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相关概念视频

Statistical Significance01:50

Statistical Significance

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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McNemar's Test01:23

McNemar's Test

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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
247
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
131
Social Proof00:52

Social Proof

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Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.
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Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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相关实验视频

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A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
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使用自发报告系统进行简单的两组比较的检测算法.

Yoshihiro Noguchi1, Tomoaki Yoshimura2

  • 1Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigakunishi, Gifu, 501-1196, Japan. noguchiy@gifu-pu.ac.jp.

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PubMed
概括
此摘要是机器生成的。

医学科学历史上以成年男性为标准,忽视了药物安全性的性别和年龄差异. 本综述探讨了用于检测特定于儿童,老年人和性别特定人群的不良事件 (AE) 信号的数据挖掘.

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科学领域:

  • 药物监督 药物监督 药物监督
  • 药品安全 药品安全
  • 数据挖掘 数据挖掘

背景情况:

  • 医学研究历史上默认使用成年男性作为疾病病理学,诊断和治疗的标准.
  • 新出现的证据强调了疾病风险因素和药物疗效的性别差异.
  • 儿科和老年人群中代谢功能的变化限制了成年男性临床试验数据的直接适用性.

研究的目的:

  • 审查目前用于检测药物相关不良事件 (AE) 信号的数据挖掘方法.
  • 为了解决缺乏关于识别特定于儿科,老年和性别特定人群的AE信号的系统文献的问题.
  • 评估AE信号检测的传统和新型数据挖掘方法.

主要方法:

  • 审查现有的关于药物监督数据挖掘技术的文献.
  • 对药物安全性评估自发报告系统的分析.
  • 探索AE信号检测的不成比例算法.

主要成果:

  • 自发报告系统对于反映现实世界药物使用至关重要,但在捕获总患者数量方面存在局限性.
  • 现有的AE信号检测算法往往无法识别特定于儿科,老年或性别特定群体的信号.
  • 目前还没有系统的方法来检测这些特定的AE信号.

结论:

  • 需要改进数据挖掘策略,以检测特定于性别和年龄的不良药物事件.
  • 目前用于AE信号检测的方法需要改进,以考虑到特定人群的差异.
  • 对专门的数据挖掘技术的进一步研究对于全面的药物安全评估至关重要.