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相关概念视频

Odds Ratio01:09

Odds Ratio

1.7K
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
6.4K
Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.0K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Hazard Ratio01:12

Hazard Ratio

559
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
559
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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相关实验视频

Updated: Jan 14, 2026

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
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对避免事件比率的样本大小估计.

David T Dunn1, Oliver T Stirrup2, David V Glidden3

  • 1MRC Clinical Trials Unit, University College London, London, UK.

Clinical trials (London, England)
|October 23, 2025
PubMed
概括

避免事件比率 (AER) 提供了一种更易于解释和更有效的方式来设计非劣势试验. 使用AER可以显著减少样本大小要求,使研究更具成本效益.

关键词:
95-95方法的使用.积极控制试验的试验.避免了事件的发生.估计和估计和估计.没有劣等的非劣等性.效果的维护 - 效果的维护样本的大小 样本大小

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科学领域:

  • 生物统计学 生物统计学
  • 临床试验设计 临床试验设计
  • 流行病学 流行病学

背景情况:

  • 避免事件比率 (AER) 是一种新的估计,用于具有时间到事件结果的非劣势性试验.
  • 与传统措施不同,AER侧重于避免事件而不是观察到的事件,需要对背景事件率或对照治疗有效性的假设.
  • 本研究开发了AER的样本大小公式,并将其与传统的速率比率方法进行比较.

研究的目的:

  • 开发和提出非劣势主动对照预防试验的样本大小公式,使用避免事件比率 (AER) 作为主要估计值.
  • 为了比较基于AER的方法的样本大小要求与传统的95-95方法.
  • 评估不同假设 (反事实性安慰剂发病率与对照治疗有效性) 对样本大小的影响.

主要方法:

  • 样本大小是使用预期事件和人年随访来表达的.
  • 公式是通过在对数尺度上使用沃尔德置信区间来导出的.
  • 样本大小计算考虑背景事件率,控制有效性,非劣势差额,信心限值和统计能力.

主要成果:

  • 基于反事实性安慰剂发病率的AER产生了最小的样本大小,与95-95方法相比减少了2.6至11.9倍,控制效率为50-80%.
  • 基于对照治疗有效性的AER也减少了样本大小 (减少1.5至6.4倍).
  • 样本大小对非劣势差额高度敏感,当差额从50%增加到80%时,其增加1.8到2.5倍.

结论:

  • AER在解释性方面提供了优势,并允许进行更小,更具成本效益的非劣势试验.
  • 在可行的情况下,通过反事实的安慰剂发病率推导AER是首选的.
  • 这些发现支持在积极对照的非劣势性试验设计中采用AER.