生物测定
平行性(语法)
等价(形式语言)
计算机科学
背景(考古学)
考试(生物学)
统计
样品(材料)
数学
化学
并行计算
色谱法
生物
离散数学
古生物学
遗传学
作者
Kelly Fleetwood,Francis Bursa,Ann Yellowlees
出处
期刊:Pda Journal of Pharmaceutical Science and Technology
[Parenteral Drug Association, Inc.]
日期:2015-03-01
卷期号:69 (2): 248-263
被引量:6
标识
DOI:10.5731/pdajpst.2015.01016
摘要
Relative potency bioassays are used to estimate the potency of a test biological product relative to a standard or reference product. It is established practice to assess the parallelism of the dose–response curves of the products prior to calculating relative potency. This paper provides a review of parallelism testing for bioassays. In particular three common methods for parallelism testing are reviewed: two significance tests (the F-test, the χ2-test) and an equivalence test. Simulation is used to compare these methods. We compare the sensitivity and specificity and receiver operating characteristic curves, and find that both the χ2-test and the equivalence test outperform the F-test on average, unless the assay-to-assay variation is considerable. No single method is optimal in all situations. We describe how bioassay scientists and statisticians can work together to determine the best approach for each bioassay, taking into account its properties and the context in which it is applied. LAY ABSTRACT: Bioassays are experiments that use living organisms, tissues, or cells to measure the concentration of a pharmaceutical. Typically the response of the living matter to a test sample with an unknown concentration of a pharmaceutical is compared to the response to a standard reference sample with a known concentration. An important step in the analysis of bioassays is checking that the test sample is responding like a diluted copy of the reference sample; this is known as testing for parallelism. There are three statistical methods commonly used to test for parallelism: the F-test, the χ2-test, and the equivalence test. This paper compares the three methods using computer simulations. We conclude that different methods are best in different situations, and we provide guidelines to help bioassay scientists and statisticians decide which method to use.
科研通智能强力驱动
Strongly Powered by AbleSci AI