Gorfine Malka, Hsu Li, Prentice Ross L
Department of Mathematics and Statistics, Bar-Ilan University, Ramat-Gan 52900, Israel.
Biostatistics. 2004 Jan;5(1):75-87. doi: 10.1093/biostatistics/5.1.75.
Stratified Cox regression models with large number of strata and small stratum size are useful in many settings, including matched case-control family studies. In the presence of measurement error in covariates and a large number of strata, we show that extensions of existing methods fail either to reduce the bias or to correct the bias under nonsymmetric distributions of the true covariate or the error term. We propose a nonparametric correction method for the estimation of regression coefficients, and show that the estimators are asymptotically consistent for the true parameters. Small sample properties are evaluated in a simulation study. The method is illustrated with an analysis of Framingham data.
具有大量分层和小分层规模的分层Cox回归模型在许多情况下都很有用，包括匹配病例对照家系研究。在协变量存在测量误差且分层数量众多的情况下，我们表明，现有方法的扩展在真实协变量或误差项的非对称分布下，要么无法减少偏差，要么无法校正偏差。我们提出了一种用于估计回归系数的非参数校正方法，并表明估计量对于真实参数是渐近一致的。在模拟研究中评估了小样本性质。通过对弗雷明汉数据的分析说明了该方法。
