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相关概念视频

Randomized Experiments01:13

Randomized Experiments

7.0K
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
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Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Blinding01:11

Blinding

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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
104
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

133
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Hazard Ratio01:12

Hazard Ratio

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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...
136

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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在部分集群随机对照试验中处理缺失的数据.

Manshu Yang1, Darrell J Gaskin2

  • 1Department of Psychology, University of Rhode Island.

Psychological methods
|November 6, 2023
PubMed
概括
此摘要是机器生成的。

在部分集群试验中处理缺失的数据对于准确的心理学研究至关重要. 使用联合建模 (MI-JM-AS) 的特定臂式多重归算对于固定效应是最好的,而 MI-SMC-AS 则是随机效应的首选.

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科学领域:

  • 心理学研究 心理学研究
  • 生物统计学 生物统计学
  • 临床试验 临床试验

背景情况:

  • 部分集群设计在心理随机对照试验中很常见.
  • 缺少的数据在这些复杂的试验设计中带来了重大挑战.
  • 处理缺失的数据对于有效的研究结论至关重要.

研究的目的:

  • 在部分集群研究中比较五种处理辅助变量依赖失踪随机数据的方法.
  • 为不同的数据结构确定最有效的归算或估计策略.
  • 为处理复杂试验设计中缺少数据的研究人员提供指导.

主要方法:

  • 进行了一项模拟研究,以比较五种统计方法.
  • 方法包括各种多重归算 (MI) 和顺序完全贝叶斯式 (SFB) 方法.
  • 具体的方法进行了比较:MI-JM-SIM,MI-JM-AS,MI-SMC-AS,SFB-NON,SFB-WEAK. 这两种方法是不同的.

主要成果:

  • 使用联合建模 (MI-JM-AS) 方法的特定臂的多重归算在缺失变量只涉及固定效应时显示出优异的性能.
  • 当不完整的变量包括随机效应时,使用实质模型兼容序列建模 (MI-SMC-AS) 方法进行特定臂的多重归算是首选的.
  • 性能根据不完整的变量中存在固定的和随机效应的存在而有所不同.

结论:

  • 在部分集群试验中处理缺失数据的方法的选择取决于不完整变量的性质 (固定的与随机效应).
  • 在特定条件下建议使用MI-JM-AS和MI-SMC-AS,以提高准确性.
  • 实证数据示例说明了这些方法的实际应用.