Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
J Clin Epidemiol. 2012 Apr;65(4):404-12. doi: 10.1016/j.jclinepi.2011.08.011. Epub 2012 Jan 2.
Many prediction models are developed by multivariable logistic regression. However, there are several alternative methods to develop prediction models. We compared the accuracy of a model that predicts the presence of deep venous thrombosis (DVT) when developed by four different methods.
We used the data of 2,086 primary care patients suspected of DVT, which included 21 candidate predictors. The cohort was split into a derivation set (1,668 patients, 329 with DVT) and a validation set (418 patients, 86 with DVT). Also, 100 cross-validations were conducted in the full cohort. The models were developed by logistic regression, logistic regression with shrinkage by bootstrapping techniques, logistic regression with shrinkage by penalized maximum likelihood estimation, and genetic programming. The accuracy of the models was tested by assessing discrimination and calibration.
There were only marginal differences in the discrimination and calibration of the models in the validation set and cross-validations.
The accuracy measures of the models developed by the four different methods were only slightly different, and the 95% confidence intervals were mostly overlapped. We have shown that models with good predictive accuracy are most likely developed by sensible modeling strategies rather than by complex development methods.
许多预测模型是通过多变量逻辑回归开发的。然而，还有几种替代方法可以开发预测模型。我们比较了通过四种不同方法开发的预测深静脉血栓（DVT）模型的准确性。
我们使用了 2086 名疑似患有深静脉血栓（DVT）的初级保健患者的数据，其中包括 21 个候选预测因子。该队列分为推导集（1668 名患者，329 名患有 DVT）和验证集（418 名患者，86 名患有 DVT）。此外，在整个队列中进行了 100 次交叉验证。模型是通过逻辑回归、通过引导技术进行收缩的逻辑回归、通过惩罚最大似然估计进行收缩的逻辑回归和遗传编程开发的。通过评估区分度和校准度来测试模型的准确性。
在验证集和交叉验证中，模型的区分度和校准度只有微小差异。
通过四种不同方法开发的模型的准确性度量值仅略有不同，95%置信区间大部分重叠。我们已经表明，具有良好预测准确性的模型最有可能通过合理的建模策略而不是复杂的开发方法来开发。
