Gaddis G M, Gaddis M L
Department of Emergency Health Services, University of Missouri, Kansas City School of Medicine, Truman Medical Center 64108.
Ann Emerg Med. 1990 May;19(5):591-7. doi: 10.1016/s0196-0644(05)82198-5.
Diagnostic tests guide physicians in assessment of clinical disease states, just as statistical tests guide scientists in the testing of scientific hypotheses. Sensitivity and specificity are properties of diagnostic tests and are not predictive of disease in individual patients. Positive and negative predictive values are predictive of disease in patients and are dependent on both the diagnostic test used and the prevalence of disease in the population studied. These concepts are best illustrated by study of a two by two table of possible outcomes of testing, which shows that diagnostic tests may lead to correct or erroneous clinical conclusions. In a similar manner, hypothesis testing may or may not yield correct conclusions. A two by two table of possible outcomes shows that two types of errors in hypothesis testing are possible. One can falsely conclude that a significant difference exists between groups (type I error). The probability of a type I error is alpha. One can falsely conclude that no difference exists between groups (type II error). The probability of a type II error is beta. The consequence and probability of these errors depend on the nature of the research study. Statistical power indicates the ability of a research study to detect a significant difference between populations, when a significant difference truly exists. Power equals 1-beta. Because hypothesis testing yields "yes" or "no" answers, confidence intervals can be calculated to complement the results of hypothesis testing. Finally, just as some abnormal laboratory values can be ignored clinically, some statistical differences may not be relevant clinically.
诊断测试指导医生评估临床疾病状态，就如同统计测试指导科学家检验科学假设一样。敏感性和特异性是诊断测试的特性，而非个体患者疾病的预测指标。阳性预测值和阴性预测值是患者疾病的预测指标，并且取决于所使用的诊断测试以及所研究人群中疾病的患病率。通过研究测试可能结果的二乘二表，这些概念能得到最好的阐释，该表表明诊断测试可能会导致正确或错误的临床结论。以类似的方式，假设检验可能得出也可能得不出正确结论。一个可能结果的二乘二表表明假设检验中可能存在两种错误类型。一种可能错误地得出组间存在显著差异的结论（I型错误）。I型错误的概率是α。另一种可能错误地得出组间不存在差异的结论（II型错误）。II型错误的概率是β。这些错误的后果和概率取决于研究的性质。统计效能表明当真正存在显著差异时，一项研究检测人群间显著差异的能力。效能等于1-β。由于假设检验得出“是”或“否”的答案，因此可以计算置信区间以补充假设检验的结果。最后，正如某些异常实验室值在临床上可被忽略一样，一些统计差异在临床上可能也不相关。
