Accurate risk assessment is critical in clinical decision-making. It entails the projected risk based on a risk prediction model agreeing with the observed risk in the target cohort. However, the model often over- or under-estimates the risk. Building a new model for the target cohort would be ideal but costly. It is therefore of great interest to recalibrate an existing model for the target cohort. Existing methods have been proposed to recalibrate the model by leveraging the disease incidence rates from the target cohort. However, they assume the same covariate distribution across cohorts and when the assumption is violated, the recalibrated model can be substantially biased. Further, recalibration is also complicated by the two-phase sampling design that is commonly used for developing risk prediction models. In this paper, we develop a weighted estimating-equation approach accounting for the two-phase design and combine it with a weighted empirical likelihood that leverages the summary information on both disease incidence rates and covariates from the target cohort. We provide a resampling-based inference procedure. Our extensive simulation results show that using the summary information from the target population, the proposed recalibration method yields nearly unbiased risk estimates under a wide range of scenarios. An application to a colorectal cancer study also illustrates that the proposed method yields a well-calibrated model in the target cohort.

中文翻译：

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将两阶段研究的个体数据与外部汇总统计数据相结合，重新校准纯风险


准确的风险评估对于临床决策至关重要。它需要基于与目标群体中观察到的风险一致的风险预测模型来预测风险。然而，该模型经常高估或低估风险。为目标群体建立一个新模型是理想的选择，但成本高昂。因此，针对目标群体重新校准现有模型非常有意义。已提出现有方法通过利用目标人群的疾病发病率来重新校准模型。然而，他们假设不同群组之间的协变量分布相同，当违反该假设时，重新校准的模型可能会存在很大偏差。此外，通常用于开发风险预测模型的两阶段抽样设计也使重新校准变得复杂。在本文中，我们开发了一种考虑两阶段设计的加权估计方程方法，并将其与加权经验可能性相结合，该方法利用了目标队列中疾病发病率和协变量的摘要信息。我们提供基于重采样的推理过程。我们广泛的模拟结果表明，使用目标人群的汇总信息，所提出的重新校准方法在各种情况下都能产生几乎无偏的风险估计。结直肠癌研究的应用也表明，所提出的方法在目标队列中产生了一个经过良好校准的模型。