<1010> ANALYTICAL DATA—INTERPRETATION AND TREATMENT

分析数据的解释和处理 INTRODUCTION

前言

This chapter provides information regarding acceptable practices for the analysis and consistent interpretation of data obtained from chemical and other analyses. Basic statistical approaches for evaluating data are described, and the treatment of outliers and comparison of analytical procedures are discussed in some detail.

对于分析化学分析和其他分析工作中获得的数据资料并给出相应解释的工作，本章提供了一些可接受的操作信息。针对评价数据资料的一些基础统计学方法、异常值的处理和分析方法的比较，本章都进行了较为详细的讨论。

[NOTE—It should not be inferred that the analysis tools mentioned in this chapter form an exhaustive list. Other, equally valid, statistical methods may be used at the discretion of the manufacturer and other users of this chapter.] [注：本章所列的并非是所有的分析工具。根据生产商和其他使用者的慎重判断，也可使用其他的一些等效统计方法。]

Assurance of the quality of pharmaceuticals is accomplished by combining a number of practices, including robust formulation design, validation, testing of starting materials, in-process testing, and final-product testing. Each of these practices is dependent on reliable test procedures. In the development process, test procedures are developed and validated to ensure that the manufactured products are thoroughly characterized. Final-product testing provides further assurance that the products are consistently safe, efficacious, and in compliance with their specifications.

药品的质量保证是由一系列实践活动联合完成的，这些活动包括了耐用性的处方设计、确认、起始物料的检测、过程监测和终产品检测等。所有这些活动都依赖于可靠的检测方法。在研发过程中，需要建立检测方法并对其进行确认，以便该方法能够确保所生产的产品可以被完全特征化。终产品的检测可以进一步确保产品可以始终安全、有效以及符合其质量标准。

Measurements are inherently variable. The variability of biological tests has long been recognized by the USP. For example, the need to consider this variability when analyzing biological test data is addressed in Analysis of Biological Assays <1034>. The chemical analysis measurements commonly used to analyze pharmaceuticals are also inherently variable, although less so than those of the biological tests. However, in many instances the acceptance criteria are proportionally tighter, and thus, this smaller allowable variability has to be considered when analyzing data generated using analytical procedures. If the variability of a measurement is not characterized and stated along with the result of the measurement, then the data can only be interpreted in the most limited sense. For example, stating that the difference between the averages from two laboratories when testing a common set of samples is 10% has limited interpretation, in terms of how important such a difference is, without knowledge of the intralaboratory variability.

任何测量本质上都是可变的。生物学检测的这种变异性很早就被USP认识到。比如，在其<1034> Analysis of Biological Assays中就规定了对生物检测数据进行分析时需要考虑其变异性。通常用于药品分析中的化学分析测量方法也同样本质上是可变的，尽管其变异性比生物检测实验小。但在很多情况下，其接受标准也相应地更严格；因此，当使用这些方法分析所获得的数据时，对这种可接受的较小变异也必须予以考虑。如果没有描述出一种测量的变异特性，仅以该测量的结果进行表述，那么就只能在非常有限的层面上对该资料的进行解释。比如，当两个实验室测量一组相同样本时，如果没有实验室间的变异性相关信息，仅通过表达其测量均值的差异为10%想要说明这种差异有多么显著，其解释意义是非常有限的。

This chapter provides direction for scientifically acceptable treatment and interpretation of data. Statistical tools that may be helpful in the interpretation of analytical data are described. Many descriptive statistics, such as the mean and standard deviation, are in common use. Other statistical tools, such as outlier tests, can be performed using several different, scientifically valid approaches, and examples of these tools and their applications are also included. The framework within which the results from a compendial test are interpreted is clearly outlined in General Notices and Requirements 7. Test Results. Selected references that might be helpful in obtaining additional information on the statistical tools discussed in this chapter are listed in Appendix G at the end of the chapter. USP does not endorse these citations, and they do not represent an exhaustive list. Further information about many of the methods cited in this chapter may also be found in most statistical textbooks.

本章提供了一个对实验数据进行科学适当处理及解释的指导。在这里，对一些有益于进行数据解释的统计工具进行了描述。其中许多描述性统计方法是通常使用的，如均值和标准差。可以通过几种不同的，经科学确认的方式使用其他一些统计工具，如异常值检验，本章还给出了这些方法的相关实例及其应用。有关对法定方法检测数据结果进行解释的框架都明确概述在General Notices and Requirements 7. Test Results一节当中。附录G罗列了一些有益于获取关于本章中讨论过的统计工具的更多信息的相关文献。USP并未核准这些引用文献，且这些文献也并非代表所讨论的统计方法全部内容。本章所引用统计方法的更多信息也可以在大部分统计教材中找到。

PREREQUISITE LABORATORY PRACTICES AND PRINCIPLES

实验室活动规范的先决条件和原则

The sound application of statistical principles to laboratory data requires the assumption that such data have been collected in a traceable (i.e., documented) and unbiased manner. To ensure this, the following practices are beneficial. 完全正确地应用统计原理于分析实验室的数据，需要具备下列，即这些数据以一种可以溯源（如记录并存档）并无偏倚的方式收集。遵守下列规范是非常有益于确保基本假定要求的。

Sound Record Keeping 保存完好无误的记录

Laboratory records are maintained with sufficient detail, so that other equally qualified analysts can reconstruct the experimental conditions and review the results obtained. When collecting data, the data should generally be obtained with more decimal places than the specification requires and rounded only after final calculations are completed as per the General Notices and Requirements.

