Abstract

Predicting risks of chronic diseases has become increasingly important in clinical practice. When a prediction model is developed in a cohort, there is a great interest to apply the model to other cohorts. Due to potential discrepancy in baseline disease incidences between different cohorts and shifts in patient composition, the risk predicted by the model built in the source cohort often under- or over-estimates the risk in a new cohort. In this article, we assume the relative risks of predictors are the same between the two cohorts, and propose a novel weighted estimating equation approach to recalibrating the projected risk for the targeted population through updating the baseline risk. The recalibration leverages the knowledge about survival probabilities for the disease of interest and competing events, and summary information of risk factors from the target population. We establish the consistency and asymptotic normality of the proposed estimators. Extensive simulation demonstrate that the proposed estimators are robust, even if the risk factor distributions differ between the source and target populations, and gain efficiency if they are the same, as long as the information from the target is precise. The method is illustrated with a recalibration of colorectal cancer prediction model.

摘要

预测慢性病的风险在临床实践中变得越来越重要。当在队列中开发预测模型时，将模型应用于其他队列会产生很大的兴趣。由于不同队列之间基线疾病发病率的潜在差异以及患者构成的变化，源队列中建立的模型预测的风险通常低估或高估了新队列中的风险。在这篇文章中，我们假设预测变量的相对风险在两个队列之间是相同的，并提出了一种新的加权估计方程方法，通过更新基线风险来重新校准目标人群的预计风险。重新校准利用了有关感兴趣疾病和竞争事件的生存概率的知识，目标人群的风险因素汇总信息。我们建立了所提出的估计量的一致性和渐近正态性。广泛的模拟表明，所提出的估计量是稳健的，即使源和目标人群之间的风险因素分布不同，并且如果它们相同则可以获得效率，只要来自目标的信息是准确的。该方法通过结直肠癌预测模型的重新校准进行说明。只要来自目标的信息是准确的。该方法通过结直肠癌预测模型的重新校准进行说明。只要来自目标的信息是准确的。该方法通过结直肠癌预测模型的重新校准进行说明。