Reducing bias in parameter estimates from stepwise regression in proportional hazards regression with right-censored data.
Reducing bias in parameter estimates from stepwise regression in proportional hazards regression with right-censored data.
复制标题
使用右删失数据减少比例风险回归中逐步回归的参数估计偏差。
DOI：
10.1007/s10985-007-9078-5
复制
发表时间：
2008
影响因子：
1.3
通讯作者：
Zaslavsky,AlanM
中科院分区：
文献类型：
--
作者：
Soh,Chang-Heok;Harrington,DavidP;Zaslavsky,AlanM
当在同一数据集上进行逐步回归和模型拟合的变量选择时，纳入模型的竞争会导致系数估计值远离零的选择偏倚。在右删失数据下的比例风险回归中，选择偏差会使所选参数的参数估计的绝对值膨胀，而忽略其他变量则会使系数向零收缩。本文探讨了逐步比例风险回归中参数估计值的偏差程度，并提出了一种自举方法，类似于米勒提出的方法（《回归中的子集选择》，第二版。Chapman & Hall/CRC，2002）进行线性回归，以校正选择偏倚。我们还使用自助方法来估计调整估计量的标准误。模拟结果表明，大量的偏差可能存在于未校正的逐步估计，二元协变量，可能超过250%的真实参数值。模拟结果还表明，所提出的自助偏差校正参数估计的条件平均值，给定一个变量被选择，被移动到更接近所选择的模型中的标准偏似然估计的无条件平均值，和人口的参数值。我们还探讨了调整对对数相对风险估计的影响，给定选定模型中协变量的值。所提出的方法说明了原发性胆汁性肝硬化和多发性骨髓瘤从东部肿瘤协作组的数据集。
When variable selection with stepwise regression and model fitting are conducted on the same data set, competition for inclusion in the model induces a selection bias in coefficient estimators away from zero. In proportional hazards regression with right-censored data, selection bias inflates the absolute value of parameter estimate of selected parameters, while the omission of other variables may shrink coefficients toward zero. This paper explores the extent of the bias in parameter estimates from stepwise proportional hazards regression and proposes a bootstrap method, similar to those proposed by Miller (Subset Selection in Regression, 2nd edn. Chapman & Hall/CRC, 2002) for linear regression, to correct for selection bias. We also use bootstrap methods to estimate the standard error of the adjusted estimators. Simulation results show that substantial biases could be present in uncorrected stepwise estimators and, for binary covariates, could exceed 250% of the true parameter value. The simulations also show that the conditional mean of the proposed bootstrap bias-corrected parameter estimator, given that a variable is selected, is moved closer to the unconditional mean of the standard partial likelihood estimator in the chosen model, and to the population value of the parameter. We also explore the effect of the adjustment on estimates of log relative risk, given the values of the covariates in a selected model. The proposed method is illustrated with data sets in primary biliary cirrhosis and in multiple myeloma from the Eastern Cooperative Oncology Group.