实验室记录应包含有充分的细节，以便其他有同等能力的人员可以重建实验条件并评估所得实验结果。当采集数据资料时，应遵守“General Notices and Requirements”中的要求，通常以比质量标准要求保留的小数位数多几位的格式进行采集，并且只在所有计算都完成时才进行修约。

Sampling Considerations

抽样考虑

Effective sampling is an important step in the assessment of a quality attribute of a population. The purpose of sampling is to provide representative data (the sample) for estimating the properties of the population. How to attain such a sample depends entirely on the question that is to be answered by the sample data. In general, use of a random process is considered the most appropriate way of selecting a sample. Indeed, a random and independent sample is necessary to ensure that the resulting data produce valid estimates of the properties of the population. Generating a nonrandom or ―convenience‖ sample risks the possibility that the estimates will be biased. The most straightforward type of random sampling is called simple random sampling, a process in which every unit of the population has an equal chance of appearing in the sample. However, sometimes this method of selecting a random sample is not optimal because it cannot guarantee equal representation among factors (i.e., time, location, machine) that may influence the critical properties of the population. For example, if it requires 12 hours to manufacture all of the units in a lot and it is vital that the sample be representative of the entire production process, then taking a simple random sample after the production has been completed may not be appropriate because there can be no guarantee that such a sample will contain a similar number of units made from every time period within the 12-hour process. Instead, it is better to take a systematic random sample whereby a unit is randomly selected from the production process at systematically selected times or locations (e.g., sampling every 30 minutes from the units produced at that time) to ensure that units taken throughout the entire manufacturing process are included in the sample. Another type of random sampling procedure is needed if, for example, a product is filled into vials using four different filling machines. In this case it would be impor-tant to capture a random sample of vials from each of the filling machines. A stratified random sample, which randomly samples an equal number of vials from each of the four filling machines, would satisfy this requirement. Regardless of the reason for taking a sample (e.g., batch-release testing), a sampling plan should be established to provide details on how the sample is to be obtained to ensure that the sample is representative of the entirety of the population and that the resulting data have the required sensitivity. The optimal sampling strategy will depend on knowledge of the manufacturing and analytical measurement processes. Once the sampling scheme has been defined, it is likely that the sampling will include some element of random selection. Finally, there must be sufficient sample collected for the original analysis, subsequent verification analyses, and other analyses. Consulting a statistician to

identify the optimal sampling strategy is recommended.

要对一个总体的质量属性进行评估，有效的抽样方式是重要的一步。抽样的目的就是提供能正确描述总体特性的代表性样本资料。如何获得这样一个样本完全取决于样本所要解答的问题。一般而言，使用随机抽样是最合适的取样方式。实际上，为了确保所得样本数据能有效地评估总体的属性，一个随机、独立的样本是必须的。采用非随机或“便利”的样本会出现偏倚评估的风险。最直接的随机抽样的方式是“简单随机样本法（simple random sampling）”，在该过程中，总体中每一个单体都有相同的机会出现在样本中。但有时这种简单抽样方法也不是最优的，因为它不能保证平等地体现某些因素（如时间、地点和机器），而这些因素会对总体的一些重要属性产生影响。例如，如果要求12小时生产出一个批次的所有单元，那么所取样本能够代表整个生产过程是极为重要的，这时，如果在生产完成后，采用简单随机抽样的方法进行抽样将是不合适的，因为无法保证这样抽取的样本会均等或类似均等地包含12小时内每个时间段生产的单元。这时，最好采用系统随机样本法（systematic random sample）的方式，以便使每一个所抽单元来自于整个生产过程中的不同时间段和地点段（如在生产过程中，每隔30分钟抽取一个单元），从而确保了所抽样本均衡地来自于整个生产过程。当假设一个产品用4个不同的分装机器分装到药瓶时，若进行抽样，则需要另一种随机抽样方法，以确保所抽的随机样本中包含了来自于每一台机器的药瓶。“分层随机样本法（stratified random sample）”，即将4台分装机中均等数目的药瓶随机进行取样的方式，可以满足这样的要求。如果不考虑取样原因时（如批放行检测），则在抽样时应建立一个抽样方案来提供一些细节，这些细节是有关如何取样才能确保样本可以代表总体的所有属性，并且确保取得的样本有必需的灵敏性。最佳抽样（的选用）策略取决于对生产和分析测量过程的了解。一旦确定了抽样方案，取样很可能包含一些随机选择的基本要素。最后，必须采集足够量的样本以便进行初步分析、后续验证分析和其他分析等。建议咨询统计人员以便确定最优抽样策略。

Tests discussed in the remainder of this chapter assume that simple random sampling has been performed. 本章下面所讨论的检测实验都是针对假定采用了简单随机抽样得到的样本。

Use of Reference Standards

对照品的使用

Where USP or NF tests or assays call for the use of a USP Reference Standard, only those results obtained using the specified USP Reference Standard are conclusive for purposes of demonstrating conformance to such USP or NF standards. While USP standards apply at all times in the life of an article from production to expiration, USP does not specify when testing must be done, or any frequency of testing. Accordingly, users of USP and NF apply a range of strategies and practices to assure articles achieve and maintain conformance with compendial requirements, including when and if tested. Such strategies and practices can include the use of secondary standards traceable to the USP Reference Standard, to supplement or support any testing undertaken for the purpose of conclusively demonstrating conformance to applicable compendial standards. Because the assignment of a value to a standard is one of the most important factors that influences the accuracy of an analysis, it is critical that this be done correctly.