Lynn H S, McCulloch C E
Biometrics Unit, Cornell University, Ithaca, New York 14853.
Biometrics. 1992 Jun;48(2):397-409.
Two methods of analysis are compared to estimate the treatment effect of a comparative study where each treated individual is matched with a single control at the design stage. The usual matched-pairs analysis accounts for the pairing directly in its model, whereas regression adjustment ignores the matching but instead models the pairing using a set of covariates. For a normal linear model, the estimated treatment effect from the matched-pairs analysis (paired t-test) is more efficient. For a Bernoulli logistic model, matched-pairs analysis performs better when the sample size is small, but is inferior to logistic regression for large sample sizes.
在一项比较研究中，若每个接受治疗的个体在设计阶段都与一个单独的对照个体进行匹配，现比较两种分析方法以估计治疗效果。通常的配对分析在其模型中直接考虑配对因素，而回归调整则忽略匹配，而是使用一组协变量对配对进行建模。对于正态线性模型，配对分析（配对t检验）估计的治疗效果更有效。对于伯努利逻辑模型，当样本量较小时，配对分析表现更好，但对于大样本量，它不如逻辑回归。
