Variable selection under multiple imputation using the bootstrap in a prognostic study
Variable selection under multiple imputation using the bootstrap in a prognostic study
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DOI：
10.1186/1471-2288-7-33
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发表时间：
2007-07-13
影响因子：
4
通讯作者：
de Vet, Henrica C. W.
中科院分区：
文献类型：
--
作者：
Heymans, Martijn W.;van Buuren, Stef;de Vet, Henrica C. W.
背景：在许多预后研究中，缺失数据是一个具有挑战性的问题。多重插补（MI）考虑了插补不确定性，允许进行充分的统计检验。我们开发并测试了一种方法，结合MI与bootstrapping技术研究预后variable selection.Method：在我们的前瞻性队列研究中，我们合并了三个不同的随机对照试验（RCT）的数据，以评估慢性腰痛的预后变量。在结果和预后变量中，数据缺失的范围为0 - 48.1%。我们使用了四种方法来分别研究抽样和插补变异的影响：仅MI，仅Bootstrap，以及两种结合联合收割机MI和Bootstrap的方法。根据每个预后变量的纳入频率（即变量在模型中出现的次数比例）选择变量。在不同的包含水平下，对这四种方法建立的预测模型的判别和校正能力进行了评估。结果：我们发现，插补变异对包含频率的影响大于抽样变异的影响。当MI和bootstrapping相结合的范围为0%（全模型）到90%的变量选择，bootstrapping校正的c-指数值为0.70至0.71和斜率值为0.64至0.86被发现。结论：我们建议考虑插补和抽样变异的缺失数据集。新的程序相结合的MI与自举变量选择，结果在多变量预后模型具有良好的性能，因此是有吸引力的应用数据集缺失值。
Background: Missing data is a challenging problem in many prognostic studies. Multiple imputation (MI) accounts for imputation uncertainty that allows for adequate statistical testing. We developed and tested a methodology combining MI with bootstrapping techniques for studying prognostic variable selection.Method: In our prospective cohort study we merged data from three different randomized controlled trials (RCTs) to assess prognostic variables for chronicity of low back pain. Among the outcome and prognostic variables data were missing in the range of 0 and 48.1%. We used four methods to investigate the influence of respectively sampling and imputation variation: MI only, bootstrap only, and two methods that combine MI and bootstrapping. Variables were selected based on the inclusion frequency of each prognostic variable, i.e. the proportion of times that the variable appeared in the model. The discriminative and calibrative abilities of prognostic models developed by the four methods were assessed at different inclusion levels.Results: We found that the effect of imputation variation on the inclusion frequency was larger than the effect of sampling variation. When MI and bootstrapping were combined at the range of 0% (full model) to 90% of variable selection, bootstrap corrected c-index values of 0.70 to 0.71 and slope values of 0.64 to 0.86 were found.Conclusion: We recommend to account for both imputation and sampling variation in sets of missing data. The new procedure of combining MI with bootstrapping for variable selection, results in multivariable prognostic models with good performance and is therefore attractive to apply on data sets with missing values.