-Hai, Yi, Fa-Xian, Zhu, Chao-Yang, Zhuang, Qian-Yuan, Zhou, Li-Qun, Zou, Xiao-Feng, Xing, Nian-Zeng, He, Da-Lin, and Wang, Xing-Huan

Published
2022
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20. Management of granulomatous lobular mastitis: an international multidisciplinary consensus (2021 edition)

Author

Yuan, Qian-Qian, Xiao, Shu-Yuan, Farouk, Omar, Du, Yu-Tang, Sheybani, Fereshte, Tan, Qing Ting, Akbulut, Sami, Cetin, Kenan, Alikhassi, Afsaneh, Yaghan, Rami Jalal, Durur-Subasi, Irmak, Altintoprak, Fatih, Eom, Tae Ik, Alper, Fatih, Hasbahceci, Mustafa, Martínez-Ramos, David, Oztekin, Pelin Seher, Kwong, Ava, Pluguez-Turull, Cedric W., Brownson, Kirstyn E., Chandanwale, Shirish, Habibi, Mehran, Lan, Liu-Yi, Zhou, Rui, Zeng, Xian-Tao, Bai, Jiao, Bai, Jun-Wen, Chen, Qiong-Rong, Chen, Xing, Zha, Xiao-Ming, Dai, Wen-Jie, Dai, Zhi-Jun, Feng, Qin-Yu, Gao, Qing-Jun, Gao, Run-Fang, Han, Bao-San, Hou, Jin-Xuan, Hou, Wei, Liao, Hai-Ying, Luo, Hong, Liu, Zheng-Ren, Lu, Jing-Hua, Luo, Bin, Ma, Xiao-Peng, Qian, Jun, Qin, Jian-Yong, Wei, Wei, Wei, Gang, Xu, Li-Ying, Xue, Hui-Chao, Yang, Hua-Wei, Yang, Wei-Ge, Zhang, Chao-Jie, Zhang, Fan, Zhang, Guan-Xin, Zhang, Shao-Kun, Zhang, Shu-Qun, Zhang, Ye-Qiang, Zhang, Yue-Peng, Zhang, Sheng-Chu, Zhao, Dai-Wei, Zheng, Xiang-Min, Zheng, Le-Wei, Xu, Gao-Ran, Zhou, Wen-Bo, and Wu, Gao-Song

Published
2022
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23. Clinical biomarker‐based biological aging and risk of benign prostatic hyperplasia: A large prospective cohort study.

Author

Huang, Qiao, Li, Bing‐Hui, Wang, Yong‐Bo, Zi, Hao, Zhang, Yuan‐Yuan, Li, Fei, Fang, Cheng, Tang, Shi‐Di, Jin, Ying‐Hui, Huang, Jiao, and Zeng, Xian‐Tao

Subjects
RISK assessment, TESTOSTERONE, RESEARCH funding, T-test (Statistics), QUESTIONNAIRES, AGE distribution, DESCRIPTIVE statistics, CHI-squared test, BENIGN prostatic hyperplasia, LONGITUDINAL method, GENETIC risk score, AGING, MEN'S health, CONFIDENCE intervals, DATA analysis software, BIOMARKERS, PROPORTIONAL hazards models, PATIENT aftercare, DISEASE risk factors
Abstract

Objective: Chronological age (CAge), biological age (BAge), and accelerated age (AAge) are all important for aging‐related diseases. CAge is a known risk factor for benign prostatic hyperplasia (BPH); However, the evidence of association of BAge and AAge with BPH is limited. This study aimed to evaluate the association of CAge, Bage, and AAge with BPH in a large prospective cohort. Method: A total of 135,933 males without BPH at enrolment were extracted from the UK biobank. We calculated three BAge measures (Klemera–Doubal method, KDM; PhenoAge; homeostatic dysregulation, HD) based on 16 biomarkers. Additionally, we calculated KDM‐BAge and PhenoAge‐BAge measures based on the Levine method. The KDM‐AAge and PhenoAge‐AAge were assessed by the difference between CAge and BAge and were standardized (mean = 0 and standard deviation [SD] = 1). Cox proportional hazard models were applied to assess the associations of CAge, Bage, and AAge with incident BPH risk. Results: During a median follow‐up of 13.150 years, 11,811 (8.690%) incident BPH were identified. Advanced CAge and BAge measures were associated with an increased risk of BPH, showing threshold effects at a later age (all P for nonlinearity <0.001). Nonlinear relationships between AAge measures and risk of BPH were also found for KDM‐AAge (P = 0.041) and PhenoAge‐AAge (P = 0.020). Compared to the balance comparison group (−1 SD < AAge < 1 SD), the accelerated aging group (AAge > 2 SD) had a significantly elevated BPH risk with hazard ratio (HR) of 1.115 (95% CI, 1.000–1.223) for KDM‐AAge and 1.180 (95% CI, 1.068–1.303) for PhenoAge‐AAge, respectively. For PhenoAge‐AAge, subgroup analysis of the accelerated aging group showed an increased HR of 1.904 (95% CI, 1.374–2.639) in males with CAge <50 years and 1.233 (95% CI, 1.088–1.397) in those having testosterone levels <12 nmol/L. Moreover, AAge‐associated risk of BPH was independent of and additive to genetic risk. Conclusions: Biological aging is an independent and modifiable risk factor for BPH. We suggest performing active health interventions to slow biological aging, which will help mitigate the progression of prostate aging and further reduce the burden of BPH. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Perceived infection transmission routes, infection control practices, psychosocial changes, and management of COVID-19 infected healthcare workers in a tertiary acute care hospital in Wuhan: a cross-sectional survey

