Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics Subjects: Statistics >> Biomedical Statistics submitted time 2024-05-06
Abstract： 【Abstract】 Extremely unbalanced data here refers to datasets where the values of independent or dependent variables exhibit severe imbalances in proportions, such as extremely unbalanced case-control ratios, very low disease incidence rates, heavily censored survival data, and low-frequency or rare genetic variants. In such scenarios, test statistics in classical statistical methods, such as logistic regression and Cox proportional hazards models, may deviate from normality or chi-square assumptions, leading to difficulties in controlling type I errors. With the increasing availability and exploration of resources from large-scale population cohorts in whole-genome association studies, there is a growing demand for efficient and accurate statistical approaches to handle extremely unbalanced data in independent and non-independent samples. To address this need, this paper provides a systematic methodological overview. Firstly, it derives test statistics from classical statistical methods. Secondly, it elucidates the impact of extremely unbalanced data on the distribution of test statistics. Thirdly, it introduces two widely used methods for correcting statistics in genome-wide association studies: Firth correction and saddlepoint approximation methods. Finally, it briefly introduces commonly used software for extremely unbalanced genomic data. This paper provides theoretical references and application recommendations for the statistical analysis of extremely unbalanced data.
Peer Review Status:
Awaiting Review
Subjects: Medicine, Pharmacy >> Clinical Medicine Subjects: Statistics >> Biomedical Statistics submitted time 2022-11-21
Abstract： Sample size determination for clinical trials is one of the key components of study design. Based on medical device registration review recently published by National Medical Products Administration, Center for Medical Device Evaluation, and other public information, we conducted an analysis of the sample size for medical device registration clinical trials, including study design, part of which being compared with that in the US. Our results showed that the median sample size for Class III medical device registration trials is 120 (IQR 90~167.5). Sample size was influenced significantly by regulation policies, and some differed significantly from that in the US. Disclose of registration review is a giant leap for medical device regulation in China; however, the disclosed information needs to be further improved.
Peer Review Status:Awaiting Review
Subjects: Statistics >> Biomedical Statistics submitted time 2022-05-02
Abstract：
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Peer Review Status:Awaiting Review
Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics Subjects: Statistics >> Biomedical Statistics submitted time 2020-02-28
Abstract：目的 建立一种数据驱动的实用方法预测突发全新传染性疾病的疫情趋势，通过动态预判疫情风险与分级为防控策略提供量化依据。方法 在移动平均法的基础上予以改进，提出一种移动平均预测限（Moving Average Prediction Limits, MAPL）方法，采用既往重症急性呼吸综合征（Severe Acute Respiratory Syndrome，SARS）疫情数据验证MAPL方法对疫情趋势和风险预判的实用性。追踪本次新型冠状病毒（COVID-19）感染疫情从2020年1月16日起的官方公布数据，采用MAPL方法预判疫情变动趋势与疫区适时风险分级。 结果 基于MAPL方法分析显示，2020年2月初全国COVID-19感染疫情达到峰值。经过前期积极防控，2月中旬起全国疫情整体呈下降趋势。到2月下旬各地疫情有明显的区域性差异。与湖北地区相比，非湖北地区新增病例数下降速度快且未来疫情加重的风险相对较小。在几个重要的疫情输入省份，新增确诊病例数和可疑病例数的发展趋势一致，但消减速度在各省份间存在差异。 结论 MAPL方法可以辅助判断疫情趋势并适时预判风险分级，各疫情输入区可结合当地实际与疫情风险分级规划落实差异化精准防控策略。
Peer Review Status:Awaiting Review
Subjects: Survey & Drawing Science and Technology >> Photogrammetry and Remote Sensing Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics Subjects: Statistics >> Biomedical Statistics submitted time 2020-02-19
Abstract： " "
Peer Review Status:Awaiting Review
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