{"title":"利用目标人群的汇总统计数据,对基于人群的病例对照研究的时间到事件结果进行风险预测。","authors":"Jiayin Zheng, Li Hsu","doi":"10.1007/s10985-024-09626-x","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283322/pdf/","citationCount":"0","resultStr":"{\"title\":\"Risk projection for time-to-event outcome from population-based case-control studies leveraging summary statistics from the target population.\",\"authors\":\"Jiayin Zheng, Li Hsu\",\"doi\":\"10.1007/s10985-024-09626-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Risk stratification based on prediction models has become increasingly important in preventing and managing chronic diseases. 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引用次数: 0
摘要
在预防和管理慢性疾病方面,基于预测模型的风险分层变得越来越重要。然而,由于成本和时间的限制,并非每个人群都有资源收集足够详细的大量个体信息来开发风险预测模型。更实用的方法是利用现有研究开发的预测模型,并用目标人群的相关汇总信息对其进行校准。现有的许多研究都是在基于人群的病例对照设计下进行的。Gail 等人(J Natl Cancer Inst 81:1879-1886,1989 年)建议把从病例对照数据中得到的几率估计值与目标人群的疾病发病率结合起来,以得到基线危险函数,从而得到纯粹的患病风险。然而,该方法要求病例对照研究中病例的危险因素分布与目标人群相同,如果违反了这一要求,可能会导致风险估计出现偏差。在本文中,我们提出了两种新的加权估计方程方法,除了利用目标人群的无病概率外,还利用(部分)风险因素的汇总信息来校准基线风险。我们确定了所提估计方程的一致性和渐近正态性。广泛的模拟研究和对结直肠癌研究的应用表明,所提出的估计器在有限样本中减少偏差方面表现良好。
Risk projection for time-to-event outcome from population-based case-control studies leveraging summary statistics from the target population.
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.
期刊介绍:
The objective of Lifetime Data Analysis is to advance and promote statistical science in the various applied fields that deal with lifetime data, including: Actuarial Science – Economics – Engineering Sciences – Environmental Sciences – Management Science – Medicine – Operations Research – Public Health – Social and Behavioral Sciences.