Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study.
Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study.
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DOI：
10.1186/1471-2288-10-112
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发表时间：
2010-12-31
影响因子：
4
通讯作者：
Holder RL
中科院分区：
文献类型：
--
作者：
Marshall A;Altman DG;Holder RL
在预后建模研究中缺失协变量数据的适当处理尚未最终确定。进行了一项恢复性研究，以研究不同缺失数据方法对预后模型性能的影响。从7507例患者的大型完整数据集中对1000例病例的观察数据进行了抽样替换，以获得500个重复。使用随机缺失（MAR）机制对三个协变量施加五个水平的缺失（范围从5%到75%）。应用了5种缺失数据方法：a）完整病例分析（CC）B）使用回归转换和预测均值匹配（SI）进行单次插补，c）使用回归转换插补进行多重插补，d）使用回归转换和预测均值匹配（MICE-PMM）进行多重插补，e）使用灵活的加性插补模型进行多重插补。将考克斯比例风险模型拟合至每个数据集，并获得回归系数和模型性能指标的估计值。CC产生了有偏的回归系数估计和膨胀的标准误差（SE）与25%或更多的缺失。SI后低估的SE导致覆盖率差，缺失率为25%或更多。MI的方法进行了调查，MI使用MICE-PMM产生最小的偏差估计和更好的模型性能指标。然而，这种MI方法仍然产生有偏回归系数估计值，缺失率为75%。所有缺失数据方法的结果之间几乎没有差异，缺失率为5%。然而，使用MICE-PMM进行MI可能是处理10%至50% MAR缺失的首选缺失数据方法。
The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model. Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained. CC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness. Very few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness.
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影响因子：
5.7
作者：
MENG, XL
通讯作者：
MENG, XL
DOI：
10.1016/s0928-4869(01)00048-9
发表时间：
2002-05-15
期刊：
SIMULATION PRACTICE AND THEORY
影响因子：
--
作者：
Murphy, SP;Perera, T
通讯作者：
Perera, T
影响因子：
168.9
作者：
Gray, Richard;Barnwell, Jennifer;Kerr, David J.
通讯作者：
Kerr, David J.
影响因子：
8.8
作者：
通讯作者：
--
影响因子：
2
作者：
Demissie, S;LaValley, MP;Cupples, LA
通讯作者：
Cupples, LA