贝叶斯对质量容忍限值 (QTL) 监测的方法
J C Poythress1, Jin Hyung Lee2, Kentaro Takeda1
1Data Science, Astellas Pharma Global Development, Inc, Northbrook, Illinois, USA.
Pharmaceutical statistics
|August 9, 2024
概括
在临床试验中监测质量容忍限度 (QTL) 的统计学上合理的方法比固定的值更早,更准确地检测出违规行为. 贝叶斯式方法为确保参与者安全和数据完整性提供了进一步的改进.
科学领域:
- 临床试验方法论 临床试验方法论
- 监管科学是一种监管科学.
- 统计过程控制统计过程控制
背景情况:
- 质量耐受限 (QTL) 监测对于临床试验中的基于风险的监测至关重要,符合ICH E6 (R2) 良好的临床实践指南.
- 监测关键参数可确保参与者的安全和试验结果的可靠性.
- 违反QTL可能标志着影响试验完整性的系统问题.
研究的目的:
- 在临床试验中评估和比较有效的QTL监测的统计方法.
- 开发能够早期检测潜在的QTL漏洞的方法,同时尽量减少虚假警报.
- 引入贝叶斯的方法来加强QTL监控.
主要方法:
- 基于统计的方法的比较,考虑到数据的变化,与简单的固定门方法相比.
- 开发和应用贝叶斯方法用于QTL监控.
- 贝叶斯方法的扩展,包括部分信息.
主要成果:
- 与固定门方法相比,基于统计的方法显示出优越的操作特性.
- 拟议的贝叶斯方法显示出其超越传统频率主义方法的潜力.
- 将部分信息纳入贝叶斯方法进一步提高了它的有效性.
结论:
- 先进的统计方法,特别是贝叶斯方法,为临床试验中的QTL监测提供了显著的优势.
- 早期检测QTL漏洞是可以实现的,可以及时干预,以保持试验质量和参与者安全.
- 这些方法提高了临床试验数据的可靠性和完整性.
相关概念视频
Detection of Gross Error: The Q Test
5.8K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.8K
Estimating Population Mean with Known Standard Deviation
8.3K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.3K
Quality Assurance
117
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
117
Quantifying and Rejecting Outliers: The Grubbs Test
1.5K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Quality Control
155
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
155
Testing a Claim about Standard Deviation
2.4K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.4K


