Brown Michael D, Reeves Mathew J
Grand Rapids MERC/Michigan State University Program in Emergency Medicine, Grand Rapids, MI, USA.
Ann Emerg Med. 2003 Aug;42(2):292-7. doi: 10.1067/mem.2003.274.
Emergency physicians are often confronted with making diagnostic decisions on the basis of a test result represented on a continuous scale. When the results of continuous data are expressed as binary outcomes using a single cutoff, loss of information and distortion may occur. In this setting, interval likelihood ratios provide a distinct advantage in interpretation over those based on a dichotomized sensitivity and specificity. Dividing the data into intervals uses more of the information contained in the data and allows the clinician to more appropriately interpret the test results and to make valid clinical decisions. This article illustrates the advantages of interval likelihood ratios with examples and demonstrates how to calculate them on the basis of different data formats. Authors and journals need to be encouraged to report the results of studies of performance of diagnostic tests using interval ranges rather than simple dichotomization when the tests involve continuous variables.
急诊医生常常需要根据连续尺度上呈现的检测结果做出诊断决策。当连续数据的结果使用单一临界值表示为二元结果时,可能会出现信息丢失和扭曲。在这种情况下,区间似然比在解释方面比基于二分灵敏度和特异度的似然比具有明显优势。将数据划分为区间会利用数据中包含的更多信息,并使临床医生能够更恰当地解释检测结果并做出有效的临床决策。本文通过示例说明了区间似然比的优势,并演示了如何根据不同的数据格式计算它们。当检测涉及连续变量时,需要鼓励作者和期刊使用区间范围而非简单二分法来报告诊断检测性能研究的结果。