Lansky D
Searle, Skokie, IL 60077, USA.
Dev Biol Stand. 1999;97:157-68.
For a biological assay to be useful for quality control it should fail bad lots, pass good lots, and estimate relative potency with high accuracy and precision. To fail a lot we rely most heavily on the test for parallelism. For the parallelism test and other preliminary tests as well as for inference, appropriate estimates of assay variation are crucial. Location effects on 96 well plates and serial dilution of samples using multichannel pipettes make it difficult to obtain good estimates of assay variation. This paper develops the use of a split-block design and analysis where blocks are reasonably consistent regions of a plate; this approach removes some location effects, allows other location effects to be treated as assay variation and provides appropriate measures of assay variation. Randomization, even within the split-block design, is difficult without robots to reduce the likelihood of procedural errors. There are hardware, software, and validation obstacles to implementation of robots in the bioassay laboratory. More generally, validation of a bioassay should be reported on log relative potency and must address between- and within-assay variation. When between assay variation is not small, the usual weighted approach to combining relative potency estimates (which ignores between-assay variation) is inappropriate; a simple sampling average and standard deviation is a better solution.
对于一种用于质量控制的生物学测定法而言，它应该能够判定不合格批次、通过合格批次，并以高精度和高准确度估计相对效价。要判定一个批次不合格，我们最主要依赖于平行线检验。对于平行线检验和其他初步检验以及推断而言，测定变异的恰当估计至关重要。96孔板上的位置效应以及使用多通道移液器对样品进行系列稀释，使得难以获得测定变异的良好估计。本文开发了一种裂区设计及分析方法的应用，其中区组是板上合理一致的区域；这种方法消除了一些位置效应，允许将其他位置效应视为测定变异，并提供了测定变异的恰当度量。如果没有机器人来降低程序错误的可能性，即使在裂区设计内进行随机化也很困难。在生物测定实验室中实施机器人存在硬件、软件和验证方面的障碍。更一般地说，生物测定的验证应报告对数相对效价，并且必须解决批间和批内变异问题。当批间变异不小的时候，通常用于合并相对效价估计值的加权方法（该方法忽略批间变异）是不合适的；简单的抽样平均值和标准差是更好的解决方案。
