Lui K J
Department of Mathematical Sciences, College of Sciences, San Diego State University, California 92182-7720, USA.
Biometrics. 1998 Jun;54(2):706-11.
This paper discusses interval estimation of the risk ratio (RR) between a secondary infection, given a primary infection, and the primary infection. Three asymptotic closed-form interval estimators are developed using Wald's test statistic, the logarithmic transformation, and Fieller's theorem. The performance of these interval estimators is compared with respect to the coverage probability and the expected length of the resulting confidence intervals. When the underlying probability of a primary infection is high (say, 0.80), all three estimators perform reasonably well. In fact, in this case, they are all essentially equivalent when the number of subjects n > or = 100. When the probability of a primary infection is small (say, 0.20) or moderate (say, 0.30 to 0.50), the interval estimator using the logarithmic transformation outperforms the other two estimators when n < or = 100. In fact, the coverage probability of the former estimator is consistently greater than or equal to the desired confidence level in all the situations considered in this paper and hence is recommended for general use.
本文讨论了在有原发性感染的情况下，继发性感染与原发性感染之间风险比（RR）的区间估计。利用Wald检验统计量、对数变换和Fieller定理，开发了三种渐近闭式区间估计量。比较了这些区间估计量在覆盖概率和所得置信区间预期长度方面的性能。当原发性感染的潜在概率较高（例如0.80）时，这三种估计量的表现都相当不错。事实上，在这种情况下，当受试者数量n≥100时，它们基本上都是等效的。当原发性感染的概率较小（例如0.20）或适中（例如0.30至0.50）时，当n≤100时，使用对数变换的区间估计量优于其他两种估计量。事实上，在本文考虑的所有情况下，前一种估计量的覆盖概率始终大于或等于所需的置信水平，因此建议普遍使用。
