Prediction models for clustered data: comparison of a random intercept and standard regression model.
Prediction models for clustered data: comparison of a random intercept and standard regression model.
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DOI：
10.1186/1471-2288-13-19
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发表时间：
2013-02-15
影响因子：
4
通讯作者：
Vergouwe Y
中科院分区：
文献类型：
--
作者：
Bouwmeester W;Twisk JW;Kappen TH;van Klei WA;Moons KG;Vergouwe Y
当研究数据被聚集时，标准回归分析被认为是不合适的，需要使用聚集数据的分析技术。对于预测者效应在患者水平的预测研究，随机效应回归模型可能比标准回归分析更受欢迎。众所周知，随机效应参数估计与标准Logistic回归参数估计是不同的。在这里，我们比较了随机效应和标准Logistic回归模型提供准确预测的能力。通过对1642名有术后恶心和呕吐风险的外科患者的实证研究，我们建立了标准截距Logistic回归或随机截距Logistic回归的预后模型。这些模型的外部有效性在来自其他麻醉师的新患者中进行了评估。我们通过使用5%、15%或30%的类内相关系数(ICC)的模拟研究来支持我们的结果。估计了标准性能度量和适用于集群数据结构的度量。如果将集群效应用于风险预测，用随机效应分析建立的模型显示出比标准方法更好的区分性(标准c指数为0.69比0.66)。在外部验证组中，两个模型显示出相似的辨别能力(标准c指数为0.68比0.67)。仿真研究证实了这些结果。对于ICC值较高(≥为15%)的数据集，如果所使用的性能测量假设与模型开发方法相同的数据结构，则模型校正仅适用于外部对象：标准校正测量对标准开发的模型显示良好的校正，适应聚类式数据结构的校正测量对具有随机截距的预测模型显示良好的校正。只有在使用集群效应进行预测时，具有随机截距的模型才能比标准模型区分得更好。具有随机截距的预测模型具有较好的簇内校正效果。
When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.
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影响因子：
5.7
作者：
Van den Bosch, JE;Moons, KG;Kalkman, CJ
通讯作者：
Kalkman, CJ
影响因子：
2
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VANHOUWELINGEN, HC;THOROGOOD, J
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THOROGOOD, J
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Moerbeek, M;van Breukelen, GJP;Berger, MP
通讯作者：
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2
作者：
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