Author

Jin, Ying-Hui, Huang, Qiao, Wang, Yun-Yun, Zeng, Xian-Tao, Luo, Li-Sha, Pan, Zhen-Yu, Yuan, Yu-Feng, Chen, Zhi-Min, Cheng, Zhen-Shun, Huang, Xing, Wang, Na, Li, Bing-Hui, Zi, Hao, Zhao, Ming-Juan, Ma, Lin-Lu, Deng, Tong, Wang, Ying, and Wang, Xing-Huan

Published
2020
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44. The spatial and temporal trends of severe periodontitis burden in Asia, 1990-2019: A population-based epidemiological study.

Author

Luo, Li‐Sha, Luan, Hang‐Hang, Jiang, Jun‐Feng, Wu, Lan, Li, Cheng, Leng, Wei‐Dong, Zeng, Xian‐Tao, Luo, Li-Sha, Luan, Hang-Hang, Jiang, Jun-Feng, Leng, Wei-Dong, and Zeng, Xian-Tao

Abstract

Background: To investigate the long-term and spatial patterns of incidence, prevalence, and disability-adjusted life year (DALY) rates of severe periodontitis in Asia from 1990 to 2019, and to estimate the associations between disease burden and socioeconomic development using the Socio-Demographic Index (SDI).Methods: Data were obtained from the global burden of disease study 2019. The average annual percent change (AAPC) was calculated to reflect temporal trends, spatial autocorrelation analysis was conducted to estimate the spatial characteristics, and spatial panel models were used to investigate the association between SDI and severe periodontitis burden.Results: For Asia as a whole, the crude rates increased by 1.10% per year for incidence, 1.42% per year for prevalence, and 1.41% per year for DALY from 1990 to 2019. The age-standardized incidence, prevalence and DALY rates increased by 0.18%, 0.22%, and 0.23% per year, respectively. Spatially, the hot spots of age-standardized incidence, prevalence and DALY rates were located in Southern Asia, besides, these rates all showed increasing trends in most countries, and the increases were clustered in Southeastern Asia. Further, SDI showed a negative association with incidence (coef = -14.44; 95% CI: -24.63, -4.25) and prevalence (coef = -40.09; -51.81, -28.36), and a positive association with DALY rates (coef = 0.31; 0.23; 0.38).Conclusions: Severe periodontitis poses a serious public health challenge in Asian countries with increasing temporal trends and substantial spatial inequalities. Effective geographically targeted public health interventions and strategies are needed to address the growing burden associated with severe periodontitis. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Additional file 1 of Treatment and surveillance for non-muscle-invasive bladder cancer: a clinical practice guideline (2021 edition)

Author

Jin, Ying-Hui, Zeng, Xian-Tao, Liu, Tong-Zu, Bai, Zhi-Ming, Dou, Zhong-Ling, Ding, De-Gang, Fan, Zhi-Lu, Han, Ping, Huang, Yi-Ran, Huang, Xing, Li, Ming, Li, Xiao-Dong, Li, Yi-Ning, Li, Xu-Hui, Liang, Chao-Zhao, Liu, Jiu-Min, Ma, Hong-Shun, Qi, Juan, Shi, Jia-Qi, Wang, Jian, Wang, De-Lin, Wang, Zhi-Ping, Wang, Yun-Yun, Wang, Yong-Bo, Wei, Qiang, Xia, Hai-Bo, Xing, Jin-Chun, Yan, Si-Yu, Zhang, Xue-Pei, Zheng, Guo-You, Xing, Nian-Zeng, He, Da-Lin, and Wang, Xing-Huan

Abstract

Additional file 1. Classification criterion of NMIBC

Published
2022
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