Statistical Aspects of Radioimmunoassay
Statistical Aspects of Radioimmunoassay
复制标题
放射免疫分析的统计方面
DOI：
10.1007/978-3-642-71809-0_8
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发表时间：
1987
期刊：
影响因子：
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通讯作者：
P. Munson
中科院分区：
文献类型：
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作者：
D. Rodbard;V. Guardabasso;P. Munson
logit-log方法（Rodbard和Lewald 1970）仍然是当今使用的RIA数据简化的最流行方法（图1）。它具有简单的优点（Rodbard 1979）。该方法可以图形化地实现，使用特殊的logit-log图纸。它可以使用小型可编程手持计算器或计算机来实现（Davis et al. 1980）。即使是最小的微型计算机也可以通过适当的加权和详细的统计分析轻松执行此方法。加权线性回归甚至可以由流行的电子表格程序的特殊“宏”程序来执行。logit-log方法已被纳入许多商业系统（β和γ计数器）。当logit-log方法“工作”时（可能是90%-95%的时间），一切都很好。不幸的是，在约5%-10%的测定中，logit-log方法不能提供RIA剂量-反应曲线的充分描述。当logit-log方法失败时，分析师应该怎么做？由于有许多替代品，每个都有自己的优点和局限性，分析师经常面临令人困惑的情况（Rodbard 1979）。我们是否要尝试所有可能的方法，采用试错法？我们什么时候才能说我们有一个“好”的方法，一个“足够”或“最佳”的方法？是否有系统的方法？我们是否将被限制在商业化的、交钥匙的、“黑盒”的数据分析方法中实现的曲线拟合方法？本章将试图为“当logit-log方法失败时我该怎么办？”这个问题提供一个简单、合理的方法。
The logit-log method (Rodbard and Lewald 1970) remains the most popular method for RIA data reduction in use today (Fig. 1). It has the virtue of simplicity (Rodbard 1979). The method can be implemented graphically, using special logit-log graph paper. It can be implemented using a small programmable handheld calculator or computer (Davis et al. 1980). Even the smallest microcomputers can easily perform this method with proper weighting and detailed statistical analysis. A weighted linear regression can even be performed by special “macro” programs for popular spreadsheet programs. The logit-log method has been incorporated into numerous commercial systems (β- and γ-counters). When the logit-log method “works” (which is probably about 90%–95% of the time) everything is fine. Unfortunately, in about 5%–10% of assays, the logit-log method fails to provide an adequate description of the RIA dose-response curve. What should the assayist do when the logit-log method fails? Since numerous alternatives are available, each with its own advantages and limitations, the assayist is often faced with a bewildering situation (Rodbard 1979). Are we to try all possible methods, using a trial-and-error approach? When will we be able to say that we have a “good” method, an “adequate” or “optimal” method? Is there a systematic approach? Are we to be restricted to the curve-fitting methods implemented in commercial, turnkey, “black box” approaches to data analysis? This chapter will attempt to provide a simple, rational approach to the question, “what should I do when the logit-log method fails?”