Impact of imputation of missing data on estimation of survival rates: an example in breast cancer.
Impact of imputation of missing data on estimation of survival rates: an example in breast cancer.
复制标题
缺失数据估算对生存率估计的影响：以乳腺癌为例。
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
A. Talei
中科院分区：
文献类型：
--
作者：
M. Baneshi;A. Talei
多因素回归模型在医学中经常用于估计风险组患者的生存率。然而，如果在模型的发展中所需的假设没有得到满足，他们的结果是不可推广的。缺失数据是病理学中常见的问题。本文的目的是解决排除缺失数据病例的危险，并强调在开发多因子模型之前对缺失数据进行插补的重要性。方法：本研究对设拉子（伊朗南部）诊断的310例乳腺癌患者进行了研究。采用完全病例考克斯回归模型，计算预后指数，将患者分为3个风险组。然后，应用通过链式方程的多变量插补（MICE）方法，对缺失数据进行10次插补。使用插补数据集进行建模，将患者分配到风险组。比较了与完整病例分析和插补数据集相对应的估计精算总体生存（OS）率。结果：至少有一个数据缺失的病例的生存曲线明显更好。相对于插补数据集，分析完整病例数据得出的估计值低估了所有风险组的OS率。此外，置信区间更宽，表明由于样本量和把握度的损耗而导致精密度损失。
Multifactorial regression models are frequently used in medicine to estimate survival rate of patients across risk groups. However, their results are not generalisable, if in the development of models assumptions required are not satisfied. Missing data is a common problem in pathology. The aim of this paper is to address the danger of exclusion of cases with missing data, and to highlight the importance of imputation of missing data before development of multifactorial models. Methods: This study was performed on 310 breast cancer patients diagnosed in Shiraz (Southern Iran). Performing a complete-case Cox regression model, a prognostic index was calculated so as to categorise the patients into 3 risk groups. Then, applying the Multivariate Imputation via Chained Equations (MICE) method, missing data were imputed 10 times. Using imputed data sets, modelling was performed to assign patients into risk groups. Estimated actuarial Overal Survival (OS) rates corresponding to analysis of complete-case and imputed data sets were compared. Results: Cases with at least one missing datum experienced a significantly better survival curve. Estimates derived analysing complete-case data, relative to imputed data sets, underestimated the OS rate in all risk groups. In addition confidence intervals were wider indicating loss in precision due to attrition in sample size and power.