Individual participant data (IPD) from multiple sources allows external validation of a prognostic model across multiple populations. Often this reveals poor calibration, potentially causing poor predictive performance in some populations. However, rather than discarding the model outright, it may be possible to modify the model to improve performance using recalibration techniques. We use IPD meta‐analysis to identify the simplest method to achieve good model performance. We examine four options for recalibrating an existing time‐to‐event model across multiple populations: (i) shifting the baseline hazard by a constant, (ii) re‐estimating the shape of the baseline hazard, (iii) adjusting the prognostic index as a whole, and (iv) adjusting individual predictor effects. For each strategy, IPD meta‐analysis examines (heterogeneity in) model performance across populations. Additionally, the probability of achieving good performance in a new population can be calculated allowing ranking of recalibration methods. In an applied example, IPD meta‐analysis reveals that the existing model had poor calibration in some populations, and large heterogeneity across populations. However, re‐estimation of the intercept substantially improved the expected calibration in new populations, and reduced between‐population heterogeneity. Comparing recalibration strategies showed that re‐estimating both the magnitude and shape of the baseline hazard gave the highest predicted probability of good performance in a new population. In conclusion, IPD meta‐analysis allows a prognostic model to be externally validated in multiple settings, and enables recalibration strategies to be compared and ranked to decide on the least aggressive recalibration strategy to achieve acceptable external model performance without discarding existing model information.

中文翻译：

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用于外部验证、重新校准和更新灵活参数预测模型的个体参与者数据元分析

来自多个来源的个体参与者数据 (IPD) 允许跨多个人群对预后模型进行外部验证。这通常表明校准不佳，可能导致某些人群的预测性能不佳。然而，与其直接丢弃模型，不如使用重新校准技术修改模型以提高性能。我们使用 IPD 荟萃分析来确定实现良好模型性能的最简单方法。我们检查了四个选项，用于重新校准跨多个人群的现有事件发生时间模型：（i）将基线风险移动一个常数，（ii）重新估计基线风险的形状，（iii）将预后指数调整为一个整体，以及 (iv) 调整个体预测效应。对于每个策略，IPD 荟萃分析检查（异质性）模型在人群中的性能。此外，可以计算在新群体中实现良好性能的概率，从而对重新校准方法进行排名。在一个应用示例中，IPD 荟萃分析表明，现有模型在某些人群中的校准较差，并且在人群之间存在较大的异质性。然而，对截距的重新估计大大改善了新人群的预期校准，并降低了人群间的异质性。比较重新校准策略表明，重新估计基线危害的大小和形状可以得到在新人群中表现良好的最高预测概率。总之，IPD 荟萃分析允许在多种环境中对预后模型进行外部验证，