Lui Kung-Jong
Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182-7720, USA.
Stat Med. 2005 Oct 15;24(19):2953-62. doi: 10.1002/sim.2159.
Kuritz and Landis considered case-control studies with multiple matching and proposed an asymptotic interval estimator of the attributable risk based on Wald's statistic. Using Monte Carlo simulation, Kuritz and Landis demonstrated that their interval estimator could perform well when the number of matched sets was large (>or=100). However, the number of matched sets may often be moderate or small in practice. In this paper, we evaluate the performance of Kuritz and Landis' interval estimator in small or moderate number of matched sets and compare it with four other interval estimators. We note that the coverage probability of Kuritz and Landis' interval estimator tends to be less than the desired confidence level when the probability of exposure among cases is large. In these cases, the interval estimator using the logarithmic transformation and the two interval estimators derived from the quadratic equations developed here can generally improve the coverage probability of Kuritz and Landis' interval estimator, especially for the case of a small number of matched sets. Furthermore, we find that an interval estimator derived from a quadratic equation is consistently more efficient than Kuritz and Landis' interval estimator. The interval estimator using the logit transformation, although which performs poorly when the underlying odds ratio (OR) is close to 1, can be useful when both the probability of exposure among cases and the underlying OR are moderate or large.
库里茨和兰迪斯考虑了具有多重匹配的病例对照研究，并基于沃尔德统计量提出了归因风险的渐近区间估计量。通过蒙特卡罗模拟，库里茨和兰迪斯证明，当匹配集数量很大（≥100）时，他们的区间估计量表现良好。然而，在实际中匹配集数量往往可能是中等或较小。在本文中，我们评估了库里茨和兰迪斯的区间估计量在少量或中等数量匹配集情况下的性能，并将其与其他四个区间估计量进行比较。我们注意到，当病例中的暴露概率很大时，库里茨和兰迪斯的区间估计量的覆盖概率往往低于期望的置信水平。在这些情况下，使用对数变换的区间估计量以及由本文开发的二次方程推导得出的两个区间估计量通常可以提高库里茨和兰迪斯的区间估计量的覆盖概率，特别是在匹配集数量较少的情况下。此外，我们发现由二次方程推导得出的区间估计量始终比库里茨和兰迪斯的区间估计量更有效。使用对数it变换的区间估计量，虽然在基础比值比（OR）接近1时表现不佳，但当病例中的暴露概率和基础OR都为中等或较大时可能会有用。
