Trending and Out-of-Trend results in pharmaceutical industry

Trending and Out-of-Trend results in pharmaceutical industry
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制药行业的趋势和非趋势结果

DOI:
10.33320/maced.pharm.bull.2019.65.01.005
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发表时间:
2019
期刊:
Macedonian Pharmaceutical Bulletin
影响因子:
--
通讯作者:
M. G. Dodov
M. G. Dodov
中科院分区:
--
文献类型:
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作者:
Maja Velinovska Cadinoska;Nada Popstefanova;Miroslava Ilievska;Elizabeta Karadžinska;Marija Jovanoska;M. G. Dodov

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监管机构和行业本身对改进质量监测程序的要求越来越高。这种对质量和风险管理的重视促使重新评价系统和程序,以确保遵守拟议的准则。质量监测的基本工具之一是统计工具,我们可以用它来支持关于特定过程的可变性和能力的任何结论,从而我们可以确保控制状态。可以使用的技术有很多,其中之一是统计过程控制(SPC)。SPC是一种高度结构化的方法,用于定义什么是重要的,通过过程筛选;数据收集和测量数据;数据分析和发现差异;需要做什么以及如何使过程处于控制和保持控制状态。趋势分析是统计过程控制的一部分,能够看到过程的大致方向。趋势分析用于防止可能出现的不符合规格的结果和可能对患者健康产生影响的不合格产品。不再接受只在规格范围内和工艺控制之外的情况。 关键词:趋势、趋势、控制图、统计过程控制、可变数据、离散(属性)数据、标准差、控制上限、控制下限
There are increasing demands from both, regulatory agencies and industry itself, for improving the quality monitoring procedures. This focus on quality and risk management has prompted for re-evaluation of the systems and procedures to ensure compliance with the proposed guidelines. One of the essential tools for quality monitoring is statistical tools with which we can support any conclusion with regard to the variability and capability of a given process and thus we can ensure a state of control. There are numerous techniques which can be used and one of them is Statistical Process Control (SPC). SPC is a highly structured approach for defining what is important, by process screening; data collection and measuring the data; data analyzing and finding the variations; what needs to be done and how to bring the process in control and maintain the state of control. Trending is part of SPC and enables to see the general direction towards which the process is moving. Trending is used as a prevention of possible out-of-specification results and nonconforming products that could have impact on the patient’s health. It is no longer acceptable to be only within specification limits and out of process control. Keywords: trend, trending, control charts, SPC, variable data, discrete (attribute) data, standard deviation, upper control limit, lower control limit