Egger M J, Coleman M L, Ward J R, Reading J C, Williams H J
Control Clin Trials. 1985 Mar;6(1):12-24. doi: 10.1016/0197-2456(85)90093-5.
Measurement of improvement in clinical trials in chronic diseases commonly compares baseline data to endpoint values by performing t-tests or analysis of variance (ANOVA) on raw gains or percentage changes. This procedure can be misleading and the use of an analysis of covariance (ANCOVA) should be considered. Properly used, ANCOVA increases statistical power in a clinical trial. However, its advantage over t-tests can be nullified by small numbers of patients, violations of assumptions, and incorrect application of the techniques. An evaluation of ANCOVA in chronic disease studies is given, with examples of its strengths and weaknesses as seen in several drug trials in the rheumatic diseases. Recommendations on its use and a decision tree for the nonstatistician are provided.
在慢性病临床试验中，衡量改善情况通常是通过对原始增益或百分比变化进行t检验或方差分析（ANOVA），将基线数据与终点值进行比较。这个过程可能会产生误导，应该考虑使用协方差分析（ANCOVA）。如果使用得当，ANCOVA可以提高临床试验的统计效力。然而，患者数量少、违反假设以及技术应用不正确可能会抵消它相对于t检验的优势。本文对慢性病研究中的ANCOVA进行了评估，并列举了在几项风湿性疾病药物试验中其优缺点的实例。还提供了关于其使用的建议以及供非统计人员使用的决策树。
