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相关概念视频

Probability Laws01:49

Probability Laws

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Overview
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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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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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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基于条件功率的β支出函数在组序列设计中的组序列设计中.

Senmiao Ni1, Zihang Zhong1, Zhiwei Jiang2

  • 1Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.

Biometrical journal. Biometrische Zeitschrift
|April 6, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的CP-β支出函数,用于组序列设计,以更好地控制徒劳性监测期间的II型错误. 该方法确保了精确的错误率控制,并保持了整体I型错误率,提高了试验设计的灵活性.

关键词:
贝塔支出函数是贝塔支出函数.有条件功率的条件功率.毫无意义的停止停止了.组序列设计组的设计.第二种类型的错误控制.

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科学领域:

  • 统计 统计 统计 统计
  • 临床试验设计 临床试验设计

背景情况:

  • 条件功率 (CP) 对于在组序列试验中徒劳性监测至关重要.
  • 现有的CP方法可能会损害II型错误率控制.
  • 需要灵活的方法来管理无用性监测中的II型错误率.

研究的目的:

  • 引入一个灵活的CP-beta支出函数用于徒劳性监测.
  • 规范整个试验的II型错误率支出.
  • 在CP框架内集成beta支出概念,以精确控制错误.

主要方法:

  • 开发了一种新的CP-β支出函数.
  • 整合了一个预先确定的标准化效果大小,用于徒劳性监控.
  • 使用集成计算停止边界,类似于传统的β支出函数.

主要成果:

  • CP-beta支出函数精确控制无用性监控期间的II型错误率.
  • 在各种信息时间场景和CP门中表现出适应性.
  • 模拟研究和现实世界的试验实例证实了精确的功率捕获和I型错误率控制.

结论:

  • 拟议的CP-beta支出函数为组序列设计提供了竞争性和灵活的替代方案.
  • 便于在各种临床试验环境中直接实施.
  • 有效地管理II型错误率,并在徒劳监控期间保持I型错误控制.