测量不确定性< 905 > 用蒙特卡洛和引导方法测试剂量单位统一性的测试 - 采样和分析步骤产生的不确定性
Maisa Torres Martins1, Felipe Rebello Lourenço1
1Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, Av. Prof. Lineu Prestes, 580 - Bloco 15, 05508-000 São Paulo, SP, Brazil.
Journal of pharmaceutical and biomedical analysis
|November 23, 2023
概括
在剂量单位统一性测试中评估测量不确定性对于药物质量至关重要. 这项研究使用了蒙特卡洛和引导方法来评估Haloperidol和Ofloxacin片的错误合格决定的风险.
科学领域:
- 制药科学 制药科学
- 分析化学 分析化学
- 质量控制 质量控制 质量控制
背景情况:
- 剂量单位统一性 (UDU) 测试可以确保药物的质量,安全性和有效性.
- 活性药物成分 (API) 含量的变化可能会影响药品质量.
- 准确评估UDU对于防止劣质药物至关重要.
研究的目的:
- 为了评估UDU测试的验收值 (AV) 中的测量不确定性.
- 为了估计由于不确定性而导致不正确的合规/不合规决策的风险.
- 为了比较不确定性定量化的蒙特卡洛和引导方法.
主要方法:
- 内容一致性 (CU) 和重量变化 (WV) 测试对Haloperidol和Ofloxacin片进行了测试.
- 使用蒙特卡洛和引导方法来量化测量不确定性.
- 考虑了采样和分析步骤的不确定性贡献.
主要成果:
- 用两种方法对两种药物确定了AV的置信区间 (CI95%).
- 哈洛佩里多尔片显示出假合规的风险增加 (6.5%12.1%).
- 采样和分析步骤的不确定性显著影响了AV值.
结论:
- 测量不确定性评估支持可靠的合规性评估.
- 量化不确定性减少了在UDU测试中错误决策的风险.
- 蒙特卡洛和引导都是评估UDU测试不确定性的有价值的.
相关概念视频
Uncertainty in Measurement: Accuracy and Precision
73.8K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
73.8K
Propagation of Uncertainty from Systematic Error
529
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
529
Uncertainty: Overview
564
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
564
Propagation of Uncertainty from Random Error
699
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
699
Uncertainty in Measurement: Reading Instruments
38.3K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
38.3K
Bootstrapping
610
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
610


