Brunelli Alessandro, Rocco Gaetano
Unit of Thoracic Surgery, Umberto I Regional Hospital, Ancona, Italy.
J Thorac Cardiovasc Surg. 2006 Jun;131(6):1243-7. doi: 10.1016/j.jtcvs.2006.02.002.
The objective of the present analysis was to compare the performance of a lung resection mortality model developed by means of logistic regression and bootstrap analysis with that of multiple mortality models developed by using the traditional training-and-test method from the same dataset.
Eleven mortality models (1 developed by means of logistic regression and bootstrap validation and the other 10 developed by means of the traditional training-and-test random splitting of the dataset) were generated by the data of unit A (571 patients submitted to major lung resection). The performances of each of the 11 mortality models were then evaluated by assessing the distribution of the respective c-statistics in 1000 bootstrap samples derived from unit B (224 patients).
The first model (logistic regression and bootstrap analysis) had good discrimination among the 1000 bootstrap external samples (c-statistics >0.7 in 80% of samples and >0.8 in 38% of samples). Among the 10 training-and-test models, only one model had a similar performance, whereas the others had a poorer discrimination.
The traditional training-and-test method for risk model building proved to be unreliable across multiple external populations and was generally inferior to bootstrap analysis for variable selection in regression analysis. Therefore the use of bootstrap analysis must be recommended for every future model-building process.
本分析的目的是比较通过逻辑回归和自助法分析开发的肺切除死亡率模型与使用传统训练-测试方法从同一数据集开发的多个死亡率模型的性能。
利用单位A（571例行大肺切除术患者）的数据生成了11个死亡率模型（1个通过逻辑回归和自助法验证开发，另外10个通过数据集的传统训练-测试随机分割开发）。然后，通过评估从单位B（224例患者）获得的1000个自助样本中各c统计量的分布，对这11个死亡率模型的性能进行评估。
第一个模型（逻辑回归和自助法分析）在1000个自助外部样本中具有良好的区分度（80%的样本c统计量>0.7，38%的样本c统计量>0.8）。在10个训练-测试模型中，只有一个模型具有类似的性能，而其他模型的区分度较差。
事实证明，传统的风险模型构建训练-测试方法在多个外部人群中不可靠，并且在回归分析中的变量选择方面通常不如自助法分析。因此，对于未来的每个模型构建过程，必须推荐使用自助法分析。
