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Abstract：
Risk stratification based on prediction models has become increasingly important in preventing and managing chronic diseases. However, due to cost- and time-limitations, not every population can have resources for collecting enough detailed individual-level information on a large number of people to develop risk prediction models. A more practical approach is to use prediction models developed from existing studies and calibrate them with relevant summary-level information of the target population. Many existing studies were conducted under the population-based case-control design. Gail et al. (J Natl Cancer Inst 81:1879-1886, 1989) proposed to combine the odds ratio estimates obtained from case-control data and the disease incidence rates from the target population to obtain the baseline hazard function, and thereby the pure risk for developing diseases. However, the approach requires the risk factor distribution of cases from the case-control studies be same as the target population, which, if violated, may yield biased risk estimation. In this article, we propose two novel weighted estimating equation approaches to calibrate the baseline risk by leveraging the summary information of (some) risk factors in addition to disease-free probabilities from the targeted population. We establish the consistency and asymptotic normality of the proposed estimators. Extensive simulation studies and an application to colorectal cancer studies demonstrate the proposed estimators perform well for bias reduction in finite samples.
摘要：
基于预测模型的风险分层在预防和管理慢性病中变得越来越重要。然而,由于成本和时间的限制，并非每个人都有资源来收集大量人群的足够详细的个人层面信息，以开发风险预测模型。一种更实用的方法是使用从现有研究中开发的预测模型，并用目标人群的相关摘要级信息对其进行校准。许多现有的研究是在基于人群的病例对照设计下进行的。Gail等人。（JNatlCancerInst81：1879-1886，1989）提出将从病例对照数据中获得的比值比估计和目标人群的疾病发病率相结合，以获得基线风险函数，从而导致疾病的纯粹风险。然而,该方法要求来自病例对照研究的病例的风险因素分布与目标人群相同，which,如果违反,可能会产生有偏差的风险估计。在这篇文章中,我们提出了两种新颖的加权估计方程方法,通过利用(一些)危险因素的汇总信息以及目标人群的无病概率来校准基线风险.我们建立了所提出的估计量的一致性和渐近正态。广泛的模拟研究和在结直肠癌研究中的应用表明，所提出的估计器在有限样本中的偏倚减少方面表现良好。
